1
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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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2
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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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3
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Csizi KS, Reiher M. Automated preparation of nanoscopic structures: Graph-based sequence analysis, mismatch detection, and pH-consistent protonation with uncertainty estimates. J Comput Chem 2024; 45:761-776. [PMID: 38124290 DOI: 10.1002/jcc.27276] [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: 11/11/2023] [Accepted: 11/14/2023] [Indexed: 12/23/2023]
Abstract
Structure and function in nanoscale atomistic assemblies are tightly coupled, and every atom with its specific position and even every electron will have a decisive effect on the electronic structure, and hence, on the molecular properties. Molecular simulations of nanoscopic atomistic structures therefore require accurately resolved three-dimensional input structures. If extracted from experiment, these structures often suffer from severe uncertainties, of which the lack of information on hydrogen atoms is a prominent example. Hence, experimental structures require careful review and curation, which is a time-consuming and error-prone process. Here, we present a fast and robust protocol for the automated structure analysis and pH-consistent protonation, in short, ASAP. For biomolecules as a target, the ASAP protocol integrates sequence analysis and error assessment of a given input structure. ASAP allows for pK a prediction from reference data through Gaussian process regression including uncertainty estimation and connects to system-focused atomistic modeling described in Brunken and Reiher (J. Chem. Theory Comput. 16, 2020, 1646). Although focused on biomolecules, ASAP can be extended to other nanoscopic objects, because most of its design elements rely on a general graph-based foundation guaranteeing transferability. The modular character of the underlying pipeline supports different degrees of automation, which allows for (i) efficient feedback loops for human-machine interaction with a low entrance barrier and for (ii) integration into autonomous procedures such as automated force field parametrizations. This facilitates fast switching of the pH-state through on-the-fly system-focused reparametrization during a molecular simulation at virtually no extra computational cost.
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Affiliation(s)
- Katja-Sophia Csizi
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Markus Reiher
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
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4
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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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5
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Csizi K, Eckert L, Brunken C, Hofstetter TB, Reiher M. The Apparently Unreactive Substrate Facilitates the Electron Transfer for Dioxygen Activation in Rieske Dioxygenases. Chemistry 2022; 28:e202103937. [PMID: 35072969 PMCID: PMC9306888 DOI: 10.1002/chem.202103937] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Indexed: 12/29/2022]
Abstract
Rieske dioxygenases belong to the non-heme iron family of oxygenases and catalyze important cis-dihydroxylation as well as O-/N-dealkylation and oxidative cyclization reactions for a wide range of substrates. The lack of substrate coordination at the non-heme ferrous iron center, however, makes it particularly challenging to delineate the role of the substrate for productive O 2 activation. Here, we studied the role of the substrate in the key elementary reaction leading to O 2 activation from a theoretical perspective by systematically considering (i) the 6-coordinate to 5-coordinate conversion of the non-heme FeII upon abstraction of a water ligand, (ii) binding of O 2 , and (iii) transfer of an electron from the Rieske cluster. We systematically evaluated the spin-state-dependent reaction energies and structural effects at the active site for all combinations of the three elementary processes in the presence and absence of substrate using naphthalene dioxygenase as a prototypical Rieske dioxygenase. We find that reaction energies for the generation of a coordination vacancy at the non-heme FeII center through thermoneutral H2 O reorientation and exothermic O 2 binding prior to Rieske cluster oxidation are largely insensitive to the presence of naphthalene and do not lead to formation of any of the known reactive Fe-oxygen species. By contrast, the role of the substrate becomes evident after Rieske cluster oxidation and exclusively for the 6-coordinate non-heme FeII sites in that the additional electron is found at the substrate instead of at the iron and oxygen atoms. Our results imply an allosteric control of the substrate on Rieske dioxygenase reactivity to happen prior to changes at the non-heme FeII in agreement with a strategy that avoids unproductive O 2 activation.
