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. [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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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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3
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Zhao XG, Yang Q, Xu Y, Liu QY, Li ZY, Liu XX, Zhao YX, He SG. Machine Learning for Experimental Reactivity of a Set of Metal Clusters toward C-H Activation. J Am Chem Soc 2024; 146:12485-12495. [PMID: 38651836 DOI: 10.1021/jacs.4c00501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Understanding the mechanisms of C-H activation of alkanes is a very important research topic. The reactions of metal clusters with alkanes have been extensively studied to reveal the electronic features governing C-H activation, while the experimental cluster reactivity was qualitatively interpreted case by case in the literature. Herein, we prepared and mass-selected over 100 rhodium-based clusters (RhxVyOz- and RhxCoyOz-) to react with light alkanes, enabling the determination of reaction rate constants spanning six orders of magnitude. A satisfactory model being able to quantitatively describe the rate data in terms of multiple cluster electronic features (average electron occupancy of valence s orbitals, the minimum natural charge on the metal atom, cluster polarizability, and energy gap involved in the agostic interaction) has been constructed through a machine learning approach. This study demonstrates that the general mechanisms governing the very important process of C-H activation by diverse metal centers can be discovered by interpreting experimental data with artificial intelligence.
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Affiliation(s)
- Xi-Guan Zhao
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Qi Yang
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Ying Xu
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Qing-Yu Liu
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Zi-Yu Li
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Xiao-Xiao Liu
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Yan-Xia Zhao
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Sheng-Gui He
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
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4
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Xu W, Zhao Y, Chen J, Wan Z, Yan D, Zhang X, Zhang R. A Q-learning method based on coarse-to-fine potential energy surface for locating transition state and reaction pathway. J Comput Chem 2024; 45:487-497. [PMID: 37966714 DOI: 10.1002/jcc.27259] [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: 07/07/2023] [Revised: 10/25/2023] [Accepted: 11/02/2023] [Indexed: 11/16/2023]
Abstract
Transition state (TS) on the potential energy surface (PES) plays a key role in determining the kinetics and thermodynamics of chemical reactions. Inspired by the fact that the dynamics of complex systems are always driven by rare but significant transition events, we herein propose a TS search method in accordance with the Q-learning algorithm. Appropriate reward functions are set for a given PES to optimize the reaction pathway through continuous trial and error, and then the TS can be obtained from the optimized reaction pathway. The validity of this Q-learning method with reasonable settings of Q-value table including actions, states, learning rate, greedy rate, discount rate, and so on, is exemplified in 2 two-dimensional potential functions. In the applications of the Q-learning method to two chemical reactions, it is demonstrated that the Q-learning method can predict consistent TS and reaction pathway with those by ab initio calculations. Notably, the PES must be well prepared before using the Q-learning method, and a coarse-to-fine PES scanning scheme is thus introduced to save the computational time while maintaining the accuracy of the Q-learning prediction. This work offers a simple and reliable Q-learning method to search for all possible TS and reaction pathway of a chemical reaction, which may be a new option for effectively exploring the PES in an extensive search manner.
