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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Spiekermann KA, Dong X, Menon A, Green WH, Pfeifle M, Sandfort F, Welz O, Bergeler M. Accurately Predicting Barrier Heights for Radical Reactions in Solution Using Deep Graph Networks. J Phys Chem A 2024; 128:8384-8403. [PMID: 39298746 DOI: 10.1021/acs.jpca.4c04121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
Quantitative estimates of reaction barriers and solvent effects are essential for developing kinetic mechanisms and predicting reaction outcomes. Here, we create a new data set of 5,600 unique elementary radical reactions calculated using the M06-2X/def2-QZVP//B3LYP-D3(BJ)/def2-TZVP level of theory. A conformer search is done for each species using TPSS/def2-TZVP. Gibbs free energies of activation and of reaction for these radical reactions in 40 common solvents are obtained using COSMO-RS for solvation effects. These balanced reactions involve the elements H, C, N, O, and S, contain up to 19 heavy atoms, and have atom-mapped SMILES. All transition states are verified by an intrinsic reaction coordinate calculation. We next train a deep graph network to directly estimate the Gibbs free energy of activation and of reaction in both gas and solution phases using only the atom-mapped SMILES of the reactant and product and the SMILES of the solvent. This simple input representation avoids computationally expensive optimizations for the reactant, transition state, and product structures during inference, making our model well-suited for high-throughput predictive chemistry and quickly providing information for (retro-)synthesis planning tools. To properly measure model performance, we report results on both interpolative and extrapolative data splits and also compare to several baseline models. During training and testing, the data set is augmented by including the reverse direction of each reaction and variants with different resonance structures. After data augmentation, we have around 2 million entries to train the model, which achieves a testing set mean absolute error of 1.16 kcal mol-1 for the Gibbs free energy of activation in solution. We anticipate this model will accelerate predictions for high-throughput screening to quickly identify relevant reactions in solution, and our data set will serve as a benchmark for future studies.
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Affiliation(s)
- Kevin A Spiekermann
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Xiaorui Dong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Angiras Menon
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Mark Pfeifle
- BASF Digital Solutions GmbH, Ludwigshafen am Rhein 67061, Germany
| | - Frederik Sandfort
- BASF SE, Scientific Modeling, Group Research, Ludwigshafen am Rhein 67056, Germany
| | - Oliver Welz
- BASF SE, Scientific Modeling, Group Research, Ludwigshafen am Rhein 67056, Germany
| | - Maike Bergeler
- BASF SE, Scientific Modeling, Group Research, Ludwigshafen am Rhein 67056, Germany
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3
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van Gerwen P, Briling KR, Bunne C, Somnath VR, Laplaza R, Krause A, Corminboeuf C. 3DReact: Geometric Deep Learning for Chemical Reactions. J Chem Inf Model 2024; 64:5771-5785. [PMID: 39007724 PMCID: PMC11323278 DOI: 10.1021/acs.jcim.4c00104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 07/03/2024] [Accepted: 07/08/2024] [Indexed: 07/16/2024]
Abstract
Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.
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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
| | - Charlotte Bunne
- National
Center for Competence in Research − Catalysis (NCCR-Catalysis), École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
- Learning
& Adaptive Systems Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Vignesh Ram Somnath
- National
Center for Competence in Research − Catalysis (NCCR-Catalysis), École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
- Learning
& Adaptive Systems Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Ruben Laplaza
- 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
| | - Andreas Krause
- National
Center for Competence in Research − Catalysis (NCCR-Catalysis), École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
- Learning
& Adaptive Systems Group, Department of Computer Science, ETH Zurich, 8092 Zurich, 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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4
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Yang Y, Zhang S, Ranasinghe KD, Isayev O, Roitberg AE. Machine Learning of Reactive Potentials. Annu Rev Phys Chem 2024; 75:371-395. [PMID: 38941524 DOI: 10.1146/annurev-physchem-062123-024417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.