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Affiliation(s)
- Katja‐Sophia Csizi
- EawagSwiss Federal Institute of Aquatic Science and TechnologyÜberlandstrasse 1338600DübendorfSwitzerland
- ETH ZürichLaboratory for Physical ChemistryVladimir-Prelog-Weg 28093ZürichSwitzerland
| | - Lina Eckert
- ETH ZürichLaboratory for Physical ChemistryVladimir-Prelog-Weg 28093ZürichSwitzerland
| | - Christoph Brunken
- EawagSwiss Federal Institute of Aquatic Science and TechnologyÜberlandstrasse 1338600DübendorfSwitzerland
- ETH ZürichLaboratory for Physical ChemistryVladimir-Prelog-Weg 28093ZürichSwitzerland
| | - Thomas B. Hofstetter
- EawagSwiss Federal Institute of Aquatic Science and TechnologyÜberlandstrasse 1338600DübendorfSwitzerland
| | - Markus Reiher
- ETH ZürichLaboratory for Physical ChemistryVladimir-Prelog-Weg 28093ZürichSwitzerland
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6
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Steiner M, Reiher M. Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis. Top Catal 2022; 65:6-39. [PMID: 35185305 PMCID: PMC8816766 DOI: 10.1007/s11244-021-01543-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2021] [Indexed: 12/11/2022]
Abstract
Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system.
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Affiliation(s)
- Miguel Steiner
- 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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7
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Bensberg M, Türtscher PL, Unsleber JP, Reiher M, Neugebauer J. Solvation Free Energies in Subsystem Density Functional Theory. J Chem Theory Comput 2022; 18:723-740. [PMID: 34985890 DOI: 10.1021/acs.jctc.1c00864] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
For many chemical processes the accurate description of solvent effects are vitally important. Here, we describe a hybrid ansatz for the explicit quantum mechanical description of solute-solvent and solvent-solvent interactions based on subsystem density functional theory and continuum solvation schemes. Since explicit solvent molecules may compromise the scalability of the model and transferability of the predicted solvent effect, we aim to retain both, for different solutes as well as for different solvents. The key for the transferability is the consistent subsystem decomposition of solute and solvent. The key for the scalability is the performance of subsystem DFT for increasing numbers of subsystems. We investigate molecular dynamics and stationary point sampling of solvent configurations and compare the resulting (Gibbs) free energies to experiment and theoretical methods. We can show that with our hybrid model reaction barriers and reaction energies are accurately reproduced compared to experimental data.
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Affiliation(s)
- Moritz Bensberg
- Theoretische Organische Chemie, Organisch-Chemisches Institut and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität Münster, Corrensstraße 36, 48149 Münster, Germany
| | - Paul L Türtscher
- ETH Zürich, Laboratorium für Physikalische Chemie, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Jan P Unsleber
- ETH Zürich, Laboratorium für Physikalische Chemie, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Markus Reiher
- ETH Zürich, Laboratorium für Physikalische Chemie, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Johannes Neugebauer
- Theoretische Organische Chemie, Organisch-Chemisches Institut and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität Münster, Corrensstraße 36, 48149 Münster, Germany
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8
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Affiliation(s)
- Markus Reiher
- ETH Zürich, Laboratorium für Physikalische Chemie Vladimir-Prelog-Weg 2 8093 Zürich Switzerland
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9
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Barros EP, Ries B, Böselt L, Champion C, Riniker S. Recent developments in multiscale free energy simulations. Curr Opin Struct Biol 2021; 72:55-62. [PMID: 34534706 DOI: 10.1016/j.sbi.2021.08.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/06/2021] [Accepted: 08/16/2021] [Indexed: 11/26/2022]
Abstract
Physics-based free energy simulations enable the rigorous calculation of properties, such as conformational equilibria, solvation or binding free energies. While historically most applications have occurred at the atomistic level of resolution, a range of advances in the past years make it possible now to reliably cross the temporal, spatial and theory scales for the modeling of complex systems or the efficient prediction of results at the accuracy level of expensive quantum-mechanical calculations. In this mini-review, we discuss recent methodological advances as well as opportunities opened up by the introduction of machine learning approaches, which tackle the diverse challenges across the different scales, improve the accuracy and feasibility, and push the boundaries of multiscale free energy simulations.
