1
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Stuyver T. TS-tools: Rapid and automated localization of transition states based on a textual reaction SMILES input. J Comput Chem 2024; 45:2308-2317. [PMID: 38850166 DOI: 10.1002/jcc.27374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/08/2024] [Accepted: 03/20/2024] [Indexed: 06/10/2024]
Abstract
Here, TS-tools is presented, a Python package facilitating the automated localization of transition states (TS) based on a textual reaction SMILES input. TS searches can either be performed at xTB or DFT level of theory, with the former yielding guesses at marginal computational cost, and the latter directly yielding accurate structures at greater expense. On a benchmarking dataset of mono- and bimolecular reactions, TS-tools reaches an excellent success rate of 95% already at xTB level of theory. For tri- and multimolecular reaction pathways - which are typically not benchmarked when developing new automated TS search approaches, yet are relevant for various types of reactivity, cf. solvent- and autocatalysis and enzymatic reactivity - TS-tools retains its ability to identify TS geometries, though a DFT treatment becomes essential in many cases. Throughout the presented applications, a particular emphasis is placed on solvation-induced mechanistic changes, another issue that received limited attention in the automated TS search literature so far.
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Affiliation(s)
- Thijs Stuyver
- Ecole Nationale Supérieure de Chimie de Paris, Université PSL, CNRS, Institute of Chemistry for Life and Health Sciences, Paris, France
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2
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Stulajter MM, Rappoport D. Reaction Networks Resemble Low-Dimensional Regular Lattices. J Chem Theory Comput 2024. [PMID: 39236261 DOI: 10.1021/acs.jctc.4c00810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
The computational exploration, manipulation, and design of complex chemical reactions face fundamental challenges related to the high-dimensional nature of potential energy surfaces (PESs) that govern reactivity. Accurately modeling complex reactions is crucial for understanding the chemical processes involved in, for example, organocatalysis, autocatalytic cycles, and one-pot molecular assembly. Our prior research demonstrated that discretizing PESs using heuristics based on bond breaking and bond formation produces a reaction network representation with a low-dimensional structure (metric space). We now find that these stoichiometry-preserving reaction networks possess additional, though approximate, structure and resemble low-dimensional regular lattices with a small amount of random edge rewiring. The heuristics-based discretization thus generates a nonlinear dimensionality reduction by a factor of 10 with an a posteriori error measure (probability of random rewiring). The structure becomes evident through a comparative analysis of CHNO reaction networks of varying stoichiometries against a panel of size-matched generative network models, taking into account their local, metric, and global properties. The generative models include random networks (Erdős-Rényi and bipartite random networks), regular lattices (periodic and nonperiodic), and network models with a tunable level of "randomness" (Watts-Strogatz graphs and regular lattices with random rewiring). The CHNO networks are simultaneously closely matched in all these properties by 3-4-dimensional regular lattices with 10% or less of edges randomly rewired. The effective dimensionality reduction is found to be independent of the system size, stoichiometry, and ruleset, suggesting that search and sampling algorithms for PESs of complex chemical reactions can be effectively leveraged.
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Affiliation(s)
- Miko M Stulajter
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States
- Computational Science Research Center, San Diego State University, San Diego, California 92182, United States
| | - Dmitrij Rappoport
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States
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3
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Csizi KS, Steiner M, Reiher M. Nanoscale chemical reaction exploration with a quantum magnifying glass. Nat Commun 2024; 15:5320. [PMID: 38909029 PMCID: PMC11193806 DOI: 10.1038/s41467-024-49594-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 06/04/2024] [Indexed: 06/24/2024] Open
Abstract
Nanoscopic systems exhibit diverse molecular substructures by which they facilitate specific functions. Theoretical models of them, which aim at describing, understanding, and predicting these capabilities, are difficult to build. Viable quantum-classical hybrid models come with specific challenges regarding atomistic structure construction and quantum region selection. Moreover, if their dynamics are mapped onto a state-to-state mechanism such as a chemical reaction network, its exhaustive exploration will be impossible due to the combinatorial explosion of the reaction space. Here, we introduce a "quantum magnifying glass" that allows one to interactively manipulate nanoscale structures at the quantum level. The quantum magnifying glass seamlessly combines autonomous model parametrization, ultra-fast quantum mechanical calculations, and automated reaction exploration. It represents an approach to investigate complex reaction sequences in a physically consistent manner with unprecedented effortlessness in real time. We demonstrate these features for reactions in bio-macromolecules and metal-organic frameworks, diverse systems that highlight general applicability.
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Affiliation(s)
- Katja-Sophia Csizi
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Miguel Steiner
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
- ETH Zurich, NCCR Catalysis, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Markus Reiher
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
- ETH Zurich, NCCR Catalysis, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
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4
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Weymuth T, Unsleber JP, Türtscher PL, Steiner M, Sobez JG, Müller CH, Mörchen M, Klasovita V, Grimmel SA, Eckhoff M, Csizi KS, Bosia F, Bensberg M, Reiher M. SCINE-Software for chemical interaction networks. J Chem Phys 2024; 160:222501. [PMID: 38857173 DOI: 10.1063/5.0206974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/09/2024] [Indexed: 06/12/2024] Open
Abstract
The software for chemical interaction networks (SCINE) project aims at pushing the frontier of quantum chemical calculations on molecular structures to a new level. While calculations on individual structures as well as on simple relations between them have become routine in chemistry, new developments have pushed the frontier in the field to high-throughput calculations. Chemical relations may be created by a search for specific molecular properties in a molecular design attempt, or they can be defined by a set of elementary reaction steps that form a chemical reaction network. The software modules of SCINE have been designed to facilitate such studies. The features of the modules are (i) general applicability of the applied methodologies ranging from electronic structure (no restriction to specific elements of the periodic table) to microkinetic modeling (with little restrictions on molecularity), full modularity so that SCINE modules can also be applied as stand-alone programs or be exchanged for external software packages that fulfill a similar purpose (to increase options for computational campaigns and to provide alternatives in case of tasks that are hard or impossible to accomplish with certain programs), (ii) high stability and autonomous operations so that control and steering by an operator are as easy as possible, and (iii) easy embedding into complex heterogeneous environments for molecular structures taken individually or in the context of a reaction network. A graphical user interface unites all modules and ensures interoperability. All components of the software have been made available as open source and free of charge.
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Affiliation(s)
- Thomas Weymuth
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jan P Unsleber
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Paul L Türtscher
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Miguel Steiner
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jan-Grimo Sobez
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Charlotte H Müller
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Maximilian Mörchen
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Veronika Klasovita
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Stephanie A Grimmel
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Marco Eckhoff
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Katja-Sophia Csizi
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Francesco Bosia
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Moritz Bensberg
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Markus Reiher
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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5
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Srinivasan K, Puliyanda A, Prasad V. Identification of Reaction Network Hypotheses for Complex Feedstocks from Spectroscopic Measurements with Minimal Human Intervention. J Phys Chem A 2024; 128:4714-4729. [PMID: 38836378 DOI: 10.1021/acs.jpca.4c01592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
In this work, we detail an automated reaction network hypothesis generation protocol for processes involving complex feedstocks where information about the species and reactions involved is unknown. Our methodology is process agnostic and can be utilized in any reactive process with spectroscopic measurements that provide information on the evolution of the components in the mixture. We decompose the mixture spectra to obtain spectroscopic signatures of the individual components and use a 1-D convolutional neural network to automatically identify functional groups indicated by them. We employ atom-atom mapping to automatically recover reaction rules that are applied on candidate molecules identified from chemistry databases through fingerprint similarity. The method is tested on synthetic data and on spectroscopic measurements of lab-scale batch hydrothermal liquefaction (HTL) of biomass to determine the accuracy of prediction across datasets of varying complexities. Our methodology is able to identify reaction network hypotheses containing reaction networks close to the ground truth in the case of synthetic data, and we are also able to recover candidate molecules and reaction networks close to the ones reported in the previous literature studies for biomass pyrolysis.
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Affiliation(s)
- Karthik Srinivasan
- Department of Chemical and Materials Engineering, Donadeo Innovation Centre for Engineering, 9211, 116st NW, Edmonton T6G 1H9, AB, Canada
| | - Anjana Puliyanda
- Department of Chemical and Materials Engineering, Donadeo Innovation Centre for Engineering, 9211, 116st NW, Edmonton T6G 1H9, AB, Canada
| | - Vinay Prasad
- Department of Chemical and Materials Engineering, Donadeo Innovation Centre for Engineering, 9211, 116st NW, Edmonton T6G 1H9, AB, Canada
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6
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Bensberg M, Reiher M. Uncertainty-Aware First-Principles Exploration of Chemical Reaction Networks. J Phys Chem A 2024; 128:4532-4547. [PMID: 38787736 PMCID: PMC11163430 DOI: 10.1021/acs.jpca.3c08386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Exploring large chemical reaction networks with automated exploration approaches and accurate quantum chemical methods can require prohibitively large computational resources. Here, we present an automated exploration approach that focuses on the kinetically relevant part of the reaction network by interweaving (i) large-scale exploration of chemical reactions, (ii) identification of kinetically relevant parts of the reaction network through microkinetic modeling, (iii) quantification and propagation of uncertainties, and (iv) reaction network refinement. Such an uncertainty-aware exploration of kinetically relevant parts of a reaction network with automated accuracy improvement has not been demonstrated before in a fully quantum mechanical approach. Uncertainties are identified by local or global sensitivity analysis. The network is refined in a rolling fashion during the exploration. Moreover, the uncertainties are considered during kinetically steering of a rolling reaction network exploration. We demonstrate our approach for Eschenmoser-Claisen rearrangement reactions. The sensitivity analysis identifies that only a small number of reactions and compounds are essential for describing the kinetics reliably, resulting in efficient explorations without sacrificing accuracy and without requiring prior knowledge about the chemistry unfolding.