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Affiliation(s)
- Wenjun Xu
- Department of Physics, City University of Hong Kong, Hong Kong SAR, China
| | - Yanling Zhao
- Department of Physics, City University of Hong Kong, Hong Kong SAR, China
| | - Jialu Chen
- Department of Physics, City University of Hong Kong, Hong Kong SAR, China
| | - Zhongyu Wan
- Department of Physics, City University of Hong Kong, Hong Kong SAR, China
| | - Dadong Yan
- Department of Physics, Beijing Normal University, Beijing, China
| | - Xinghua Zhang
- School of Science, Beijing Jiaotong University, Beijing, China
| | - Ruiqin Zhang
- Department of Physics, City University of Hong Kong, Hong Kong SAR, China
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5
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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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6
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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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7
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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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8
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Liu Q, Tang K, Zhang L, Du J, Meng Q. Computer‐assisted synthetic planning considering reaction kinetics based on transition state automated generation method. AIChE J 2023. [DOI: 10.1002/aic.18092] [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)
- Qilei Liu
- State Key Laboratory of Fine Chemical, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering Dalian University of Technology Dalian 116024 China
| | - Kun Tang
- State Key Laboratory of Fine Chemical, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering Dalian University of Technology Dalian 116024 China
| | - Lei Zhang
- State Key Laboratory of Fine Chemical, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering Dalian University of Technology Dalian 116024 China
| | - Jian Du
- State Key Laboratory of Fine Chemical, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering Dalian University of Technology Dalian 116024 China
| | - Qingwei Meng
- State Key Laboratory of Fine Chemical, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering Dalian University of Technology Dalian 116024 China
- Ningbo Research Institute Dalian University of Technology Ningbo 315016 China
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9
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Zhao Q, Vaddadi SM, Woulfe M, Ogunfowora LA, Garimella SS, Isayev O, Savoie BM. Comprehensive exploration of graphically defined reaction spaces. Sci Data 2023; 10:145. [PMID: 36935430 PMCID: PMC10025260 DOI: 10.1038/s41597-023-02043-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 02/27/2023] [Indexed: 03/21/2023] Open
Abstract
Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties.
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Affiliation(s)
- Qiyuan Zhao
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA
| | - Sai Mahit Vaddadi
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA
| | - Michael Woulfe
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA
| | - Lawal A Ogunfowora
- Department of Chemistry, Purdue University, West Lafayette, IN, 47906, USA
| | - Sanjay S Garimella
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, 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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10
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Choi S. Prediction of transition state structures of gas-phase chemical reactions via machine learning. Nat Commun 2023; 14:1168. [PMID: 36859495 PMCID: PMC9977841 DOI: 10.1038/s41467-023-36823-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 02/15/2023] [Indexed: 03/03/2023] Open
Abstract
The elucidation of transition state (TS) structures is essential for understanding the mechanisms of chemical reactions and exploring reaction networks. Despite significant advances in computational approaches, TS searching remains a challenging problem owing to the difficulty of constructing an initial structure and heavy computational costs. In this paper, a machine learning (ML) model for predicting the TS structures of general organic reactions is proposed. The proposed model derives the interatomic distances of a TS structure from atomic pair features reflecting reactant, product, and linearly interpolated structures. The model exhibits excellent accuracy, particularly for atomic pairs in which bond formation or breakage occurs. The predicted TS structures yield a high success ratio (93.8%) for quantum chemical saddle point optimizations, and 88.8% of the optimization results have energy errors of less than 0.1 kcal mol-1. Additionally, as a proof of concept, the exploration of multiple reaction paths of an organic reaction is demonstrated based on ML inferences. I envision that the proposed approach will aid in the construction of initial geometries for TS optimization and reaction path exploration.
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Affiliation(s)
- Sunghwan Choi
- Division of National Supercomputing, Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea.
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11
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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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12
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Tsou PK, Huynh HT, Phan HT, Kuo JL. A self-adapting first-principles exploration on the dissociation mechanism in sodiated aldohexose pyranoses assisted with neural network potentials. Phys Chem Chem Phys 2023; 25:3332-3342. [PMID: 36633012 DOI: 10.1039/d2cp04421h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Understanding the mechanism of collision-induced dissociation (CID) in mono-saccharides with density functional theory (DFT) is challenging because of many possible reaction paths that originate from their high structural diversity. To search for the transition state (TS) from the huge number of conformers, we propose a three-step search scheme with the assistance of neural network potential (NNP). The search starts from a cross-checking of sugars, to a global search of all possible channels, and in the end, an exhaustive exploration around the low-lying channels. The cross-checking step quickly adapts the NNP from the studied molecules to the target ones. The other two steps utilize the adapted NNP to find the available pathways via random sampling of the structures. The study of the CID reactions in all eight types of aldohexose pyranoses was applied using the search scheme. The DFT calculations on AH-0 (Glc, Gal, and Man) in the previous study were utilized to construct an NNP and provide the TS structure database for searching AH-1 (All, Alt, Gul, Ido, and Tal). In total, we identified around 5200 TSs in AH-0 and AH-1, and the final NNP covers an energy range of more than 500 kJ mol-1 with a mean absolute error of energy less than 4 kJ mol-1. The search scheme is useful not only for saccharides but also for highly flexible bio-molecules.