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Affiliation(s)
- Emilia P Barros
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Benjamin Ries
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Lennard Böselt
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Candide Champion
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
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10
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Zheng D, Wang F. Performing Molecular Dynamics Simulations and Computing Hydration Free Energies on the B3LYP-D3(BJ) Potential Energy Surface with Adaptive Force Matching: A Benchmark Study with Seven Alcohols and One Amine. ACS PHYSICAL CHEMISTRY AU 2021; 1:14-24. [PMID: 34939071 PMCID: PMC8679650 DOI: 10.1021/acsphyschemau.1c00006] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Indexed: 12/13/2022]
Abstract
The potential energy surfaces at the B3LYP-D3(BJ) level for eight solutes in dilute aqueous solutions were mapped into simple pairwise additive force field expressions using the adaptive force matching (AFM) method. The quality of the fits was validated by computing the hydration free energy (HFE), enthalpy of hydration, and diffusion constant for each solute. By force matching B3LYP-D3(BJ), the predictions from the models agree with the closest experimental HFE and enthalpy of hydration within chemical accuracy. The diffusion constants from the models are also in good agreement with experimental references. The good agreement provides confidence on the quality of B3LYP-D3(BJ) in producing potential energy surfaces for thermodynamic property calculations through AFM for the molecules studied. Accurate computational predictions could potentially provide validations to experimental measurements in cases where experimental measurements from different sources do not agree.
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11
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Sami S, Menger MFSJ, Faraji S, Broer R, Havenith RWA. Q-Force: Quantum Mechanically Augmented Molecular Force Fields. J Chem Theory Comput 2021; 17:4946-4960. [PMID: 34251194 PMCID: PMC8359013 DOI: 10.1021/acs.jctc.1c00195] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
The quality of molecular
dynamics simulations strongly depends
on the accuracy of the underlying force fields (FFs) that determine
all intra- and intermolecular interactions of the system. Commonly,
transferable FF parameters are determined based on a representative
set of small molecules. However, such an approach sacrifices accuracy
in favor of generality. In this work, an open-source and automated
toolkit named Q-Force is presented, which augments these transferable
FFs with molecule-specific bonded parameters and atomic charges that
are derived from quantum mechanical (QM) calculations. The molecular
fragmentation procedure allows treatment of large molecules (>200
atoms) with a low computational cost. The generated Q-Force FFs can
be used at the same computational cost as transferable FFs, but with
improved accuracy: We demonstrate this for the vibrational properties
on a set of small molecules and for the potential energy surface on
a complex molecule (186 atoms) with photovoltaic applications. Overall,
the accuracy, user-friendliness, and minimal computational overhead
of the Q-Force protocol make it widely applicable for atomistic molecular
dynamics simulations.