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Affiliation(s)
- Moritz Bensberg
- Department of Chemistry and Applied
Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Markus Reiher
- Department of Chemistry and Applied
Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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7
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van Gerwen P, Briling KR, Calvino Alonso Y, Franke M, Corminboeuf C. Benchmarking machine-readable vectors of chemical reactions on computed activation barriers. DIGITAL DISCOVERY 2024; 3:932-943. [PMID: 38756222 PMCID: PMC11094696 DOI: 10.1039/d3dd00175j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/28/2024] [Indexed: 05/18/2024]
Abstract
In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well. Here, we bring together all of these efforts and benchmark various methods (Morgan fingerprints, the DRFP, the CGR representation-based Chemprop, SLATMd, B2Rl2, EquiReact and language model BERT + RXNFP) for the prediction of computed activation barriers on three diverse datasets.
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Affiliation(s)
- Puck van Gerwen
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Ksenia R Briling
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Yannick Calvino Alonso
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Malte Franke
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Clemence Corminboeuf
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
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8
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Steiner M, Reiher M. A human-machine interface for automatic exploration of chemical reaction networks. Nat Commun 2024; 15:3680. [PMID: 38693117 PMCID: PMC11063077 DOI: 10.1038/s41467-024-47997-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 04/15/2024] [Indexed: 05/03/2024] Open
Abstract
Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.
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Affiliation(s)
- Miguel Steiner
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
- ETH Zurich, NCCR Catalysis, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Markus Reiher
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
- ETH Zurich, NCCR Catalysis, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
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9
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Focke K, De Santis M, Wolter M, Martinez B JA, Vallet V, Pereira Gomes AS, Olejniczak M, Jacob CR. Interoperable workflows by exchanging grid-based data between quantum-chemical program packages. J Chem Phys 2024; 160:162503. [PMID: 38686818 DOI: 10.1063/5.0201701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 04/02/2024] [Indexed: 05/02/2024] Open
Abstract
Quantum-chemical subsystem and embedding methods require complex workflows that may involve multiple quantum-chemical program packages. Moreover, such workflows require the exchange of voluminous data that go beyond simple quantities, such as molecular structures and energies. Here, we describe our approach for addressing this interoperability challenge by exchanging electron densities and embedding potentials as grid-based data. We describe the approach that we have implemented to this end in a dedicated code, PyEmbed, currently part of a Python scripting framework. We discuss how it has facilitated the development of quantum-chemical subsystem and embedding methods and highlight several applications that have been enabled by PyEmbed, including wave-function theory (WFT) in density-functional theory (DFT) embedding schemes mixing non-relativistic and relativistic electronic structure methods, real-time time-dependent DFT-in-DFT approaches, the density-based many-body expansion, and workflows including real-space data analysis and visualization. Our approach demonstrates, in particular, the merits of exchanging (complex) grid-based data and, in general, the potential of modular software development in quantum chemistry, which hinges upon libraries that facilitate interoperability.
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Affiliation(s)
- Kevin Focke
- Institute of Physical and Theoretical Chemistry, Technische Universität Braunschweig, Gaußstraße 17, 38106 Braunschweig, Germany
| | - Matteo De Santis
- CNRS, UMR 8523-PhLAM-Physique des Lasers Atomes et Molécules, Univ. Lille, F-59000 Lille, France
| | - Mario Wolter
- Institute of Physical and Theoretical Chemistry, Technische Universität Braunschweig, Gaußstraße 17, 38106 Braunschweig, Germany
| | - Jessica A Martinez B
- CNRS, UMR 8523-PhLAM-Physique des Lasers Atomes et Molécules, Univ. Lille, F-59000 Lille, France
- Department of Chemistry, Rutgers University, Newark, New Jersey 07102, USA
| | - Valérie Vallet
- CNRS, UMR 8523-PhLAM-Physique des Lasers Atomes et Molécules, Univ. Lille, F-59000 Lille, France
| | | | - Małgorzata Olejniczak
- Centre of New Technologies, University of Warsaw, S. Banacha 2c, 02-097 Warsaw, Poland
| | - Christoph R Jacob
- Institute of Physical and Theoretical Chemistry, Technische Universität Braunschweig, Gaußstraße 17, 38106 Braunschweig, Germany
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10
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Ribó JM, Hochberg D. Physical Chemistry Models for Chemical Research in the XXth and XXIst Centuries. ACS PHYSICAL CHEMISTRY AU 2024; 4:122-134. [PMID: 38560750 PMCID: PMC10979499 DOI: 10.1021/acsphyschemau.3c00057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 12/22/2023] [Accepted: 12/22/2023] [Indexed: 04/04/2024]
Abstract
Thermodynamic hypotheses and models are the touchstone for chemical results, but the actual models based on time-invariance, which have performed efficiently in the development of chemistry, are nowadays invalid for the interpretation of the behavior of complex systems exhibiting nonlinear kinetics and with matter and energy exchange flows with the surroundings. Such fields of research will necessarily foment and drive the use of thermodynamic models based on the description of irreversibility at the macroscopic level, instead of the current models which are strongly anchored in microreversibility.
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Affiliation(s)
- Josep M. Ribó
- Department
of Inorganic and Organic Chemistry, University
of Barcelona, c. Martí i Franquès 1, 08028 Barcelona, Catalonia, Spain
- Institute
of Cosmos Science (IEEC-UB), c. Martí i Franquès 1, 08028 Barcelona, Catalonia, Spain
| | - David Hochberg
- Department
of Molecular Evolution, Centro de Astrobiología
(CSIC-INTA), E-28850 Torrejón de Ardóz, Madrid, Spain
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11
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Pracht P, Grimme S, Bannwarth C, Bohle F, Ehlert S, Feldmann G, Gorges J, Müller M, Neudecker T, Plett C, Spicher S, Steinbach P, Wesołowski PA, Zeller F. CREST-A program for the exploration of low-energy molecular chemical space. J Chem Phys 2024; 160:114110. [PMID: 38511658 DOI: 10.1063/5.0197592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 02/29/2024] [Indexed: 03/22/2024] Open
Abstract
Conformer-rotamer sampling tool (CREST) is an open-source program for the efficient and automated exploration of molecular chemical space. Originally developed in Pracht et al. [Phys. Chem. Chem. Phys. 22, 7169 (2020)] as an automated driver for calculations at the extended tight-binding level (xTB), it offers a variety of molecular- and metadynamics simulations, geometry optimization, and molecular structure analysis capabilities. Implemented algorithms include automated procedures for conformational sampling, explicit solvation studies, the calculation of absolute molecular entropy, and the identification of molecular protonation and deprotonation sites. Calculations are set up to run concurrently, providing efficient single-node parallelization. CREST is designed to require minimal user input and comes with an implementation of the GFNn-xTB Hamiltonians and the GFN-FF force-field. Furthermore, interfaces to any quantum chemistry and force-field software can easily be created. In this article, we present recent developments in the CREST code and show a selection of applications for the most important features of the program. An important novelty is the refactored calculation backend, which provides significant speed-up for sampling of small or medium-sized drug molecules and allows for more sophisticated setups, for example, quantum mechanics/molecular mechanics and minimum energy crossing point calculations.
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Affiliation(s)
- Philipp Pracht
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Stefan Grimme
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany
| | - Christoph Bannwarth
- Institute for Physical Chemistry, RWTH Aachen University, Melatener Str. 20, 52056 Aachen, Germany
| | - Fabian Bohle
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany
| | - Sebastian Ehlert
- AI4Science, Microsoft Research, Evert van de Beekstraat 354, 1118 CZ Schiphol, The Netherlands
| | - Gereon Feldmann
- Institute for Physical Chemistry, RWTH Aachen University, Melatener Str. 20, 52056 Aachen, Germany
| | - Johannes Gorges
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany
| | - Marcel Müller
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany
| | - Tim Neudecker
- Institute for Physical and Theoretical Chemistry, University of Bremen, 28359 Bremen, Germany
| | - Christoph Plett
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany
| | | | - Pit Steinbach
- Institute for Physical Chemistry, RWTH Aachen University, Melatener Str. 20, 52056 Aachen, Germany
| | - Patryk A Wesołowski
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Felix Zeller
- Institute for Physical and Theoretical Chemistry, University of Bremen, 28359 Bremen, Germany
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12
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Tsekenis G, Cimini G, Kalafatis M, Giacometti A, Gili T, Caldarelli G. Network topology mapping of chemical compounds space. Sci Rep 2024; 14:5266. [PMID: 38438443 PMCID: PMC10912673 DOI: 10.1038/s41598-024-54594-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 02/14/2024] [Indexed: 03/06/2024] Open
Abstract
We define bipartite and monopartite relational networks of chemical elements and compounds using two different datasets of inorganic chemical and material compounds, as well as study their topology. We discover that the connectivity between elements and compounds is distributed exponentially for materials, and with a fat tail for chemicals. Compounds networks show similar distribution of degrees, and feature a highly-connected club due to oxygen . Chemical compounds networks appear more modular than material ones, while the communities detected reveal different dominant elements specific to the topology. We successfully reproduce the connectivity of the empirical chemicals and materials networks by using a family of fitness models, where the fitness values are derived from the abundances of the elements in the aggregate compound data. Our results pave the way towards a relational network-based understanding of the inherent complexity of the vast chemical knowledge atlas, and our methodology can be applied to other systems with the ingredient-composite structure.