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Affiliation(s)
- Pei-Kang Tsou
- Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei, 10617, Taiwan.
| | - Hai Thi Huynh
- Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei, 10617, Taiwan. .,Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 11529, Taiwan.,Department of Chemistry, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Huu Trong Phan
- Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei, 10617, Taiwan. .,Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 11529, Taiwan.,Department of Chemistry, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Jer-Lai Kuo
- Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei, 10617, Taiwan. .,Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 11529, Taiwan.,Department of Chemistry, National Tsing Hua University, Hsinchu 30013, Taiwan.,International Graduate Program of Molecular Science and Technology (NTU-MST), National Taiwan University, Taipei 10617, Taiwan
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13
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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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14
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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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15
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Reiser P, Neubert M, Eberhard A, Torresi L, Zhou C, Shao C, Metni H, van Hoesel C, Schopmans H, Sommer T, Friederich P. Graph neural networks for materials science and chemistry. COMMUNICATIONS MATERIALS 2022; 3:93. [PMID: 36468086 PMCID: PMC9702700 DOI: 10.1038/s43246-022-00315-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 11/07/2022] [Indexed: 05/14/2023]
Abstract
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.
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Affiliation(s)
- Patrick Reiser
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Marlen Neubert
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - André Eberhard
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - Luca Torresi
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - Chen Zhou
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - Chen Shao
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Present Address: Institute for Applied Informatics and Formal Description Systems, Karlsruhe Institute of Technology, Kaiserstr. 89, 76133 Karlsruhe, Germany
| | - Houssam Metni
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- ECPM, Université de Strasbourg, 25 Rue Becquerel, 67087 Strasbourg, France
| | - Clint van Hoesel
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Department of Applied Physics, Eindhoven University of Technology, Groene Loper 19, 5612 AP Eindhoven, The Netherlands
| | - Henrik Schopmans
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Timo Sommer
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute for Theory of Condensed Matter, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131 Karlsruhe, Germany
- Present Address: School of Chemistry, Trinity College Dublin, College Green, Dublin 2, Ireland
| | - Pascal Friederich
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
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16
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Spiekermann K, Pattanaik L, Green WH. High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions. Sci Data 2022; 9:417. [PMID: 35851390 PMCID: PMC9293986 DOI: 10.1038/s41597-022-01529-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 06/30/2022] [Indexed: 12/13/2022] Open
Abstract
Quantitative chemical reaction data, including activation energies and reaction rates, are crucial for developing detailed kinetic mechanisms and accurately predicting reaction outcomes. However, such data are often difficult to find, and high-quality datasets are especially rare. Here, we use CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP to obtain high-quality single point calculations for nearly 22,000 unique stable species and transition states. We report the results from these quantum chemistry calculations and extract the barrier heights and reaction enthalpies to create a kinetics dataset of nearly 12,000 gas-phase reactions. These reactions involve H, C, N, and O, contain up to seven heavy atoms, and have cleaned atom-mapped SMILES. Our higher-accuracy coupled-cluster barrier heights differ significantly (RMSE of ∼5 kcal mol−1) relative to those calculated at ωB97X-D3/def2-TZVP. We also report accurate transition state theory rate coefficients \documentclass[12pt]{minimal}
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\begin{document}$${k}_{\infty }(T)$$\end{document}k∞(T) between 300 K and 2000 K and the corresponding Arrhenius parameters for a subset of rigid reactions. We believe this data will accelerate development of automated and reliable methods for quantitative reaction prediction. Measurement(s) | Barrier Heights • Enthalpies • Rate Coefficients | Technology Type(s) | ab initio quantum chemistry computational method |
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Affiliation(s)
- Kevin Spiekermann
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Lagnajit Pattanaik
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA.