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Affiliation(s)
- Selim Sami
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands.,Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Maximilian F S J Menger
- Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Shirin Faraji
- Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Ria Broer
- Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Remco W A Havenith
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands.,Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands.,Department of Inorganic and Physical Chemistry, Ghent University, Krijgslaan 281-(S3), B-9000 Ghent, Belgium
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12
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Vilhena JG, Greff da Silveira L, Livotto PR, Cacelli I, Prampolini G. Automated Parameterization of Quantum Mechanically Derived Force Fields for Soft Materials and Complex Fluids: Development and Validation. J Chem Theory Comput 2021; 17:4449-4464. [PMID: 34185536 DOI: 10.1021/acs.jctc.1c00213] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The reliability of molecular dynamics (MD) simulations in predicting macroscopic properties of complex fluids and soft materials, such as liquid crystals, colloidal suspensions, or polymers, relies on the accuracy of the adopted force field (FF). We present an automated protocol to derive specific and accurate FFs, fully based on ab initio quantum mechanical (QM) data. The integration of the Joyce and Picky procedures, recently proposed by our group to provide an accurate description of simple liquids, is here extended to larger molecules, capable of exhibiting more complex fluid phases. While the standard Joyce protocol is employed to parameterize the intramolecular FF term, a new automated procedure is here proposed to handle the computational cost of the QM calculations required for the parameterization of the intermolecular FF term. The latter is thus obtained by integrating the old Picky procedure with a fragmentation reconstruction method (FRM) that allows for a reliable, yet computationally feasible sampling of the intermolecular energy surface at the QM level. The whole FF parameterization protocol is tested on a benchmark liquid crystal, and the performances of the resulting quantum mechanically derived (QMD) FF were compared with those delivered by a general-purpose, transferable one, and by the third, "hybrid" FF, where only the bonded terms were refined against QM data. Lengthy atomistic MD simulations are carried out with each FF on extended 5CB systems in both isotropic and nematic phases, eventually validating the proposed protocol by comparing the resulting macroscopic properties with other computational models and with experiments. The QMD-FF yields the best performances, reproducing both phases in the correct range of temperatures and well describing their structure, dynamics, and thermodynamic properties, thus providing a clear protocol that may be explored to predict such properties on other complex fluids or soft materials.
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Affiliation(s)
- J G Vilhena
- Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland
| | - Leandro Greff da Silveira
- Instituto de Química, Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves 9500, CEP 91501-970 Porto Alegre, Brazil
| | - Paolo Roberto Livotto
- Instituto de Química, Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves 9500, CEP 91501-970 Porto Alegre, Brazil
| | - Ivo Cacelli
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa, Via G. Moruzzi 13, I-56124 Pisa, Italy
| | - Giacomo Prampolini
- Istituto di Chimica dei Composti OrganoMetallici, ICCOM-CNR, Area della Ricerca, via G. Moruzzi 1, I-56124 Pisa, Italy
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13
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Brunken C, Reiher M. Automated Construction of Quantum–Classical Hybrid Models. J Chem Theory Comput 2021; 17:3797-3813. [DOI: 10.1021/acs.jctc.1c00178] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Christoph Brunken
- 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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14
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A Force Field for a Manganese-Vanadium Water Oxidation Catalyst: Redox Potentials in Solution as Showcase. Catalysts 2021. [DOI: 10.3390/catal11040493] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
We present a molecular mechanics force field in AMBER format for the mixed-valence manganese vanadium oxide cluster [Mn4V4O17(OAc)3]3−—a synthetic analogue of the oxygen-evolving complex that catalyzes the water oxidation reaction in photosystem II—with parameter sets for two different oxidation states. Most force field parameters involving metal atoms have been newly parametrized and the harmonic terms refined using hybrid quantum mechanics/molecular mechanics reference simulations, although some parameters were adapted from pre-existing force fields of vanadate cages and manganese oxo dimers. The characteristic Jahn–Teller distortions of d4 MnIII ions in octahedral environments are recovered by the force field. As an application, the developed parameters have been used to calculate the redox potential of the [MnIIIMn3IV] ⇌ [Mn4IV]+e− half-reaction in acetonitrile by means of Marcus theory.