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Affiliation(s)
- Georgios Tsekenis
- Institute for Complex Systems, National Research Council, Rome, Italy.
- Department of Molecular Sciences and Nanosystems (DMSN), "Ca' Foscari" University of Venice, Venice, Italy.
| | - Giulio Cimini
- Physics Department and INFN, University of Rome Tor Vergata, Rome, Italy
| | - Marinos Kalafatis
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Achille Giacometti
- Department of Molecular Sciences and Nanosystems (DMSN), "Ca' Foscari" University of Venice, Venice, Italy
- European Centre of Living Technologies (ECLT), "Ca' Foscari" University of Venice, Venice, Italy
| | - Tommaso Gili
- Networks Unit, IMT School for Advanced Studies Lucca, 55100, Lucca, Italy
| | - Guido Caldarelli
- Institute for Complex Systems, National Research Council, Rome, Italy
- Department of Molecular Sciences and Nanosystems (DMSN), "Ca' Foscari" University of Venice, Venice, Italy
- European Centre of Living Technologies (ECLT), "Ca' Foscari" University of Venice, Venice, Italy
- Rara Foundation - Sustainable Materials and Technologies ETS, 30171, Venice, Italy
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13
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Stan-Bernhardt A, Glinkina L, Hulm A, Ochsenfeld C. Exploring Chemical Space Using Ab Initio Hyperreactor Dynamics. ACS CENTRAL SCIENCE 2024; 10:302-314. [PMID: 38435517 PMCID: PMC10906254 DOI: 10.1021/acscentsci.3c01403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 03/05/2024]
Abstract
In recent years, first-principles exploration of chemical reaction space has provided valuable insights into intricate reaction networks. Here, we introduce ab initio hyperreactor dynamics, which enables rapid screening of the accessible chemical space from a given set of initial molecular species, predicting new synthetic routes that can potentially guide subsequent experimental studies. For this purpose, different hyperdynamics derived bias potentials are applied along with pressure-inducing spherical confinement of the molecular system in ab initio molecular dynamics simulations to efficiently enhance reactivity under mild conditions. To showcase the advantages and flexibility of the hyperreactor approach, we present a systematic study of the method's parameters on a HCN toy model and apply it to a recently introduced experimental model for the prebiotic formation of glycinal and acetamide in interstellar ices, which yields results in line with experimental findings. In addition, we show how the developed framework enables the study of complicated transitions like the first step of a nonenzymatic DNA nucleoside synthesis in an aqueous environment, where the molecular fragmentation problem of earlier nanoreactor approaches is avoided.
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Affiliation(s)
- Alexandra Stan-Bernhardt
- Chair
of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstrasse 5, D-81377 München, Germany
| | - Liubov Glinkina
- Chair
of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstrasse 5, D-81377 München, Germany
| | - Andreas Hulm
- Chair
of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstrasse 5, D-81377 München, Germany
| | - Christian Ochsenfeld
- Chair
of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstrasse 5, D-81377 München, Germany
- Max
Planck Institute for Solid State Research, Heisenbergstrasse 1, D-70569 Stuttgart, Germany
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14
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Adebar N, Keupp J, Emenike VN, Kühlborn J, Vom Dahl L, Möckel R, Smiatek J. Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions. J Phys Chem A 2024; 128:929-944. [PMID: 38271617 DOI: 10.1021/acs.jpca.3c06265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Emerging concepts from scientific deep machine learning such as physics-informed neural networks (PINNs) enable a data-driven approach for the study of complex kinetic problems. We present an extended framework that combines the advantages of PINNs with the detailed consideration of experimental parameter variations for the simulation and prediction of chemical reaction kinetics. The approach is based on truncated Taylor series expansions for the underlying fundamental equations, whereby the external variations can be interpreted as perturbations of the kinetic parameters. Accordingly, our method allows for an efficient consideration of experimental parameter settings and their influence on the concentration profiles and reaction kinetics. A particular advantage of our approach, in addition to the consideration of univariate and multivariate parameter variations, is the robust model-based exploration of the parameter space to determine optimal reaction conditions in combination with advanced reaction insights. The benefits of this concept are demonstrated for higher-order chemical reactions including catalytic and oscillatory systems in combination with small amounts of training data. All predicted values show a high level of accuracy, demonstrating the broad applicability and flexibility of our approach.
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Affiliation(s)
- Niklas Adebar
- Development NCE, Chemical Development, Boehringer Ingelheim Pharma GmbH & Co. KG, D-55218 Ingelheim (Rhein), Germany
| | - Julian Keupp
- Development NCE, Chemical Development, Boehringer Ingelheim Pharma GmbH & Co. KG, D-55218 Ingelheim (Rhein), Germany
| | - Victor N Emenike
- HP BioP Launch and Innovation, Boehringer Ingelheim Pharma GmbH & Co. KG, D-55218 Ingelheim (Rhein), Germany
| | - Jonas Kühlborn
- Development NCE, Chemical Development, Boehringer Ingelheim Pharma GmbH & Co. KG, D-55218 Ingelheim (Rhein), Germany
| | - Lisa Vom Dahl
- Development NCE, Analytical Development, Boehringer Ingelheim Pharma GmbH & Co. KG, D-55218 Ingelheim (Rhein), Germany
| | - Robert Möckel
- Development NCE, Chemical Development, Boehringer Ingelheim Pharma GmbH & Co. KG, D-55218 Ingelheim (Rhein), Germany
| | - Jens Smiatek
- Institute for Computational Physics, University of Stuttgart, D-70569 Stuttgart, Germany
- Development NCE, Strategy NCEs, Boehringer Ingelheim Pharma GmbH & Co. KG, D-88397 Biberach (Riss), Germany
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15
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Loskot P. A query-response causal analysis of reaction events in biochemical reaction networks. Comput Biol Chem 2024; 108:107995. [PMID: 38039799 DOI: 10.1016/j.compbiolchem.2023.107995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 11/16/2023] [Accepted: 11/27/2023] [Indexed: 12/03/2023]
Abstract
The stochastic kinetics of biochemical reaction networks is described by a chemical master equation (CME) and the underlying laws of mass action. Assuming network-free simulations of the rule-based models of biochemical reaction networks (BRNs), this paper departs from the usual analysis of network dynamics as the time-dependent distributions of chemical species counts, and instead considers statistically evaluating the sequences of reaction events generated from the stochastic simulations. The reaction event-time series can be used for reaction clustering, identifying rare events, and recognizing the periods of increased or steady-state activity. However, the main aim of this paper is to device an effective method for identifying causally and anti-causally related sub-sequences of reaction events using their empirical probabilities. This allows discovering some of the causal dynamics of BRNs as well as uncovering their short-term deterministic behaviors. In particular, it is proposed that the reaction sub-sequences that are conditionally nearly certain or nearly uncertain can be considered as being causally related. Moreover, since the time-ordering of reaction events is locally irrelevant, the reaction sub-sequences can be transformed into the reaction sets or multi-sets. The distance metrics can be then used to define the equivalences among the reaction events. The proposed method for identifying the causally related reaction sub-sequences has been implemented as a computationally efficient query-response mechanism. The method was evaluated for five models of genetic networks in seven defined numerical experiments. The models were simulated in BioNetGen using the open-source network-free simulator NFsim. This simulator had to be modified first to allow recording the traces of reaction events, and it is available in the Github repository, ploskot/nfsim_1.20. The generated event time-series were analyzed with Python and Matlab scripts. The whole process of data generation, analysis and visualization has been nearly fully automated using shell scripts. This demonstrates the opportunities for substantially increasing the research productivity by creating automated data generation and processing pipelines.
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Affiliation(s)
- Pavel Loskot
- ZJU-UIUC Institute, 314400, Haining, Zhejiang, China.
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16
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Kim S, Woo J, Kim WY. Diffusion-based generative AI for exploring transition states from 2D molecular graphs. Nat Commun 2024; 15:341. [PMID: 38184661 PMCID: PMC10771475 DOI: 10.1038/s41467-023-44629-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 12/21/2023] [Indexed: 01/08/2024] Open
Abstract
The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperforms the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learns the distribution of TS geometries for diverse reactions in training. Thus, TSDiff finds more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration.
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Affiliation(s)
- Seonghwan Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea
| | - Jeheon Woo
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea
| | - Woo Youn Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea.
- AI Institute, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea.
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17
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Duan C, Du Y, Jia H, Kulik HJ. Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. NATURE COMPUTATIONAL SCIENCE 2023; 3:1045-1055. [PMID: 38177724 DOI: 10.1038/s43588-023-00563-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 11/03/2023] [Indexed: 01/06/2024]
Abstract
Transition state search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D transition state structures, however, requires numerous computationally intensive quantum chemistry calculations due to the complexity of potential energy surfaces. Here we developed an object-aware SE(3) equivariant diffusion model that satisfies all physical symmetries and constraints for generating sets of structures-reactant, transition state and product-in an elementary reaction. Provided reactant and product, this model generates a transition state structure in seconds instead of hours, which is typically required when performing quantum-chemistry-based optimizations. The generated transition state structures achieve a median of 0.08 Å root mean square deviation compared to the true transition state. With a confidence scoring model for uncertainty quantification, we approach an accuracy required for reaction barrier estimation (2.6 kcal mol-1) by only performing quantum chemistry-based optimizations on 14% of the most challenging reactions. We envision usefulness for our approach in constructing large reaction networks with unknown mechanisms.
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Affiliation(s)
- Chenru Duan
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, US.