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17
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Park S, Han H, Kim H, Choi S. Machine Learning Applications for Chemical Reactions. Chem Asian J 2022; 17:e202200203. [PMID: 35471772 PMCID: PMC9401034 DOI: 10.1002/asia.202200203] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/26/2022] [Indexed: 11/30/2022]
Abstract
Machine learning (ML) approaches have enabled rapid and efficient molecular property predictions as well as the design of new novel materials. In addition to great success for molecular problems, ML techniques are applied to various chemical reaction problems that require huge costs to solve with the existing experimental and simulation methods. In this review, starting with basic representations of chemical reactions, we summarized recent achievements of ML studies on two different problems; predicting reaction properties and synthetic routes. The various ML models are used to predict physical properties related to chemical reaction properties (e. g. thermodynamic changes, activation barriers, and reaction rates). Furthermore, the predictions of reactivity, self-optimization of reaction, and designing retrosynthetic reaction paths are also tackled by ML approaches. Herein we illustrate various ML strategies utilized in the various context of chemical reaction studies.
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Affiliation(s)
- Sanggil Park
- Department of ChemistryIncheon Natoinal University and Research Institute of Basic SciencesIncheon22012Republic of Korea
| | - Herim Han
- Digital Bio R&D CenterMediazenSeoul07789Republic of Korea
- Department of Polymer Science and EngineeringDankook UniversityYongin, Gyeonggi16890Republic of Korea
| | - Hyungjun Kim
- Department of ChemistryIncheon Natoinal University and Research Institute of Basic SciencesIncheon22012Republic of Korea
| | - Sunghwan Choi
- Division of National SupercomputingKorea Institute of Science and Technology InformationDaejeon34141Republic of Korea
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18
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Komp E, Valleau S. Low-cost prediction of molecular and transition state partition functions via machine learning. Chem Sci 2022; 13:7900-7906. [PMID: 35865893 PMCID: PMC9258343 DOI: 10.1039/d2sc01334g] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 06/10/2022] [Indexed: 11/21/2022] Open
Abstract
We have generated an open-source dataset of over 30 000 organic chemistry gas phase partition functions. With this data, a machine learning deep neural network estimator was trained to predict partition functions of unknown organic chemistry gas phase transition states. This estimator only relies on reactant and product geometries and partition functions. A second machine learning deep neural network was trained to predict partition functions of chemical species from their geometry. Our models accurately predict the logarithm of test set partition functions with a maximum mean absolute error of 2.7%. Thus, this approach provides a means to reduce the cost of computing reaction rate constants ab initio. The models were also used to compute transition state theory reaction rate constant prefactors and the results were in quantitative agreement with the corresponding ab initio calculations with an accuracy of 98.3% on the log scale.
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Affiliation(s)
- Evan Komp
- Chemical Engineering, University of Washington 3781 Okanogan Ln Seattle WA 98195 USA
| | - Stéphanie Valleau
- Chemical Engineering, University of Washington 3781 Okanogan Ln Seattle WA 98195 USA
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19
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Spiekermann KA, Pattanaik L, Green WH. Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy. J Phys Chem A 2022; 126:3976-3986. [PMID: 35727075 DOI: 10.1021/acs.jpca.2c02614] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Quantitative estimates of reaction barriers are essential for developing kinetic mechanisms and predicting reaction outcomes. However, the lack of experimental data and the steep scaling of accurate quantum calculations often hinder the ability to obtain reliable kinetic values. Here, we train a directed message passing neural network on nearly 24,000 diverse gas-phase reactions calculated at CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP. Our model uses 75% fewer parameters than previous studies, an improved reaction representation, and proper data splits to accurately estimate performance on unseen reactions. Using information from only the reactant and product, our model quickly predicts barrier heights with a testing MAE of 2.6 kcal mol-1 relative to the coupled-cluster data, making it more accurate than a good density functional theory calculation. Furthermore, our results show that future modeling efforts to estimate reaction properties would significantly benefit from fine-tuning calibration using a transfer learning technique. We anticipate this model will accelerate and improve kinetic predictions for small molecule chemistry.