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15
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Böselt L, Thürlemann M, Riniker S. Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems. J Chem Theory Comput 2021; 17:2641-2658. [PMID: 33818085 DOI: 10.1021/acs.jctc.0c01112] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary. However, QM/MM MD simulations are computationally expensive compared to fully classical simulations as all valence electrons are treated explicitly and a self-consistent field (SCF) procedure is required. Recently, approaches have been proposed to replace the QM description with machine-learned (ML) models. However, condensed-phase systems pose a challenge for these approaches due to long-range interactions. Here, we establish a workflow, which incorporates the MM environment as an element type in a high-dimensional neural network potential (HDNNP). The fitted HDNNP describes the potential-energy surface of the QM particles with an electrostatic embedding scheme. Thus, the MM particles feel a force from the polarized QM particles. To achieve chemical accuracy, we find that even simple systems require models with a strong gradient regularization, a large number of data points, and a substantial number of parameters. To address this issue, we extend our approach to a Δ-learning scheme, where the ML model learns the difference between a reference method (density functional theory (DFT)) and a cheaper semiempirical method (density functional tight binding (DFTB)). We show that such a scheme reaches the accuracy of the DFT reference method while requiring significantly less parameters. Furthermore, the Δ-learning scheme is capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. It is validated by performing MD simulations of retinoic acid in water and the interaction between S-adenoslymethioniat and cytosine in water. The presented results indicate that Δ-learning is a promising approach for (QM)ML/MM MD simulations of condensed-phase systems.
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Affiliation(s)
- Lennard Böselt
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Moritz Thürlemann
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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16
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Quantum Chemical Microsolvation by Automated Water Placement. Molecules 2021; 26:molecules26061793. [PMID: 33806731 PMCID: PMC8005176 DOI: 10.3390/molecules26061793] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/13/2021] [Accepted: 03/15/2021] [Indexed: 11/17/2022] Open
Abstract
We developed a quantitative approach to quantum chemical microsolvation. Key in our methodology is the automatic placement of individual solvent molecules based on the free energy solvation thermodynamics derived from molecular dynamics (MD) simulations and grid inhomogeneous solvation theory (GIST). This protocol enabled us to rigorously define the number, position, and orientation of individual solvent molecules and to determine their interaction with the solute based on physical quantities. The generated solute-solvent clusters served as an input for subsequent quantum chemical investigations. We showcased the applicability, scope, and limitations of this computational approach for a number of small molecules, including urea, 2-aminobenzothiazole, (+)-syn-benzotriborneol, benzoic acid, and helicene. Our results show excellent agreement with the available ab initio molecular dynamics data and experimental results.
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17
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Zheng M, Jaramillo-Botero A, Ju XH, Goddard WA. Coarse-grained force-field for large scale molecular dynamics simulations of polyacrylamide and polyacrylamide-gels based on quantum mechanics. Phys Chem Chem Phys 2021; 23:10909-10918. [DOI: 10.1039/d0cp05767c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Developing a coarse-grained force field for polyacrylamide based on quantum mechanics equation of state.
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Affiliation(s)
- Mei Zheng
- Materials and Process Simulation Center
- California Institute of Technology
- Pasadena
- USA
- Key Laboratory of Soft Chemistry and Functional Materials of MOE
| | | | - Xue-hai Ju
- Key Laboratory of Soft Chemistry and Functional Materials of MOE
- School of Chemical Engineering, Nanjing University of Science and Technology
- Nanjing 210094
- P. R. China
| | - William A. Goddard
- Materials and Process Simulation Center
- California Institute of Technology
- Pasadena
- USA
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18
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Abstract
It is an ultimate goal in chemistry to predict reaction without recourse to experiment. Reaction prediction is not just the reaction rate determination of known reactions but, more broadly, the reaction exploration to identify new reaction routes. This review briefly overviews the theory on chemical reaction and the current methods for computing/estimating reaction rate and exploring reaction space. We particularly focus on the atomistic simulation methods for reaction exploration, which are benefited significantly by recently emerged machine learning potentials. We elaborate the stochastic surface walking global pathway sampling based on the global neural network (SSW-NN) potential, developed in our group since 2013, which can explore complex reactions systems unbiasedly and automatedly. Two examples, molecular reaction and heterogeneous catalytic reactions, are presented to illustrate the current status for reaction prediction using SSW-NN.