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Affiliation(s)
- Yinuo Yang
- Department of Chemistry, University of Florida, Gainesville, Florida;
| | - Shuhao Zhang
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania;
| | | | - Olexandr Isayev
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania;
| | - Adrian E Roitberg
- Department of Chemistry, University of Florida, Gainesville, Florida;
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5
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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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6
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Grochowska-Tatarczak M, Koteras K, Kazimierczuk K, Malinowski PJ. Hydrosilylation of Olefins Activated on Highly Lewis-Acidic Calcium Cation. Chemistry 2024:e202401322. [PMID: 38660917 DOI: 10.1002/chem.202401322] [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: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 04/26/2024]
Abstract
The report introduces simple yet highly reactive calcium salt, Ca[Al(ORF)4]2 (RF=C(CF3)3), 1, which effectively catalyses olefin hydrosilylation through an unusual mechanism involving the activation of the alkene molecule. Upon dissolution in o-difluorobenzene (oDFB), 1 forms a highly Lewis acidic [Ca(oDFB)6]2+ complex. Our DFT calculations reveal that fluoride ion affinity is comparable to SbF5. Reactivity tests show that it effectively catalyses the hydrosilylation of olefins with high regioselectivity, also in reactions involving sterically demanding substrates like (iPr)3SiH or tetrasubstituted olefins. Experimental and computational results point to the mechanism where the olefin molecule forms a complex with Ca2+, which significantly facilitates the attack of H-SiR3 on the C=C double bond.
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Affiliation(s)
| | - K Koteras
- Centre of New Technologies, University of Warsaw, Banacha 2c, 02-093, Warsaw, Poland
| | - K Kazimierczuk
- Centre of New Technologies, University of Warsaw, Banacha 2c, 02-093, Warsaw, Poland
| | - P J Malinowski
- Centre of New Technologies, University of Warsaw, Banacha 2c, 02-093, Warsaw, Poland
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7
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Chung Y, Green WH. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates. Chem Sci 2024; 15:2410-2424. [PMID: 38362410 PMCID: PMC10866337 DOI: 10.1039/d3sc05353a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 01/04/2024] [Indexed: 02/17/2024] Open
Abstract
Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28 000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔG‡solv, ΔΔH‡solv) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal mol-1 for ΔΔG‡solv and ΔΔH‡solv, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings.
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Affiliation(s)
- Yunsie Chung
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA
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8
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Hegedüsová L, Blaise N, Pašteka LF, Budzák Š, Medveď M, Filo J, Mravec B, Slavov C, Wachtveitl J, Grabarz AM, Cigáň M. Enhancing the Potential of Fused Heterocycle-Based Triarylhydrazone Photoswitches. Chemistry 2024; 30:e202303509. [PMID: 38212244 DOI: 10.1002/chem.202303509] [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: 10/24/2023] [Indexed: 01/13/2024]
Abstract
Triarylhydrazones represent an attractive class of photochromic compounds offering many interesting features including high molar absorptivity, good addressability, and extraordinary thermal stability. In addition, unlike most other hydrazone-based photoswitches, they effectively absorb light above 365 nm. However, previously prepared triaryhydrazones suffer from low quantum yields of the Z→E photoisomerization. Here, we have designed a new subclass of naphthoyl-benzothiazole hydrazones that balance the most beneficial features of previously reported naphthoyl-quinoline and benzoyl-pyridine triarylhydrazones. These preserve the attractive absorption characteristics, exhibit higher thermal stability of the metastable form than the former and enhance the rate of the Z→E photoisomerization compared to the later, as a result of the weakening of the intramolecular hydrogen bonding between the hydrazone hydrogen and the benzothiazole moiety. Introducing the benzothiazole motif extends the tunability of the photochromic behaviour of hydrazone-based switches.