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Affiliation(s)
- Kevin A Spiekermann
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Lagnajit Pattanaik
- 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
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20
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Lustosa DM, Milo A. Mechanistic Inference from Statistical Models at Different Data-Size Regimes. ACS Catal 2022. [DOI: 10.1021/acscatal.2c01741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Danilo M. Lustosa
- Department of Chemistry, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
| | - Anat Milo
- Department of Chemistry, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
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21
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Isamura BK, Lobb KA. AMADAR: a python-based package for large scale prediction of Diels-Alder transition state geometries and IRC path analysis. J Cheminform 2022; 14:39. [PMID: 35706060 PMCID: PMC9202188 DOI: 10.1186/s13321-022-00618-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 05/25/2022] [Indexed: 12/28/2022] Open
Abstract
Predicting transition state geometries is one of the most challenging tasks in computational chemistry, which often requires expert-based knowledge and permanent human intervention. This short communication reports technical details and preliminary results of a python-based tool (AMADAR) designed to generate any Diels–Alder (DA) transition state geometry (TS) and analyze determined IRC paths in a (quasi-)automated fashion, given the product SMILES. Two modules of the package are devoted to performing, from IRC paths, reaction force analyses (RFA) and atomic (fragment) decompositions of the reaction force F and reaction force constant \documentclass[12pt]{minimal}
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\begin{document}$$\kappa$$\end{document}κ. The performance of the protocol has been assessed using a dataset of 2000 DA cycloadducts retrieved from the ZINC database. The sequential location of the corresponding TSs was achieved with a success rate of 95%. RFA plots confirmed the reaction force constant \documentclass[12pt]{minimal}
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\begin{document}$$\kappa$$\end{document}κ to be a good indicator of the (non)synchronicity of the associated DA reactions. Moreover, the atomic decomposition of \documentclass[12pt]{minimal}
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\begin{document}$$\kappa$$\end{document}κ allows for the rationalization of the (a)synchronicity of each DA reaction in terms of contributions stemming from pairs of interacting atoms. The source code of the AMADAR tool is available on GitHub [CMCDD/AMADAR(github.com)] and can be used directly with minor customizations, mostly regarding the local working environment of the user.
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Affiliation(s)
- Bienfait K Isamura
- Department of Chemistry, Rhodes University, Makhanda, 6140, South Africa
| | - Kevin A Lobb
- Department of Chemistry, Rhodes University, Makhanda, 6140, South Africa. .,Research Unit in BioInformatics (RUBi), Rhodes University, Makhanda, 6140, South Africa.