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, US.
| | - Yuanqi Du
- Department of Computer Science, Cornell University, Ithaca, NY, US
| | - Haojun Jia
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, US
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, US
| | - Heather J Kulik
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, US
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, US
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18
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Zhang Y, Xu C, Lan Z. Automated Exploration of Reaction Networks and Mechanisms Based on Metadynamics Nanoreactor Simulations. J Chem Theory Comput 2023. [PMID: 38031422 DOI: 10.1021/acs.jctc.3c00752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
We developed an automated approach to construct a complex reaction network and explore the reaction mechanisms for numerous reactant molecules by integrating several theoretical approaches. Nanoreactor-type molecular dynamics was used to generate possible chemical reactions, in which the metadynamics was used to overcome the reaction barriers, and the semiempirical GFN2-xTB method was used to reduce the computational cost. Reaction events were identified from trajectories using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for further transition-state searches at the electronic structure levels of density functional theory to obtain the reaction mechanism. Finally, the entire reaction network containing multiple pathways was built. The feasibility and efficiency of the automated construction of the reaction network were investigated using the HCHO and NH3 biomolecular reaction and the reaction network for a multispecies system comprising dozens of HCN and H2O molecules. The results indicated that the proposed approach provides a valuable and effective tool for the automated exploration of the reaction networks.
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Affiliation(s)
- Yutai Zhang
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
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19
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Zhao Q, Anstine DM, Isayev O, Savoie BM. Δ 2 machine learning for reaction property prediction. Chem Sci 2023; 14:13392-13401. [PMID: 38033903 PMCID: PMC10686042 DOI: 10.1039/d3sc02408c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 07/11/2023] [Indexed: 12/02/2023] Open
Abstract
The emergence of Δ-learning models, whereby machine learning (ML) is used to predict a correction to a low-level energy calculation, provides a versatile route to accelerate high-level energy evaluations at a given geometry. However, Δ-learning models are inapplicable to reaction properties like heats of reaction and activation energies that require both a high-level geometry and energy evaluation. Here, a Δ2-learning model is introduced that can predict high-level activation energies based on low-level critical-point geometries. The Δ2 model uses an atom-wise featurization typical of contemporary ML interatomic potentials (MLIPs) and is trained on a dataset of ∼167 000 reactions, using the GFN2-xTB energy and critical-point geometry as a low-level input and the B3LYP-D3/TZVP energy calculated at the B3LYP-D3/TZVP critical point as a high-level target. The excellent performance of the Δ2 model on unseen reactions demonstrates the surprising ease with which the model implicitly learns the geometric deviations between the low-level and high-level geometries that condition the activation energy prediction. The transferability of the Δ2 model is validated on several external testing sets where it shows near chemical accuracy, illustrating the benefits of combining ML models with readily available physical-based information from semi-empirical quantum chemistry calculations. Fine-tuning of the Δ2 model on a small number of Gaussian-4 calculations produced a 35% accuracy improvement over DFT activation energy predictions while retaining xTB-level cost. The Δ2 model approach proves to be an efficient strategy for accelerating chemical reaction characterization with minimal sacrifice in prediction accuracy.
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Affiliation(s)
- Qiyuan Zhao
- Davidson School of Chemical Engineering, Purdue University West Lafayette IN 47906 USA
| | - Dylan M Anstine
- Department of Chemistry, Carnegie Mellon University Pittsburgh PA 15213 USA
| | - Olexandr Isayev
- Department of Chemistry, Carnegie Mellon University Pittsburgh PA 15213 USA
| | - Brett M Savoie
- Davidson School of Chemical Engineering, Purdue University West Lafayette IN 47906 USA
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20
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Li G, Li Z, Gao L, Chen S, Wang G, Li S. Combined molecular dynamics and coordinate driving method for automatically searching complicated reaction pathways. Phys Chem Chem Phys 2023; 25:23696-23707. [PMID: 37610711 DOI: 10.1039/d3cp02443a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The combined molecular dynamics and coordinate driving (MD/CD) method is updated and generalized in this work to broaden its applications in automatically searching reaction pathways for complicated reactions. In this updated version, MD simulations are performed with the GFN's family of methods to systematically sample conformers for almost any systems with atomic numbers Z ≤ 86. The improved CD procedure is greatly accelerated by applying a pre-screening stage at the semiempirical GFN2-xTB level. An automatic module based on the Marcus theory and its improved version (the Wolynes theory) is designed to include single electron transfer (SET) processes into reaction pathways. The capabilities of this method are demonstrated by exploring the most possible reaction pathways of three experimentally reported reactions: the organophosphine-catalyzed trans phosphinoboration, the Fe(II) complex-mediated C(sp2)-H borylation reaction, and the SET-triggered deaminative radical cross-coupling reaction. Comprehensive reaction networks are obtained for all three reactions with reasonable computational costs. Detailed mechanisms for these reactions can account for the reported experimental facts.
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Affiliation(s)
- Guoao Li
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
| | - Zhenxing Li
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
| | - Liuzhou Gao
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
| | - Shengda Chen
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
| | - Guoqiang Wang
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
| | - Shuhua Li
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
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21
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Toniato A, Unsleber JP, Vaucher AC, Weymuth T, Probst D, Laino T, Reiher M. Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning. DIGITAL DISCOVERY 2023; 2:663-673. [PMID: 37312681 PMCID: PMC10259370 DOI: 10.1039/d3dd00006k] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 03/09/2023] [Indexed: 06/15/2023]
Abstract
Data-driven synthesis planning has seen remarkable successes in recent years by virtue of modern approaches of artificial intelligence that efficiently exploit vast databases with experimental data on chemical reactions. However, this success story is intimately connected to the availability of existing experimental data. It may well occur in retrosynthetic and synthesis design tasks that predictions in individual steps of a reaction cascade are affected by large uncertainties. In such cases, it will, in general, not be easily possible to provide missing data from autonomously conducted experiments on demand. However, first-principles calculations can, in principle, provide missing data to enhance the confidence of an individual prediction or for model retraining. Here, we demonstrate the feasibility of such an ansatz and examine resource requirements for conducting autonomous first-principles calculations on demand.
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Affiliation(s)
- Alessandra Toniato
- Laboratory of Physical Chemistry, ETH Zurich Vladimir-Prelog-Weg 2 8093 Zurich Switzerland
- National Center for Competence in Research-Catalysis (NCCR Catalysis), ETH Zurich Vladimir-Prelog-Weg 1-5/10 8093 Zurich Switzerland
- IBM Research Europe 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR Catalysis), IBM Research 8803 Rüschlikon Switzerland
| | - Jan P Unsleber
- Laboratory of Physical Chemistry, ETH Zurich Vladimir-Prelog-Weg 2 8093 Zurich Switzerland
- National Center for Competence in Research-Catalysis (NCCR Catalysis), ETH Zurich Vladimir-Prelog-Weg 1-5/10 8093 Zurich Switzerland
| | - Alain C Vaucher
- IBM Research Europe 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR Catalysis), IBM Research 8803 Rüschlikon Switzerland
| | - Thomas Weymuth
- Laboratory of Physical Chemistry, ETH Zurich Vladimir-Prelog-Weg 2 8093 Zurich Switzerland
- National Center for Competence in Research-Catalysis (NCCR Catalysis), ETH Zurich Vladimir-Prelog-Weg 1-5/10 8093 Zurich Switzerland
| | - Daniel Probst
- IBM Research Europe 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR Catalysis), IBM Research 8803 Rüschlikon Switzerland
| | - Teodoro Laino
- IBM Research Europe 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR Catalysis), IBM Research 8803 Rüschlikon Switzerland
| | - Markus Reiher
- Laboratory of Physical Chemistry, ETH Zurich Vladimir-Prelog-Weg 2 8093 Zurich Switzerland
- National Center for Competence in Research-Catalysis (NCCR Catalysis), ETH Zurich Vladimir-Prelog-Weg 1-5/10 8093 Zurich Switzerland
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22
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Unsleber JP. Accelerating Reaction Network Explorations with Automated Reaction Template Extraction and Application. J Chem Inf Model 2023. [PMID: 37216641 DOI: 10.1021/acs.jcim.3c00102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Autonomously exploring chemical reaction networks with first-principles methods can generate vast data. Especially autonomous explorations without tight constraints risk getting trapped in regions of reaction networks that are not of interest. In many cases, these regions of the networks are only exited once fully searched. Consequently, the required human time for analysis and computer time for data generation can make these investigations unfeasible. Here, we show how simple reaction templates can facilitate the transfer of chemical knowledge from expert input or existing data into new explorations. This process significantly accelerates reaction network explorations and improves cost-effectiveness. We discuss the definition of the reaction templates and their generation based on molecular graphs. The resulting simple filtering mechanism for autonomous reaction network investigations is exemplified with a polymerization reaction.
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Affiliation(s)
- Jan P Unsleber
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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23
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Abstract
Combustion is a reactive oxidation process that releases energy bound in chemical compounds used as fuels─energy that is needed for power generation, transportation, heating, and industrial purposes. Because of greenhouse gas and local pollutant emissions associated with fossil fuels, combustion science and applications are challenged to abandon conventional pathways and to adapt toward the demand of future carbon neutrality. For the design of efficient, low-emission processes, understanding the details of the relevant chemical transformations is essential. Comprehensive knowledge gained from decades of fossil-fuel combustion research includes general principles for establishing and validating reaction mechanisms and process models, relying on both theory and experiments with a suite of analytic monitoring and sensing techniques. Such knowledge can be advantageously applied and extended to configure, analyze, and control new systems using different, nonfossil, potentially zero-carbon fuels. Understanding the impact of combustion and its links with chemistry needs some background. The introduction therefore combines information on exemplary cultural and technological achievements using combustion and on nature and effects of combustion emissions. Subsequently, the methodology of combustion chemistry research is described. A major part is devoted to fuels, followed by a discussion of selected combustion applications, illustrating the chemical information needed for the future.