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Affiliation(s)
- Lea Hegedüsová
- Department of Organic Chemistry, Faculty of Natural Sciences, Comenius University, Bratislava, 84215, Slovakia
| | - Nadine Blaise
- Institute of Physical and Theoretical Chemistry, Faculty of Biochemistry, Chemistry, Pharmacy, Goethe University, Frankfurt am Main, 60438, Germany
| | - Lukáš F Pašteka
- Van Swinderen Institute for Particle Physics and Gravity, University of Groningen, Groningen, 9747AG, The Netherlands
- Department of Physical and Theoretical Chemistry, Faculty of Natural Sciences, Comenius University, Bratislava, 84215, Slovakia
| | - Šimon Budzák
- Department of Chemistry, Faculty of Natural Sciences, Matej Bel University, Banská Bystrica, 97400, Slovakia
| | - Miroslav Medveď
- Department of Chemistry, Faculty of Natural Sciences, Matej Bel University, Banská Bystrica, 97400, Slovakia
- Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute, Palacký University Olomouc, Olomouc, 77900, Czechia
| | - Juraj Filo
- Department of Organic Chemistry, Faculty of Natural Sciences, Comenius University, Bratislava, 84215, Slovakia
| | - Bernard Mravec
- Department of Organic Chemistry, Faculty of Natural Sciences, Comenius University, Bratislava, 84215, Slovakia
| | - Chavdar Slavov
- Department of Chemistry, University of South Florida, Tampa, FL 33620, Florida, US
| | - Josef Wachtveitl
- Institute of Physical and Theoretical Chemistry, Faculty of Biochemistry, Chemistry, Pharmacy, Goethe University, Frankfurt am Main, 60438, Germany
| | - Anna M Grabarz
- Department of Physical and Theoretical Chemistry, Faculty of Natural Sciences, Comenius University, Bratislava, 84215, Slovakia
- Institute of Advanced Materials, Faculty of Chemistry, Wrocław University of Science and Technology, Wrocław, 50370, Poland
| | - Marek Cigáň
- Department of Organic Chemistry, Faculty of Natural Sciences, Comenius University, Bratislava, 84215, Slovakia
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9
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Bianchi P, Monbaliu JCM. Revisiting the Paradigm of Reaction Optimization in Flow with a Priori Computational Reaction Intelligence. Angew Chem Int Ed Engl 2023:e202311526. [PMID: 37875458 DOI: 10.1002/anie.202311526] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/21/2023] [Accepted: 10/24/2023] [Indexed: 10/26/2023]
Abstract
The use of micro/meso-fluidic reactors has resulted in both new scenarios for chemistry and new requirements for chemists. Through flow chemistry, large-scale reactions can be performed in drastically reduced reactor sizes and reaction times. This obvious advantage comes with the concomitant challenge of re-designing long-established batch processes to fit these new conditions. The reliance on experimental trial-and-error to perform this translation frequently makes flow chemistry unaffordable, thwarting initial aspirations to revolutionize chemistry. By combining computational chemistry and machine learning, we have developed a model that provides predictive power tailored specifically to flow reactions. We show its applications to translate batch to flow, to provide mechanistic insight, to contribute reagent descriptors, and to synthesize a library of novel compounds in excellent yields after executing a single set of conditions.
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Affiliation(s)
- Pauline Bianchi
- Center for Integrated Technology and Organic Synthesis (CiTOS), MolSys Research Unit, University of Liège, B6a, Room 3/19, Allée du Six Août 13, 4000, Liège (SartTilman), Belgium
| | - Jean-Christophe M Monbaliu
- Center for Integrated Technology and Organic Synthesis (CiTOS), MolSys Research Unit, University of Liège, B6a, Room 3/19, Allée du Six Août 13, 4000, Liège (SartTilman), Belgium
- WEL Research Institute, Avenue Pasteur 6, 1300, Wavre, Belgium
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10
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Kraka E, Antonio JJ, Freindorf M. Reaction mechanism - explored with the unified reaction valley approach. Chem Commun (Camb) 2023; 59:7151-7165. [PMID: 37233449 DOI: 10.1039/d3cc01576a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
One of the ultimate goals of chemistry is to understand and manipulate chemical reactions, which implies the ability to monitor the reaction and its underlying mechanism at an atomic scale. In this article, we introduce the Unified Reaction Valley Approach (URVA) as a tool for elucidating reaction mechanisms, complementing existing computational procedures. URVA combines the concept of the potential energy surface with vibrational spectroscopy and describes a chemical reaction via the reaction path and the surrounding reaction valley traced out by the reacting species on the potential energy surface on their way from the entrance to the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting species is registered by a change in the normal vibrational modes spanning the reaction valley and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction, with curvature minima reflecting minimal change and curvature maxima indicating the location of important chemical events such as bond breaking/formation, charge polarization and transfer, rehybridization, etc. A decomposition of the path curvature into internal coordinate components or other coordinates of relevance for the reaction under consideration, provides comprehensive insight into the origin of the chemical changes taking place. After giving an overview of current experimental and computational efforts to gain insight into the mechanism of a chemical reaction and presenting the theoretical background of URVA, we illustrate how URVA works for three diverse processes, (i) [1,3] hydrogen transfer reactions; (ii) α-keto-amino inhibitor for SARS-CoV-2 Mpro; (iii) Rh-catalyzed cyanation. We hope that this article will inspire our computational colleagues to add URVA to their repertoire and will serve as an incubator for new reaction mechanisms to be studied in collaboration with our experimental experts in the field.
