1
|
Piskor T, Pinski P, Mast T, Rybkin V. Multi-Level Protocol for Mechanistic Reaction Studies Using Semi-Local Fitted Potential Energy Surfaces. Int J Mol Sci 2024; 25:8530. [PMID: 39126098 PMCID: PMC11312657 DOI: 10.3390/ijms25158530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 07/18/2024] [Accepted: 07/31/2024] [Indexed: 08/12/2024] Open
Abstract
In this work, we propose a multi-level protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are fitted with a machine learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive potential energy surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (SN2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here complete-active-space self-consistent field, CASSCF, and coupled-cluster singles and doubles, CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction coordinates and barriers. Furthermore, we find a qualitative agreement in vibrational frequencies and reaction rate coefficients. The key aspect of the method's performance is its multi-level nature, which not only saves computational effort but also allows extracting meaningful information along the reaction path, characterized by zero gradients in all but one direction. Agnostic to the nature of the TS and computationally economic, the protocol can be readily automated and routinely used for mechanistic reaction studies.
Collapse
Affiliation(s)
- Tomislav Piskor
- HQS Quantum Simulations GmbH, Rintheimer Straße 23, 76131 Karlsruhe, Germany
- Theoretical Physics, Saarland University, 66123 Saarbrücken, Germany
| | - Peter Pinski
- HQS Quantum Simulations GmbH, Rintheimer Straße 23, 76131 Karlsruhe, Germany
| | - Thilo Mast
- HQS Quantum Simulations GmbH, Rintheimer Straße 23, 76131 Karlsruhe, Germany
| | - Vladimir Rybkin
- HQS Quantum Simulations GmbH, Rintheimer Straße 23, 76131 Karlsruhe, Germany
| |
Collapse
|
2
|
Laplaza R, Wodrich MD, Corminboeuf C. Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States. J Phys Chem Lett 2024; 15:7363-7370. [PMID: 38990895 DOI: 10.1021/acs.jpclett.4c01657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
The prediction of reaction selectivity is a challenging task for computational chemistry, not only because many molecules adopt multiple conformations but also due to the exponential relationship between effective activation energies and rate constants. To account for molecular flexibility, an increasing number of methods exist that generate conformational ensembles of transition state (TS) structures. Typically, these TS ensembles are Boltzmann weighted and used to compute selectivity assuming Curtin-Hammett conditions. This strategy, however, can lead to erroneous predictions if the appropriate filtering of the conformer ensembles is not conducted. Here, we demonstrate how any possible selectivity can be obtained by processing the same sets of TS ensembles for a model reaction. To address the burdensome filtering task in a consistent and automated way, we introduce marc, a tool for the modular analysis of representative conformers that aids in avoiding human errors while minimizing the number of reoptimization computations needed to obtain correct reaction selectivity.
Collapse
Affiliation(s)
- Ruben Laplaza
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Matthew D Wodrich
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), 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 (EPFL), 1015 Lausanne, Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| |
Collapse
|
3
|
Zhang H, Juraskova V, Duarte F. Modelling chemical processes in explicit solvents with machine learning potentials. Nat Commun 2024; 15:6114. [PMID: 39030199 PMCID: PMC11271496 DOI: 10.1038/s41467-024-50418-6] [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: 08/06/2023] [Accepted: 07/08/2024] [Indexed: 07/21/2024] Open
Abstract
Solvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We apply this strategy to investigate a Diels-Alder reaction in water and methanol. The generated machine learning potentials enable us to obtain reaction rates that are in agreement with experimental data and analyse the influence of these solvents on the reaction mechanism. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner.