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Affiliation(s)
- Pei-Lin Kang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
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19
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Amabilino S, Bratholm LA, Bennie SJ, O’Connor MB, Glowacki DR. Training atomic neural networks using fragment-based data generated in virtual reality. J Chem Phys 2020; 153:154105. [DOI: 10.1063/5.0015950] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Affiliation(s)
- Silvia Amabilino
- School of Chemistry, University of Bristol, Bristol BS8 1TS, United Kingdom
- Intangible Realities Laboratory, University of Bristol, Bristol BS8 1UB, United Kingdom
| | - Lars A. Bratholm
- School of Chemistry, University of Bristol, Bristol BS8 1TS, United Kingdom
- Intangible Realities Laboratory, University of Bristol, Bristol BS8 1UB, United Kingdom
| | - Simon J. Bennie
- School of Chemistry, University of Bristol, Bristol BS8 1TS, United Kingdom
- Intangible Realities Laboratory, University of Bristol, Bristol BS8 1UB, United Kingdom
| | - Michael B. O’Connor
- Intangible Realities Laboratory, University of Bristol, Bristol BS8 1UB, United Kingdom
- Department of Computer Science, University of Bristol, Bristol BS8 1UB, United Kingdom
| | - David R. Glowacki
- School of Chemistry, University of Bristol, Bristol BS8 1TS, United Kingdom
- Intangible Realities Laboratory, University of Bristol, Bristol BS8 1UB, United Kingdom
- Department of Computer Science, University of Bristol, Bristol BS8 1UB, United Kingdom
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20
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König G, Riniker S. On the faithfulness of molecular mechanics representations of proteins towards quantum-mechanical energy surfaces. Interface Focus 2020; 10:20190121. [PMID: 33184586 DOI: 10.1098/rsfs.2019.0121] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/06/2020] [Indexed: 12/11/2022] Open
Abstract
Force fields based on molecular mechanics (MM) are the main computational tool to study the relationship between protein structure and function at the molecular level. To validate the quality of such force fields, high-level quantum-mechanical (QM) data are employed to test their capability to reproduce the features of all major conformational substates of a series of blocked amino acids. The phase-space overlap between MM and QM is quantified in terms of the average structural reorganization energies over all energy minima. Here, the structural reorganization energy is the MM potential-energy difference between the structure of the respective QM energy minimum and the structure of the closest MM energy minimum. Thus, it serves as a measure for the relative probability of visiting the QM minimum during an MM simulation. We evaluate variants of the AMBER, CHARMM, GROMOS and OPLS biomolecular force fields. In addition, the two blocked amino acids alanine and serine are used to demonstrate the dependence of the measured agreement on the QM method, the phase, and the conformational preferences. Blocked serine serves as an example to discuss possible improvements of the force fields, such as including polarization with Drude particles, or using tailored force fields. The results show that none of the evaluated force fields satisfactorily reproduces all energy minima. By decomposing the average structural reorganization energies in terms of individual energy terms, we can further assess the individual weaknesses of the parametrization strategies of each force field. The dominant problem for most force fields appears to be the van der Waals parameters, followed to a lesser degree by dihedral and bonded terms. Our results show that performing a simple QM energy optimization from an MM-optimized structure can be a first test of the validity of a force field for a particular target molecule.
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Affiliation(s)
- Gerhard König
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany.,Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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21
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Bahlke MP, Mogos N, Proppe J, Herrmann C. Exchange Spin Coupling from Gaussian Process Regression. J Phys Chem A 2020; 124:8708-8723. [DOI: 10.1021/acs.jpca.0c05983] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Marc Philipp Bahlke
- Department of Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany
| | - Natnael Mogos
- Department of Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany
| | - Jonny Proppe
- Institute of Physical Chemistry, Georg-August University, Tammannstr. 6, 37077 Göttingen, Germany
| | - Carmen Herrmann
- Department of Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany
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