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24
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Concentration‐Flux‐Steered Mechanism Exploration with an Organocatalysis Application. Isr J Chem 2023. [DOI: 10.1002/ijch.202200123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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25
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Unsleber JP, Liu H, Talirz L, Weymuth T, Mörchen M, Grofe A, Wecker D, Stein CJ, Panyala A, Peng B, Kowalski K, Troyer M, Reiher M. High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation. J Chem Phys 2023; 158:084803. [PMID: 36859110 DOI: 10.1063/5.0136526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
Quantum chemical calculations on atomistic systems have evolved into a standard approach to studying molecular matter. These calculations often involve a significant amount of manual input and expertise, although most of this effort could be automated, which would alleviate the need for expertise in software and hardware accessibility. Here, we present the AutoRXN workflow, an automated workflow for exploratory high-throughput electronic structure calculations of molecular systems, in which (i) density functional theory methods are exploited to deliver minimum and transition-state structures and corresponding energies and properties, (ii) coupled cluster calculations are then launched for optimized structures to provide more accurate energy and property estimates, and (iii) multi-reference diagnostics are evaluated to back check the coupled cluster results and subject them to automated multi-configurational calculations for potential multi-configurational cases. All calculations are carried out in a cloud environment and support massive computational campaigns. Key features of all components of the AutoRXN workflow are autonomy, stability, and minimum operator interference. We highlight the AutoRXN workflow with the example of an autonomous reaction mechanism exploration of the mode of action of a homogeneous catalyst for the asymmetric reduction of ketones.
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Affiliation(s)
- Jan P Unsleber
- Laboratory of Physical Chemistry and NCCR Catalysis, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Hongbin Liu
- Microsoft Quantum, Redmond, Washington 98052, USA
| | | | - Thomas Weymuth
- Laboratory of Physical Chemistry and NCCR Catalysis, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Maximilian Mörchen
- Laboratory of Physical Chemistry and NCCR Catalysis, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Adam Grofe
- Microsoft Quantum, Redmond, Washington 98052, USA
| | - Dave Wecker
- Microsoft Quantum, Redmond, Washington 98052, USA
| | - Christopher J Stein
- Department of Chemistry, TUM School of Natural Sciences, Technical University of Munich, Lichtenbergstr. 4, D-85748 Garching, Germany
| | - Ajay Panyala
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, USA
| | - Bo Peng
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, USA
| | - Karol Kowalski
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, USA
| | | | - Markus Reiher
- Laboratory of Physical Chemistry and NCCR Catalysis, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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26
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Wang G, Wu X, Xin B, Gu X, Wang G, Zhang Y, Zhao J, Cheng X, Chen C, Ma J. Machine Learning in Unmanned Systems for Chemical Synthesis. Molecules 2023; 28:2232. [PMID: 36903478 PMCID: PMC10004533 DOI: 10.3390/molecules28052232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/05/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023] Open
Abstract
Chemical synthesis is state-of-the-art, and, therefore, it is generally based on chemical intuition or experience of researchers. The upgraded paradigm that incorporates automation technology and machine learning (ML) algorithms has recently been merged into almost every subdiscipline of chemical science, from material discovery to catalyst/reaction design to synthetic route planning, which often takes the form of unmanned systems. The ML algorithms and their application scenarios in unmanned systems for chemical synthesis were presented. The prospects for strengthening the connection between reaction pathway exploration and the existing automatic reaction platform and solutions for improving autonomation through information extraction, robots, computer vision, and intelligent scheduling were proposed.
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Affiliation(s)
- Guoqiang Wang
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Xuefei Wu
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
| | - Bo Xin
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
| | - Xu Gu
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Gaobo Wang
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Yong Zhang
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Jiabao Zhao
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
| | - Xu Cheng
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
- Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Chunlin Chen
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
| | - Jing Ma
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
- Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
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27
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Kee CW. Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis-Challenges and Opportunities. Molecules 2023; 28:1715. [PMID: 36838703 PMCID: PMC9966076 DOI: 10.3390/molecules28041715] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/25/2023] Open
Abstract
Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed.
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Affiliation(s)
- Choon Wee Kee
- Institute of Sustainability for Chemicals, Energy and Environment (ISCE2), Agency for Science, Technology and Research (A*STAR), 1 Pesek Road, Jurong Island, Singapore 627833, Republic of Singapore
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28
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Csizi K, Reiher M. Universal
QM
/
MM
approaches for general nanoscale applications. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
| | - Markus Reiher
- Laboratorium für Physikalische Chemie ETH Zürich Zürich Switzerland
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29
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Abstract
Reactivity scales are useful research tools for chemists, both experimental and computational. However, to determine the reactivity of a single molecule, multiple measurements need to be carried out, which is a time-consuming and resource-intensive task. In this Tutorial Review, we present alternative approaches for the efficient generation of quantitative structure-reactivity relationships that are based on quantum chemistry, supervised learning, and uncertainty quantification. First published in 2002, we observe a tendency for these relationships to become not only more predictive but also more interpretable over time.
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Affiliation(s)
- Maike Vahl
- Institute of Physical and Theoretical Chemistry, Technische Universität Braunschweig, Gaußstraße 17, 38106 Braunschweig, Germany.
| | - Jonny Proppe
- Institute of Physical and Theoretical Chemistry, Technische Universität Braunschweig, Gaußstraße 17, 38106 Braunschweig, Germany.
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30
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Exploring catalytic reaction networks with machine learning. Nat Catal 2023. [DOI: 10.1038/s41929-022-00896-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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31
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Zádor J, Martí C, Van de Vijver R, Johansen SL, Yang Y, Michelsen HA, Najm HN. Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces. J Phys Chem A 2023; 127:565-588. [PMID: 36607817 DOI: 10.1021/acs.jpca.2c06558] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Automation of rate-coefficient calculations for gas-phase organic species became possible in recent years and has transformed how we explore these complicated systems computationally. Kinetics workflow tools bring rigor and speed and eliminate a large fraction of manual labor and related error sources. In this paper we give an overview of this quickly evolving field and illustrate, through five detailed examples, the capabilities of our own automated tool, KinBot. We bring examples from combustion and atmospheric chemistry of C-, H-, O-, and N-atom-containing species that are relevant to molecular weight growth and autoxidation processes. The examples shed light on the capabilities of automation and also highlight particular challenges associated with the various chemical systems that need to be addressed in future work.
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Affiliation(s)
- Judit Zádor
- Combustion Research Facility, Sandia National Laboratories, Livermore94550, California, United States
| | - Carles Martí
- Combustion Research Facility, Sandia National Laboratories, Livermore94550, California, United States
| | | | - Sommer L Johansen
- Combustion Research Facility, Sandia National Laboratories, Livermore94550, California, United States
| | - Yoona Yang
- Combustion Research Facility, Sandia National Laboratories, Livermore94550, California, United States
| | - Hope A Michelsen
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder80309, Colorado, United States
| | - Habib N Najm
- Combustion Research Facility, Sandia National Laboratories, Livermore94550, California, United States
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32
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Nakao A, Harabuchi Y, Maeda S, Tsuda K. Exploring the Quantum Chemical Energy Landscape with GNN-Guided Artificial Force. J Chem Theory Comput 2023; 19:713-717. [PMID: 36689311 PMCID: PMC9933424 DOI: 10.1021/acs.jctc.2c01061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Artificial force has been proven useful to get over energy barriers and quickly search a large portion of the energy landscape. This work proposes a method based on graph neural networks to optimize the choice of transformation patterns to examine and accelerate energy landscape exploration. In open search from glutathione, the search efficiency was largely improved in comparison to random selection. We also applied transfer learning from glutathione to tuftsin, resulting in further efficiency gains.
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Affiliation(s)
- Atsuyuki Nakao
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa277-8561, Japan
| | - Yu Harabuchi
- Institute
for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo001-0021, Japan,JST
ERATO Maeda Artificial Intelligence for Chemical Reaction Design and
Discovery Project, Sapporo060-0810, Japan,Department
of Chemistry, Faculty of Science, Hokkaido
University, Sapporo060-0810, Japan
| | - Satoshi Maeda
- Institute
for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo001-0021, Japan,JST
ERATO Maeda Artificial Intelligence for Chemical Reaction Design and
Discovery Project, Sapporo060-0810, Japan,Department
of Chemistry, Faculty of Science, Hokkaido
University, Sapporo060-0810, Japan
| | - Koji Tsuda
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa277-8561, Japan,RIKEN
Center for Advanced Intelligence Project, Tokyo103-0027, Japan,Research
and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba305-0047, Japan,
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33
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Lungu CN, Mangalagiu V, Mangalagiu II, Mehedinti MC. Benzoquinoline Chemical Space: A Helpful Approach in Antibacterial and Anticancer Drug Design. Molecules 2023; 28:molecules28031069. [PMID: 36770739 PMCID: PMC9921191 DOI: 10.3390/molecules28031069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/09/2023] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
Benzoquinolines are used in many drug design projects as starting molecules subject to derivatization. This computational study aims to characterize e benzoquinone drug space to ease future drug design processes based on these molecules. The drug space is composed of all benzoquinones, which are active on topoisomerase II and ATP synthase. Topological, chemical, and bioactivity spaces are explored using computational methodologies based on virtual screening and scaffold hopping and molecular docking, respectively. Topological space is a geometrical space in which the elements composing it can be defined as a set of neighbors (which satisfy a particular axiom). In such space, a chemical space can be defined as the property space spanned by all possible molecules and chemical compounds adhering to a given set of construction principles and boundary conditions. In this chemical space, the potentially pharmacologically active molecules form the bioactivity space. Results show a poly-morphological chemical space that suggests distinct characteristics. The chemical space is correlated with properties such as steric energy, the number of hydrogen bonds, the presence of halogen atoms, and membrane permeability-related properties. Lastly, novel chemical compounds (such as oxadiazole methybenzamide and floro methylcyclohexane diene) with drug-like potential, active on TOPO II and ATP synthase have been identified.