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Affiliation(s)
- Elfi Kraka
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, TX 75275-0314, USA.
| | - Juliana J Antonio
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, TX 75275-0314, USA.
| | - Marek Freindorf
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, TX 75275-0314, USA.
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11
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Dana AG, Johnson MS, Allen JW, Sharma S, Raman S, Liu M, Gao CW, Grambow CA, Goldman MJ, Ranasinghe DS, Gillis RJ, Payne AM, Li Y, Dong X, Spiekermann KA, Wu H, Dames EE, Buras ZJ, Vandewiele NM, Yee NW, Merchant SS, Buesser B, Class CA, Goldsmith F, West RH, Green WH. Automated reaction kinetics and network exploration (Arkane): A statistical mechanics, thermodynamics, transition state theory, and master equation software. INT J CHEM KINET 2023. [DOI: 10.1002/kin.21637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Affiliation(s)
- Alon Grinberg Dana
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
- The Wolfson Department of Chemical Engineering and Grand Technion Energy Program (GTEP) Technion – Israel Institute of Technology Haifa Israel
| | - Matthew S. Johnson
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Joshua W. Allen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Sandeep Sharma
- Department of Chemistry University of Colorado Boulder CO USA
| | - Sumathy Raman
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Mengjie Liu
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Connie W. Gao
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Colin A. Grambow
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Mark J. Goldman
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Duminda S. Ranasinghe
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Ryan J. Gillis
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - A. Mark Payne
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Yi‐Pei Li
- Department of Chemical Engineering National Taiwan University Taipei Taiwan
| | - Xiaorui Dong
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Kevin A. Spiekermann
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Haoyang Wu
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Enoch E. Dames
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Zachary J. Buras
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Nick M. Vandewiele
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Nathan W. Yee
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Shamel S. Merchant
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Beat Buesser
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Caleb A. Class
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | | | - Richard H. West
- Department of Chemical Engineering Northeastern University Boston Massachusetts USA
| | - William H. Green
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
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12
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García-Andrade X, García Tahoces P, Pérez-Ríos J, Martínez Núñez E. Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations. J Phys Chem A 2023; 127:2274-2283. [PMID: 36877614 PMCID: PMC10845151 DOI: 10.1021/acs.jpca.2c08340] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/19/2023] [Indexed: 03/07/2023]
Abstract
Different machine learning (ML) models are proposed in the present work to predict density functional theory-quality barrier heights (BHs) from semiempirical quantum mechanical (SQM) calculations. The ML models include a multitask deep neural network, gradient-boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors are similar to those of previous models considering the same number of data points. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in combustion chemistry or in astrochemistry. Finally, our results show that 70% of the features with the highest impact on model output are bespoke predictors. This custom-made set of predictors could be employed by future Δ-ML models to improve the quantitative prediction of other reaction properties.