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22
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Wu H, Grinberg Dana A, Ranasinghe DS, Pickard FC, Wood GPF, Zelesky T, Sluggett GW, Mustakis J, Green WH. Kinetic Modeling of API Oxidation: (2) Imipramine Stress Testing. Mol Pharm 2022; 19:1526-1539. [DOI: 10.1021/acs.molpharmaceut.2c00043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Haoyang Wu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Alon Grinberg Dana
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Wolfson Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa 3200003, Israel
| | - Duminda S. Ranasinghe
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Frank C. Pickard
- Pfizer Global Research and Development, Groton Laboratories, Eastern Point Road, Groton, Connecticut 06340, United States
| | - Geoffrey P. F. Wood
- Pfizer Global Research and Development, Groton Laboratories, Eastern Point Road, Groton, Connecticut 06340, United States
| | - Todd Zelesky
- Pfizer Global Research and Development, Groton Laboratories, Eastern Point Road, Groton, Connecticut 06340, United States
| | - Gregory W. Sluggett
- Pfizer Global Research and Development, Groton Laboratories, Eastern Point Road, Groton, Connecticut 06340, United States
| | - Jason Mustakis
- Pfizer Global Research and Development, Groton Laboratories, Eastern Point Road, Groton, Connecticut 06340, United States
| | - William H. Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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23
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Baradyn M, Ratkiewicz A. On-The-Fly Kinetics of the Hydrogen Abstraction by Hydroperoxyl Radical: An Application of the Reaction Class Transition State Theory. Front Chem 2022; 9:806873. [PMID: 35174142 PMCID: PMC8841336 DOI: 10.3389/fchem.2021.806873] [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] [Received: 11/01/2021] [Accepted: 12/22/2021] [Indexed: 11/23/2022] Open
Abstract
A Reaction Class Transition State Theory (RC-TST) is applied to calculate thermal rate constants for hydrogen abstraction by OOH radical from alkanes in the temperature range of 300–2500 K. The rate constants for the reference reaction C2H6 + ∙OOH → ∙C2H5 + H2O2, is obtained with the Canonical Variational Transition State Theory (CVT) augmented with the Small Curvature Tunneling (SCT) correction. The necessary parameters were obtained from M06-2X/aug-cc-pVTZ data for a training set of 24 reactions. Depending on the approximation employed, only the reaction energy or no additional parameters are needed to predict the RC-TST rates for other class representatives. Although each of the reactions can in principle be investigated at higher levels of theory, the approach provides a nearly equally reliable rate constant at a fraction of the cost needed for larger and higher level calculations. The systematic error is smaller than 50% in comparison with high level computations. Satisfactory agreement with literature data, augmented by the lack of necessity of tedious and time consuming transition state calculations, facilitated the seamless application of the proposed methodology to the Automated Reaction Mechanism Generators (ARMGs) programs.
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24
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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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25
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Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States. Top Catal 2021. [DOI: 10.1007/s11244-021-01506-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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26
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Makoś MZ, Verma N, Larson EC, Freindorf M, Kraka E. Generative adversarial networks for transition state geometry prediction. J Chem Phys 2021; 155:024116. [PMID: 34266275 DOI: 10.1063/5.0055094] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
This work introduces a novel application of generative adversarial networks (GANs) for the prediction of starting geometries in transition state (TS) searches based on the geometries of reactants and products. The multi-dimensional potential energy space of a chemical reaction often complicates the location of a starting TS geometry, leading to the correct TS combining reactants and products in question. The proposed TS-GAN efficiently maps the space between reactants and products and generates reliable TS guess geometries, and it can be easily combined with any quantum chemical software package performing geometry optimizations. The TS-GAN was trained and applied to generate TS guess structures for typical chemical reactions, such as hydrogen migration, isomerization, and transition metal-catalyzed reactions. The performance of the TS-GAN was directly compared to that of classical approaches, proving its high accuracy and efficiency. The current TS-GAN can be extended to any dataset that contains sufficient chemical reactions for training. The software is freely available for training, experimentation, and prediction at https://github.com/ekraka/TS-GAN.