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Affiliation(s)
- Claudiu N. Lungu
- Department of Surgery, Emergency Country Clinical Hospital, 800010 Galati, Romania
- Faculty of Chemistry, Alexandru Ioan Cuza University of Iasi, 11 Carol 1st Bvd, 700506 Iasi, Romania
- Department of Morphological and Functional Science, University of Medicine and Pharmacy, Dunarea de Jos, 800017 Galati, Romania
- Correspondence: (C.N.L.); (I.I.M.)
| | - Violeta Mangalagiu
- Faculty of Chemistry, Alexandru Ioan Cuza University of Iasi, 11 Carol 1st Bvd, 700506 Iasi, Romania
- Faculty of Food Engineering, Stefan cel Mare University of Suceava, 13 Universitatii Str., 720229 Suceava, Romania
| | - Ionel I. Mangalagiu
- Faculty of Chemistry, Alexandru Ioan Cuza University of Iasi, 11 Carol 1st Bvd, 700506 Iasi, Romania
- Institute of Interdisciplinary Research-CERNESIM Centre, Alexandru Ioan Cuza University of Iasi, 11 Carol I, 700506 Iasi, Romania
- Correspondence: (C.N.L.); (I.I.M.)
| | - Mihaela C. Mehedinti
- Faculty of Chemistry, Alexandru Ioan Cuza University of Iasi, 11 Carol 1st Bvd, 700506 Iasi, Romania
- Department of Morphological and Functional Science, University of Medicine and Pharmacy, Dunarea de Jos, 800017 Galati, Romania
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34
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Manna RK, Shklyaev OE, Balazs AC. Chemically Driven Multimodal Locomotion of Active, Flexible Sheets. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2023; 39:780-789. [PMID: 36602946 DOI: 10.1021/acs.langmuir.2c02666] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The inhibitor-promoter feedback loop is a vital component in regulatory pathways that controls functionality in living systems. In this loop, the production of chemical A at one site promotes the production of chemical B at another site, but B inhibits the production of A. In solution, differences in the volumes of the reactants and products of this reaction can generate buoyancy-driven fluid flows, which will deform neighboring soft material. To probe the intrinsic interrelationship among chemistry, hydrodynamics, and fluid-structure interactions, we model a bio-inspired system where a flexible sheet immersed in solution encompasses two spatially separated catalytic patches, which drive the A-B inhibitor-promotor reaction. The convective rolls of fluid generated above the patches can circulate inward or outward depending on the chemical environment. Within the regime displaying chemical oscillations, the dynamic fluid-structure interactions morph the shape of the sheet to periodically "fly", "crawl", or "swim" along the bottom of the confining chamber, revealing an intimate coupling between form and function in this system. The oscillations in the sheet's motion in turn affect the chemical oscillations in the solution. In the regime with non-oscillatory chemistry, the induced flow still morphs the shape of the sheet, but now, the fluid simply translates the sheet along the length of the chamber. The findings reveal the potential for enzymatic reactions in the body to generate hydrodynamic behavior that modifies the shape of neighboring soft tissue, which in turn modifies both the fluid dynamics and the enzymatic reaction. The findings indicate that this non-linear dynamic behavior can be playing a critical role in the functioning of regulatory pathways in living systems.
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Affiliation(s)
- Raj Kumar Manna
- Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania15260, United States
| | - Oleg E Shklyaev
- Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania15260, United States
| | - Anna C Balazs
- Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania15260, United States
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35
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Türtscher PL, Reiher M. Pathfinder─Navigating and Analyzing Chemical Reaction Networks with an Efficient Graph-Based Approach. J Chem Inf Model 2023; 63:147-160. [PMID: 36515968 PMCID: PMC9832502 DOI: 10.1021/acs.jcim.2c01136] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Indexed: 12/15/2022]
Abstract
While the field of first-principles explorations into chemical reaction space has been continuously growing, the development of strategies for analyzing resulting chemical reaction networks (CRNs) is lagging behind. A CRN consists of compounds linked by reactions. Analyzing how these compounds are transformed into one another based on kinetic modeling is a nontrivial task. Here, we present the graph-optimization-driven algorithm and program Pathfinder to allow for such an analysis of a CRN. The CRN for this work has been obtained with our open-source Chemoton reaction network exploration software. Chemoton probes reactive combinations of compounds for elementary steps and sorts them into reactions. By encoding these reactions of the CRN as a graph consisting of compound and reaction vertices and adding information about activation barriers as well as required reagents to the edges of the graph yields a complete graph-theoretical representation of the CRN. Since the probabilities of the formation of compounds depend on the starting conditions, the consumption of any compound during a reaction must be accounted for to reflect the availability of reagents. To account for this, we introduce compound costs to reflect compound availability. Simultaneously, the determined compound costs rank the compounds in the CRN in terms of their probability to be formed. This ranking then allows us to probe easily accessible compounds in the CRN first for further explorations into yet unexplored terrain. We first illustrate the working principle on an abstract small CRN. Afterward, Pathfinder is demonstrated in the example of the disproportionation of iodine with water and the comproportionation of iodic acid and hydrogen iodide. Both processes are analyzed within the same CRN, which we construct with our autonomous first-principles CRN exploration software Chemoton [Unsleber, J. P.; J. Chem. Theory Comput. 2022, 18, 5393-5409] guided by Pathfinder.
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Affiliation(s)
- Paul L. Türtscher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Markus Reiher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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36
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Tu Z, Stuyver T, Coley CW. Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery. Chem Sci 2023; 14:226-244. [PMID: 36743887 PMCID: PMC9811563 DOI: 10.1039/d2sc05089g] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022] Open
Abstract
The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.
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Affiliation(s)
- Zhengkai Tu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Thijs Stuyver
- Department of Chemical Engineering, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Connor W Coley
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
- Department of Chemical Engineering, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
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37
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Wen M, Spotte-Smith EWC, Blau SM, McDermott MJ, Krishnapriyan AS, Persson KA. Chemical reaction networks and opportunities for machine learning. NATURE COMPUTATIONAL SCIENCE 2023; 3:12-24. [PMID: 38177958 DOI: 10.1038/s43588-022-00369-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 11/08/2022] [Indexed: 01/06/2024]
Abstract
Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome.
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Affiliation(s)
- Mingjian Wen
- Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Evan Walter Clark Spotte-Smith
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, USA
| | - Samuel M Blau
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Matthew J McDermott
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, USA
| | - Aditi S Krishnapriyan
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA, USA
- Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | - Kristin A Persson
- Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, USA.
- Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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38
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Krep L, Schmalz F, Solbach F, Komissarov L, Nevolianis T, Kopp WA, Verstraelen T, Leonhard K. A Reactive Molecular Dynamics Study of Chlorinated Organic Compounds. Part II: A ChemTraYzer Study of Chlorinated Dibenzofuran Formation and Decomposition Processes. Chemphyschem 2022; 24:e202200783. [PMID: 36511423 DOI: 10.1002/cphc.202200783] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 12/14/2022]
Abstract
In our two-paper series, we first present the development of ReaxFF CHOCl parameters using the recently published ParAMS parametrization tool. In this second part, we update the reactive Molecular Dynamics - Quantum Mechanics coupling scheme ChemTraYzer and combine it with our new ReaxFF parameters from Part I to study formation and decomposition processes of chlorinated dibenzofurans. We introduce a self-learning method for recovering failed transition-state searches that improves the overall ChemTraYzer transition-state search success rate by 10 percentage points to a total of 48 %. With ChemTraYzer, we automatically find and quantify more than 500 reactions using transition state theory and DFT. Among the discovered chlorinated dibenzofuran reactions are numerous reactions that are new to the literature. In three case studies, we discuss the set of reactions that are most relevant to the dibenzofuran literature: (i) bimolecular reactions of the chlorinated-dibenzofuran precursors phenoxy radical and 1,3,5-trichlorobenzene, (ii) dibenzofuran chlorination and pyrolysis, and (iii) oxidation of chlorinated dibenzofurans.