Collapse
Affiliation(s)
- Hanwen Zhang
- Chemistry Research Laboratory, Oxford, United Kingdom
| | | | | |
Collapse
|
4
|
Benayad Z, David R, Stirnemann G. Prebiotic chemical reactivity in solution with quantum accuracy and microsecond sampling using neural network potentials. Proc Natl Acad Sci U S A 2024; 121:e2322040121. [PMID: 38809704 PMCID: PMC11161780 DOI: 10.1073/pnas.2322040121] [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: 12/14/2023] [Accepted: 04/26/2024] [Indexed: 05/31/2024] Open
Abstract
While RNA appears as a good candidate for the first autocatalytic systems preceding the emergence of modern life, the synthesis of RNA oligonucleotides without enzymes remains challenging. Because the uncatalyzed reaction is extremely slow, experimental studies bring limited and indirect information on the reaction mechanism, the nature of which remains debated. Here, we develop neural network potentials (NNPs) to study the phosphoester bond formation in water. While NNPs are becoming routinely applied to nonreactive systems or simple reactions, we demonstrate how they can systematically be trained to explore the reaction phase space for complex reactions involving several proton transfers and exchanges of heavy atoms. We then propagate at moderate computational cost hundreds of nanoseconds of a variety of enhanced sampling simulations with quantum accuracy in explicit solvent conditions. The thermodynamically preferred reaction pathway is a concerted, dissociative mechanism, with the transient formation of a metaphosphate transition state and direct participation of water solvent molecules that facilitate the exchange of protons through the nonbridging phosphate oxygens. Associative-dissociative pathways, characterized by a much tighter pentacoordinated phosphate, are higher in free energy. Our simulations also suggest that diprotonated phosphate, whose reactivity is never directly assessed in the experiments, is significantly less reactive than the monoprotonated species, suggesting that it is probably never the reactive species in normal pH conditions. These observations rationalize unexplained experimental results and the temperature dependence of the reaction rate, and they pave the way for the design of more efficient abiotic catalysts and activating groups.
Collapse
Affiliation(s)
- Zakarya Benayad
- CNRS Laboratoire de Biochimie Théorique, Institut de Biologie Physico-Chimique, Paris Sciences et Lettres University, Université Paris-Cité, 75005Paris, France
- PASTEUR, Département de Chimie, École Normale Supérieure, Paris Sciences et Lettres University, Sorbonne University, CNRS, 75005Paris, France
| | - Rolf David
- CNRS Laboratoire de Biochimie Théorique, Institut de Biologie Physico-Chimique, Paris Sciences et Lettres University, Université Paris-Cité, 75005Paris, France
- PASTEUR, Département de Chimie, École Normale Supérieure, Paris Sciences et Lettres University, Sorbonne University, CNRS, 75005Paris, France
| | - Guillaume Stirnemann
- CNRS Laboratoire de Biochimie Théorique, Institut de Biologie Physico-Chimique, Paris Sciences et Lettres University, Université Paris-Cité, 75005Paris, France
- PASTEUR, Département de Chimie, École Normale Supérieure, Paris Sciences et Lettres University, Sorbonne University, CNRS, 75005Paris, France
| |
Collapse
|
5
|
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.
Collapse
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;
| |
Collapse
|
6
|
Zhang S, Makoś MZ, Jadrich RB, Kraka E, Barros K, Nebgen BT, Tretiak S, Isayev O, Lubbers N, Messerly RA, Smith JS. Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential. Nat Chem 2024; 16:727-734. [PMID: 38454071 PMCID: PMC11087274 DOI: 10.1038/s41557-023-01427-3] [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: 04/13/2023] [Accepted: 12/12/2023] [Indexed: 03/09/2024]
Abstract
Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation.
Collapse
Affiliation(s)
- Shuhao Zhang
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Małgorzata Z Makoś
- Computational and Theoretical Chemistry Group, Department of Chemistry, Southern Methodist University, Dallas, TX, USA
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Ryan B Jadrich
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Elfi Kraka
- Computational and Theoretical Chemistry Group, Department of Chemistry, Southern Methodist University, Dallas, TX, USA
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Benjamin T Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Olexandr Isayev
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Richard A Messerly
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Justin S Smith
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
- NVIDIA Corp., Santa Clara, CA, USA.