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Affiliation(s)
| | - Pablo García Tahoces
- Department
of Electronics and Computer Science, University
of Santiago de Compostela, Santiago de Compostela 15782, Spain
| | - Jesús Pérez-Ríos
- Department
of Physics, Stony Brook University, Stony Brook, New York 11794, United States
- Institute
for Advanced Computational Science, Stony
Brook University, Stony
Brook, New York 11794-3800, United States
| | - Emilio Martínez Núñez
- Department
of Physical Chemistry, University of Santiago
de Compostela, Santiago
de Compostela 15782, Spain
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13
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Chen Y, Ou Y, Zheng P, Huang Y, Ge F, Dral PO. Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights. J Chem Phys 2023; 158:074103. [PMID: 36813722 DOI: 10.1063/5.0137101] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) is a general-purpose method that was shown to achieve high accuracy for many applications with a speed close to its baseline semiempirical quantum mechanical (SQM) method ODM2*. Here, we evaluate the hitherto unknown performance of out-of-the-box AIQM1 without any refitting for reaction barrier heights on eight datasets, including a total of ∼24 thousand reactions. This evaluation shows that AIQM1's accuracy strongly depends on the type of transition state and ranges from excellent for rotation barriers to poor for, e.g., pericyclic reactions. AIQM1 clearly outperforms its baseline ODM2* method and, even more so, a popular universal potential, ANI-1ccx. Overall, however, AIQM1 accuracy largely remains similar to SQM methods (and B3LYP/6-31G* for most reaction types) suggesting that it is desirable to focus on improving AIQM1 performance for barrier heights in the future. We also show that the built-in uncertainty quantification helps in identifying confident predictions. The accuracy of confident AIQM1 predictions is approaching the level of popular density functional theory methods for most reaction types. Encouragingly, AIQM1 is rather robust for transition state optimizations, even for the type of reactions it struggles with the most. Single-point calculations with high-level methods on AIQM1-optimized geometries can be used to significantly improve barrier heights, which cannot be said for its baseline ODM2* method.
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Affiliation(s)
- Yuxinxin Chen
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yanchi Ou
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Peikun Zheng
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yaohuang Huang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Fuchun Ge
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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14
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Johnson MS, Dong X, Grinberg Dana A, Chung Y, Farina D, Gillis RJ, Liu M, Yee NW, Blondal K, Mazeau E, Grambow CA, Payne AM, Spiekermann KA, Pang HW, Goldsmith CF, West RH, Green WH. RMG Database for Chemical Property Prediction. J Chem Inf Model 2022; 62:4906-4915. [PMID: 36222558 DOI: 10.1021/acs.jcim.2c00965] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG database contains 45 libraries of thermochemical parameters with a combination of 4564 entries and a group additivity scheme with 9 types of corrections including radical, polycyclic, and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural network trained using transfer learning from a set of >130 000 DFT calculations to 10 000 high-quality values. Correction schemes for solvent-solute effects, important for thermochemistry in the liquid phase, are available. They include tabulated values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation, the database contains 92 libraries of kinetic parameters containing a combined 21 000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps to speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction, and by providing checks on kinetic parameters from other sources.
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Affiliation(s)
- Matthew S Johnson
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Xiaorui Dong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Alon Grinberg Dana
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States.,The Wolfson Department of Chemical Engineering, Grand Technion Energy Program (GTEP), Technion─Israel Institute of Technology, Haifa3200003, Israel
| | - Yunsie Chung
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - David Farina
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts02115, United States
| | - Ryan J Gillis
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Mengjie Liu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Nathan W Yee
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Katrin Blondal
- School of Engineering, Brown University, Providence, Rhode Island02912, United States
| | - Emily Mazeau
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts02115, United States
| | - Colin A Grambow
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - A Mark Payne
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Kevin A Spiekermann
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Hao-Wei Pang
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - C Franklin Goldsmith
- School of Engineering, Brown University, Providence, Rhode Island02912, United States
| | - Richard H West
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts02115, United States
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
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15
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Green WH. Concluding Remarks on Faraday Discussion on Unimolecular Reactions. Faraday Discuss 2022; 238:741-766. [DOI: 10.1039/d2fd00136e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
This Faraday Discussion, marking the centenary of Lindemann’s explanation of the pressure-dependence of unimolecular reactions, presented recent advances in measuring and computing collisional energy transfer efficiencies, microcanonical rate coefficients, pressure-dependent...
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