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Affiliation(s)
- Lukas Krep
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Felix Schmalz
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Florian Solbach
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Leonid Komissarov
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, B-9052, Ghent, Belgium
| | - Thomas Nevolianis
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Wassja A Kopp
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Toon Verstraelen
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, B-9052, Ghent, Belgium
| | - Kai Leonhard
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
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39
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Hashemi A, Bougueroua S, Gaigeot MP, Pidko EA. ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis. J Chem Theory Comput 2022; 18:7470-7482. [PMID: 36321652 DOI: 10.1021/acs.jctc.2c00404] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Exploration of the chemical reaction space of chemical transformations in multicomponent mixtures is one of the main challenges in contemporary computational chemistry. To remove expert bias from mechanistic studies and to discover new chemistries, an automated graph-theoretical methodology is proposed, which puts forward a network formalism of homogeneous catalysis reactions and utilizes a network analysis tool for mechanistic studies. The method can be used for analyzing trajectories with single and multiple catalytic species and can provide unique conformers of catalysts including multinuclear catalyst clusters along with other catalytic mixture components. The presented three-step approach has the integrated ability to handle multicomponent catalytic systems of arbitrary complexity (mixtures of reactants, catalyst precursors, ligands, additives, and solvents). It is not limited to predefined chemical rules, does not require prealignment of reaction mixture components consistent with a reaction coordinate, and is not agnostic to the chemical nature of transformations. Conformer exploration, reactive event identification, and reaction network analysis are the main steps taken for identifying the pathways in catalytic systems given the starting precatalytic reaction mixture as the input. Such a methodology allows us to efficiently explore catalytic systems in realistic conditions for either previously observed or completely unknown reactive events in the context of a network representing different intermediates. Our workflow for the catalytic reaction space exploration exclusively focuses on the identification of thermodynamically feasible conversion channels, representative of the (secondary) catalyst deactivation or inhibition paths, which are usually most difficult to anticipate based solely on expert chemical knowledge. Thus, the expert bias is sought to be removed at all steps, and the chemical intuition is limited to the choice of the thermodynamic constraint imposed by the applicable experimental conditions in terms of threshold energy values for allowed transformations. The capabilities of the proposed methodology have been tested by exploring the reactivity of Mn complexes relevant for catalytic hydrogenation chemistry to verify previously postulated activation mechanisms and unravel unexpected reaction channels relevant to rare deactivation events.
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Affiliation(s)
- Ali Hashemi
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
| | - Sana Bougueroua
- Laboratoire Analyse et Modélisation pour la Biologie et l'Environnement (LAMBE) UMR8587, Universite Paris-Saclay, Univ Evry, CNRS, LAMBE UMR8587, Evry-Courcouronnes 91025, France
| | - Marie-Pierre Gaigeot
- Laboratoire Analyse et Modélisation pour la Biologie et l'Environnement (LAMBE) UMR8587, Universite Paris-Saclay, Univ Evry, CNRS, LAMBE UMR8587, Evry-Courcouronnes 91025, France
| | - Evgeny A Pidko
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
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40
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Lavigne C, Gomes G, Pollice R, Aspuru-Guzik A. Guided discovery of chemical reaction pathways with imposed activation. Chem Sci 2022; 13:13857-13871. [PMID: 36544742 PMCID: PMC9710306 DOI: 10.1039/d2sc05135d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 11/09/2022] [Indexed: 11/12/2022] Open
Abstract
Computational power and quantum chemical methods have improved immensely since computers were first applied to the study of reactivity, but the de novo prediction of chemical reactions has remained challenging. We show that complex reaction pathways can be efficiently predicted in a guided manner using chemical activation imposed by geometrical constraints of specific reactive modes, which we term imposed activation (IACTA). Our approach is demonstrated on realistic and challenging chemistry, such as a triple cyclization cascade involved in the total synthesis of a natural product, a water-mediated Michael addition, and several oxidative addition reactions of complex drug-like molecules. Notably and in contrast with traditional hand-guided computational chemistry calculations, our method requires minimal human involvement and no prior knowledge of the products or the associated mechanisms. We believe that IACTA will be a transformational tool to screen for chemical reactivity and to study both by-product formation and decomposition pathways in a guided way.
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Affiliation(s)
- Cyrille Lavigne
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada
| | - Gabe Gomes
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto80 St George StTorontoOntarioM5S 3H6Canada
| | - Robert Pollice
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto80 St George StTorontoOntarioM5S 3H6Canada
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto80 St George StTorontoOntarioM5S 3H6Canada,Department of Chemical Engineering & Applied Chemistry, University of Toronto200 College St.OntarioM5S 3E5Canada,Department of Materials Science & Engineering, University of Toronto184 College St.OntarioM5S 3E4Canada,Vector Institute for Artificial Intelligence661 University Ave Suite 710TorontoOntarioM5G 1M1Canada,Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR)661 University AveTorontoOntarioM5GCanada
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41
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Stan A, Esch BVD, Ochsenfeld C. Fully Automated Generation of Prebiotically Relevant Reaction Networks from Optimized Nanoreactor Simulations. J Chem Theory Comput 2022; 18:6700-6712. [PMID: 36270030 DOI: 10.1021/acs.jctc.2c00754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The nanoreactor approach first introduced by the group of Martı́nez [Wang et al. Nat. Chem. 2014, 6, 1044-1048] has recently attracted much attention because of its ability to accelerate the discovery of reaction pathways. Here, we provide a comprehensive study of various simulation parameters and present an alternative implementation for the reactivity-enhancing spherical constraint function, as well as for the detection of reaction events. In this context, a fully automated postsimulation evaluation procedure based on RDKit and NetworkX analysis is introduced. The chemical and physical robustness of the procedure is examined by investigating the reactivity of selected homogeneous systems. The optimized procedure is applied at the GFN2-xTB level of theory to a system composed of HCN molecules and argon atoms, acting as a buffer, yielding prebiotically plausible primary and secondary precursors for the synthesis of RNA. Furthermore, the formose reaction network is explored leading to numerous sugar precursors. The discovered compounds reflect experimental findings; however, new synthetic routes and a large collection of exotic, highly reactive molecules are observed, highlighting the predictive power of the nanoreactor approach for unraveling the reactive manifold.
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Affiliation(s)
- Alexandra Stan
- Chair of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstr. 7, D-81377 München, Germany
| | - Beatriz von der Esch
- Chair of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstr. 7, D-81377 München, Germany
| | - Christian Ochsenfeld
- Chair of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstr. 7, D-81377 München, Germany.,Max Planck Institute for Solid State Research, Heisenbergstr. 1, D-70569 Stuttgart, Germany
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42
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Ismail I, Chantreau Majerus R, Habershon S. Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities. J Phys Chem A 2022; 126:7051-7069. [PMID: 36190262 PMCID: PMC9574932 DOI: 10.1021/acs.jpca.2c06408] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 09/22/2022] [Indexed: 11/29/2022]
Abstract
Graph-based descriptors, such as bond-order matrices and adjacency matrices, offer a simple and compact way of categorizing molecular structures; furthermore, such descriptors can be readily used to catalog chemical reactions (i.e., bond-making and -breaking). As such, a number of graph-based methodologies have been developed with the goal of automating the process of generating chemical reaction network models describing the possible mechanistic chemistry in a given set of reactant species. Here, we outline the evolution of these graph-based reaction discovery schemes, with particular emphasis on more recent methods incorporating graph-based methods with semiempirical and ab initio electronic structure calculations, minimum-energy path refinements, and transition state searches. Using representative examples from homogeneous catalysis and interstellar chemistry, we highlight how these schemes increasingly act as "virtual reaction vessels" for interrogating mechanistic questions. Finally, we highlight where challenges remain, including issues of chemical accuracy and calculation speeds, as well as the inherent challenge of dealing with the vast size of accessible chemical reaction space.
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Affiliation(s)
- Idil Ismail
- Department of Chemistry, University
of Warwick, CoventryCV4 7AL, United Kingdom
| | | | - Scott Habershon
- Department of Chemistry, University
of Warwick, CoventryCV4 7AL, United Kingdom
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43
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Ruf A, Danger G. Network Analysis Reveals Spatial Clustering and Annotation of Complex Chemical Spaces: Application to Astrochemistry. Anal Chem 2022; 94:14135-14142. [PMID: 36209417 PMCID: PMC9583070 DOI: 10.1021/acs.analchem.2c01271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
How are molecules
linked to each other in complex systems?
In a
proof-of-concept study, we have developed the method mol2net (https://zenodo.org/record/7025094) to generate and analyze the molecular network of complex astrochemical
data (from high-resolution Orbitrap MS1 analysis of H2O:CH3OH:NH3 interstellar ice analogs)
in a data-driven and unsupervised manner, without any prior knowledge
about chemical reactions. The molecular network is clustered according
to the initial NH3 content and unlocked HCN, NH3, and H2O as spatially resolved key transformations. In
comparison with the PubChem database, four subsets were annotated:
(i) saturated C-backbone molecules without N, (ii) saturated N-backbone
molecules, (iii) unsaturated C-backbone molecules without N, and (iv)
unsaturated N-backbone molecules. These findings were validated with
previous results (e.g., identifying the two major graph components
as previously described N-poor and N-rich molecular groups) but with
additional information about subclustering, key transformations, and
molecular structures, and thus, the structural characterization of
large complex organic molecules in interstellar ice analogs has been
significantly refined.
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Affiliation(s)
- Alexander Ruf
- Laboratoire de Physique des Interactions Ioniques et Moléculaires (PIIM), Université Aix-Marseille, CNRS, 13013 Marseille, France
- Department of Chemistry and Pharmacy, Ludwig-Maximilians-University, 81377 Munich, Germany
- Excellence Cluster ORIGINS, Boltzmannstraße 2, 85748 Garching, Germany
| | - Grégoire Danger
- Laboratoire de Physique des Interactions Ioniques et Moléculaires (PIIM), Université Aix-Marseille, CNRS, 13013 Marseille, France
- Aix-Marseille Université, CNRS, CNES, LAM, 13013 Marseille, France
- Institut Universitaire de France (IUF), 75231 Paris, France
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44
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Pracht P, Bannwarth C. Fast Screening of Minimum Energy Crossing Points with Semiempirical Tight-Binding Methods. J Chem Theory Comput 2022; 18:6370-6385. [PMID: 36121838 DOI: 10.1021/acs.jctc.2c00578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The investigation of photochemical processes is a highly active field in computational chemistry. One research direction is the automated exploration and identification of minimum energy conical intersection (MECI) geometries. However, due to the immense technical effort required to calculate nonadiabatic potential energy landscapes, the routine application of such computational protocols is severely limited. In this study, we will discuss the prospect of combining adiabatic potential energy surfaces from semiempirical quantum mechanical calculations with specialized confinement potential and metadynamics simulations to identify S0/T1 minimum energy crossing point (MECP) geometries. It is shown that MECPs calculated at the GFN2-xTB level can provide suitable approximations to high-level S0/S1ab initio conical intersection geometries at a fraction of the computational cost. Reference MECIs of benzene are studied to illustrate the basic concept. An example application of the presented protocol is demonstrated for a set of photoswitch molecules.