| |
Collapse
|
7
|
Bonati L, Polino D, Pizzolitto C, Biasi P, Eckert R, Reitmeier S, Schlögl R, Parrinello M. The role of dynamics in heterogeneous catalysis: Surface diffusivity and N 2 decomposition on Fe(111). Proc Natl Acad Sci U S A 2023; 120:e2313023120. [PMID: 38060558 PMCID: PMC10723053 DOI: 10.1073/pnas.2313023120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 10/18/2023] [Indexed: 12/17/2023] Open
Abstract
Dynamics has long been recognized to play an important role in heterogeneous catalytic processes. However, until recently, it has been impossible to study their dynamical behavior at industry-relevant temperatures. Using a combination of machine learning potentials and advanced simulation techniques, we investigate the cleavage of the N[Formula: see text] triple bond on the Fe(111) surface. We find that at low temperatures our results agree with the well-established picture. However, if we increase the temperature to reach operando conditions, the surface undergoes a global dynamical change and the step structure of the Fe(111) surface is destabilized. The catalytic sites, traditionally associated with this surface, appear and disappear continuously. Our simulations illuminate the danger of extrapolating low-temperature results to operando conditions and indicate that the catalytic activity can only be inferred from calculations that take dynamics fully into account. More than that, they show that it is the transition to this highly fluctuating interfacial environment that drives the catalytic process.
Collapse
Affiliation(s)
- Luigi Bonati
- Atomistic Simulations, Italian Institute of Technology, Genova16152, Italy
| | - Daniela Polino
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano6962, Switzerland
| | - Cristina Pizzolitto
- Basic Research, Research and Development Division, Casale SA, Lugano6900, Switzerland
| | - Pierdomenico Biasi
- Basic Research, Research and Development Division, Casale SA, Lugano6900, Switzerland
| | - Rene Eckert
- BU Catalysts, R&D Syngas Applications, Clariant Produkte (Deutschland) GmbH, Munich83052, Germany
| | - Stephan Reitmeier
- BU Catalysts, R&D Syngas Applications, Clariant Produkte (Deutschland) GmbH, Munich83052, Germany
| | - Robert Schlögl
- Department of Inorganic Chemistry, Fritz-Haber Institute of the Max-Planck-Society, Berlin14195, Germany
| | - Michele Parrinello
- Atomistic Simulations, Italian Institute of Technology, Genova16152, Italy
| |
Collapse
|
8
|
Kovács DP, Batatia I, Arany ES, Csányi G. Evaluation of the MACE force field architecture: From medicinal chemistry to materials science. J Chem Phys 2023; 159:044118. [PMID: 37522405 DOI: 10.1063/5.0155322] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 06/29/2023] [Indexed: 08/01/2023] Open
Abstract
The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems, from amorphous carbon, universal materials modeling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimization to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies.
Collapse
Affiliation(s)
- Dávid Péter Kovács
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| | - Ilyes Batatia
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
- ENS Paris-Saclay, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Eszter Sára Arany
- School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SP, United Kingdom
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| |
Collapse
|
9
|
Darby JP, Kovács DP, Batatia I, Caro MA, Hart GLW, Ortner C, Csányi G. Tensor-Reduced Atomic Density Representations. PHYSICAL REVIEW LETTERS 2023; 131:028001. [PMID: 37505943 DOI: 10.1103/physrevlett.131.028001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 04/18/2023] [Indexed: 07/30/2023]
Abstract
Density-based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modeling, and the visualization and analysis of material datasets. The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. By exploiting symmetry, we recast this approach as tensor factorization of the standard neighbour-density-based descriptors and, using a new notation, identify connections to existing compression algorithms. In doing so, we form compact tensor-reduced representation of the local atomic environment whose size does not depend on the number of chemical elements, is systematically convergable, and therefore remains applicable to a wide range of data analysis and regression tasks.
Collapse
Affiliation(s)
- James P Darby
- Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom
| | - Dávid P Kovács
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom
| | - Ilyes Batatia
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom
- ENS Paris-Saclay, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Miguel A Caro
- Department of Electrical Engineering and Automation, Aalto University, FIN-02150 Espoo, Finland
| | - Gus L W Hart
- Department of Physics and Astronomy, Brigham Young University, Provo, Utah, 84602, USA
| | - Christoph Ortner
- Department of Mathematics, University of British Columbia, 1984 Mathematics Road, Vancouver, British Columbia, Canada V6T 1Z2
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom
| |
Collapse
|