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Affiliation(s)
- Małgorzata Z Makoś
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA
| | - Niraj Verma
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA
| | - Eric C Larson
- Computer Science Department, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA
| | - Marek Freindorf
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA
| | - Elfi Kraka
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA
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27
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Jackson R, Zhang W, Pearson J. TSNet: predicting transition state structures with tensor field networks and transfer learning. Chem Sci 2021; 12:10022-10040. [PMID: 34377396 PMCID: PMC8317659 DOI: 10.1039/d1sc01206a] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/21/2021] [Indexed: 12/14/2022] Open
Abstract
Transition states are among the most important molecular structures in chemistry, critical to a variety of fields such as reaction kinetics, catalyst design, and the study of protein function. However, transition states are very unstable, typically only existing on the order of femtoseconds. The transient nature of these structures makes them incredibly difficult to study, thus chemists often turn to simulation. Unfortunately, computer simulation of transition states is also challenging, as they are first-order saddle points on highly dimensional mathematical surfaces. Locating these points is resource intensive and unreliable, resulting in methods which can take very long to converge. Machine learning, a relatively novel class of algorithm, has led to radical changes in several fields of computation, including computer vision and natural language processing due to its aptitude for highly accurate function approximation. While machine learning has been widely adopted throughout computational chemistry as a lightweight alternative to costly quantum mechanical calculations, little research has been pursued which utilizes machine learning for transition state structure optimization. In this paper TSNet is presented, a new end-to-end Siamese message-passing neural network based on tensor field networks shown to be capable of predicting transition state geometries. Also presented is a small dataset of SN2 reactions which includes transition state structures - the first of its kind built specifically for machine learning. Finally, transfer learning, a low data remedial technique, is explored to understand the viability of pretraining TSNet on widely available chemical data may provide better starting points during training, faster convergence, and lower loss values. Aspects of the new dataset and model shall be discussed in detail, along with motivations and general outlook on the future of machine learning-based transition state prediction.
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Affiliation(s)
- Riley Jackson
- Department of Chemistry, University of Prince Edward Island Canada
| | - Wenyuan Zhang
- Department of Chemistry, University of Prince Edward Island Canada
| | - Jason Pearson
- Department of Chemistry, University of Prince Edward Island Canada
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Abstract
As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until 'big data' applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barriers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions. Calculations with a quantum-mechanical component are still preferred for quantitative predictions of reactivity. Although big data applications tend to be more qualitative, they have the advantage to be broadly applied to different kinds of reactions. There is a continuum of methods in between these extremes, such as methods that use quantum-derived data or descriptors in machine learning models. Here, we present an overview of the recent machine learning applications in the field of chemical reactivity from a mechanistic perspective. Starting with a summary of how reactivity questions are addressed by quantum-mechanical methods, we discuss methods that augment or replace quantum-based modelling with faster alternatives relying on machine learning.
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29
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Vassilev-Galindo V, Fonseca G, Poltavsky I, Tkatchenko A. Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules. J Chem Phys 2021; 154:094119. [DOI: 10.1063/5.0038516] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Valentin Vassilev-Galindo
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Gregory Fonseca
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Igor Poltavsky
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
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30
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Jorner K, Brinck T, Norrby PO, Buttar D. Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies. Chem Sci 2021; 12:1163-1175. [PMID: 36299676 PMCID: PMC9528810 DOI: 10.1039/d0sc04896h] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 11/02/2020] [Indexed: 12/19/2022] Open
Abstract
Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers. We train a Gaussian Process Regression model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic substitution reaction and use it to predict barriers with a mean absolute error of 0.77 kcal mol−1 for an external test set. The model was further validated on regio- and chemoselectivity prediction on patent reaction data and achieved a competitive top-1 accuracy of 86%, despite not being trained explicitly for this task. Importantly, the model gives error bars for its predictions that can be used for risk assessment by the end user. Hybrid models emerge as the preferred alternative for accurate reaction prediction in the very common low-data situation where only 100–150 rate constants are available for a reaction class. With recent advances in deep learning for quickly predicting barriers and transition state geometries from density functional theory, we envision that hybrid models will soon become a standard alternative to complement current machine learning approaches based on ground-state physical organic descriptors or structural information such as molecular graphs or fingerprints. Hybrid reactivity models, combining mechanistic calculations and machine learning with descriptors, are used to predict barriers for nucleophilic aromatic substitution.![]()
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Affiliation(s)
- Kjell Jorner
- Early Chemical Development
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Macclesfield
| | - Tore Brinck
- Applied Physical Chemistry
- Department of Chemistry
- CBH
- KTH Royal Institute of Technology
- Stockholm
| | - Per-Ola Norrby
- Data Science & Modelling
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Gothenburg
| | - David Buttar
- Early Chemical Development
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Macclesfield
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