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Affiliation(s)
- Philipp Pracht
- Institute of Physical Chemistry, RWTH Aachen University, Melatener Str. 20, 52056Aachen, Germany
| | - Christoph Bannwarth
- Institute of Physical Chemistry, RWTH Aachen University, Melatener Str. 20, 52056Aachen, Germany
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45
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Oh L, Ji Y, Li W, Varki A, Chen X, Wang LP. O-Acetyl Migration within the Sialic Acid Side Chain: A Mechanistic Study Using the Ab Initio Nanoreactor. Biochemistry 2022; 61:2007-2013. [PMID: 36054099 DOI: 10.1021/acs.biochem.2c00343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Many disease-causing viruses target sialic acids on the surface of host cells. Some viruses bind preferentially to sialic acids with O-acetyl modification at the hydroxyl group of C7, C8, or C9 on the glycerol-like side chain. Studies of proteins binding to sialosides containing O-acetylated sialic acids are crucial in understanding the related diseases but experimentally difficult due to the lability of the ester group. We recently showed that O-acetyl migration among hydroxyl groups of C7, C8, and C9 in sialic acids occurs in all directions in a pH-dependent manner. In the current study, we elucidate a full mechanistic pathway for the migration of O-acetyl among C7, C8, and C9. We used an ab initio nanoreactor to explore potential reaction pathways and density functional theory, pKa calculations, and umbrella sampling to investigate elementary steps of interest. We found that when a base is present, migration is easy in any direction and involves three key steps: deprotonation of the hydroxyl group, cyclization between the two carbons, and the migration of the O-acetyl group. This dynamic equilibrium may play a defensive role against pathogens that evolve to gain entry to the cell by binding selectively to one acetylation state.
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Affiliation(s)
- Lisa Oh
- Department of Chemistry, University of California, Davis, California 95616, United States
| | - Yang Ji
- Glycobiology Research and Training Center, Departments of Medicine and Cellular and Molecular Medicine, University of California, San Diego, California 92093, United States
| | - Wanqing Li
- Department of Chemistry, University of California, Davis, California 95616, United States
| | - Ajit Varki
- Glycobiology Research and Training Center, Departments of Medicine and Cellular and Molecular Medicine, University of California, San Diego, California 92093, United States
| | - Xi Chen
- Department of Chemistry, University of California, Davis, California 95616, United States
| | - Lee-Ping Wang
- Department of Chemistry, University of California, Davis, California 95616, United States
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46
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Petrus E, Segado-Centellas M, Bo C. Computational Prediction of Speciation Diagrams and Nucleation Mechanisms: Molecular Vanadium, Niobium, and Tantalum Oxide Nanoclusters in Solution. Inorg Chem 2022; 61:13708-13718. [PMID: 35998382 DOI: 10.1021/acs.inorgchem.2c00925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Understanding the aqueous speciation of molecular metal-oxo-clusters plays a key role in different fields such as catalysis, electrochemistry, nuclear waste recycling, and biochemistry. To describe the speciation accurately, it is essential to elucidate the underlying self-assembly processes. Herein, we apply a computational method to predict the speciation and formation mechanisms of polyoxovanadates, -niobates, and -tantalates. While polyoxovanadates have been widely studied, polyoxoniobates and -tantalates lack the same level of understanding. First, we propose a pentavanadate cluster ([V5O14]3-) as a key intermediate for the formation of the decavanadate. Our computed phase speciation diagram is in particularly good agreement with the experiments. Second, we report the formation constants of the heptaniobate, [Nb7O22]9-, decaniobate, [Nb10O28]6-, and tetracosaniobate [H9Nb24O72]15-. Additionally, we compute the speciation and phase diagram of niobium, which so far was restricted to Lindqvist derivates. Finally, we predict the formation constant of the decatantalate ([Ta10O26]6-) in water, even though it had only been synthesized in toluene. Furthermore, we also calculate the corresponding speciation and phase diagrams for polyoxotantalates. Overall, we show that our method can be successfully applied to different families of molecular metal oxides without any need for readjustments; therefore, it can be regarded as a trustworthy tool for exploring polyoxometalates' chemistry.
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Affiliation(s)
- Enric Petrus
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology (BIST), Av. Països Catalans, 16, 43007 Tarragona, Spain.,Departament de Química Física i Inorgánica, Universitat Rovira i Virgili, Marcel•lí Domingo s/n, 43007 Tarragona, Spain
| | - Mireia Segado-Centellas
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology (BIST), Av. Països Catalans, 16, 43007 Tarragona, Spain
| | - Carles Bo
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology (BIST), Av. Països Catalans, 16, 43007 Tarragona, Spain.,Departament de Química Física i Inorgánica, Universitat Rovira i Virgili, Marcel•lí Domingo s/n, 43007 Tarragona, Spain
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47
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Zhao Q, Xu Y, Greeley J, Savoie BM. Deep reaction network exploration at a heterogeneous catalytic interface. Nat Commun 2022; 13:4860. [PMID: 35982057 PMCID: PMC9388529 DOI: 10.1038/s41467-022-32514-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/03/2022] [Indexed: 11/09/2022] Open
Abstract
Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications.
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Affiliation(s)
- Qiyuan Zhao
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA
| | - Yinan Xu
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA
| | - Jeffrey Greeley
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA.
| | - Brett M Savoie
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA.
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48
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Unsleber JP, Grimmel SA, Reiher M. Chemoton 2.0: Autonomous Exploration of Chemical Reaction Networks. J Chem Theory Comput 2022; 18:5393-5409. [PMID: 35926118 DOI: 10.1021/acs.jctc.2c00193] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Fueled by advances in hardware and algorithm design, large-scale automated explorations of chemical reaction space have become possible. Here, we present our approach to an open-source, extensible framework for explorations of chemical reaction mechanisms based on the first-principles of quantum mechanics. It is intended to facilitate reaction network explorations for diverse chemical problems with a wide range of goals such as mechanism elucidation, reaction path optimization, retrosynthetic path validation, reagent design, and microkinetic modeling. The stringent first-principles basis of all algorithms in our framework is key for the general applicability that avoids any restrictions to specific chemical systems. Such an agile framework requires multiple specialized software components of which we present three modules in this work. The key module, Chemoton, drives the exploration of reaction networks. For the exploration itself, we introduce two new algorithms for elementary-step searches that are based on Newton trajectories. The performance of these algorithms is assessed for a variety of reactions characterized by a broad chemical diversity in terms of bonding patterns and chemical elements. Chemoton successfully recovers the vast majority of these. We provide the resulting data, including large numbers of reactions that were not included in our reference set, to be used as a starting point for further explorations and for future reference.
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Affiliation(s)
- Jan P Unsleber
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Stephanie A Grimmel
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Markus Reiher
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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49
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Liu X, Wang W, Wright S, Doppelbauer M, Meijer G, Truppe S, PEREZ RIOS JESUS. The chemistry of AlF and CaF production in buffer gas sources. J Chem Phys 2022; 157:074305. [DOI: 10.1063/5.0098378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
In this work, we explore the role of chemical reactions on the properties of buffer gas cooled molecular beams. In particular, we focus on scenarios relevant to the formation of AlF and CaF via chemical reactions between the Ca and Al atoms ablated from a solid target in an atmosphere of a fluorine-containing gas, in this case, SF6 and NF3. Reactions are studied following an ab initio molecular dynamics approach, and the results are rationalized following a tree-shaped reaction model based on Bayesian inference. We find that NF3 reacts more efficiently with hot metal atoms to form monofluoride molecules than SF6. In addition, when using NF3, the reaction products have lower kinetic energy, requiring fewer collisions to thermalize with the cryogenic helium. Furthermore, we find that the reaction probability for AlF formation is much higher than for CaF across a broad range of temperatures.
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Affiliation(s)
- Xiangyue Liu
- Fritz Haber Institut der Max-Planck-Gesellschaft, Germany
| | - Weiqi Wang
- Fritz Haber Institut der Max-Planck-Gesellschaft, Germany
| | - Sidney Wright
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Germany
| | | | - Gerard Meijer
- Fritz-Haber-Institut, Max-Planck-Gesellschaft, Germany
| | - Stefan Truppe
- Fritz Haber Institut der Max-Planck-Gesellschaft, Germany
| | - JESUS PEREZ RIOS
- Molecular Physics, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Germany
- Stony Brook University Department of Physics and Astronomy
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50
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Zhu LT, Chen XZ, Ouyang B, Yan WC, Lei H, Chen Z, Luo ZH. Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01036] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Li-Tao Zhu
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Xi-Zhong Chen
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, S1 3JD, U.K
| | - Bo Ouyang
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Wei-Cheng Yan
- School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - He Lei
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zhe Chen
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zheng-Hong Luo
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
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