1
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Unke OT, Stöhr M, Ganscha S, Unterthiner T, Maennel H, Kashubin S, Ahlin D, Gastegger M, Medrano Sandonas L, Berryman JT, Tkatchenko A, Müller KR. Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments. Sci Adv 2024; 10:eadn4397. [PMID: 38579003 DOI: 10.1126/sciadv.adn4397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/29/2024] [Indexed: 04/07/2024]
Abstract
The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.
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Affiliation(s)
- Oliver T Unke
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
| | - Martin Stöhr
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Stefan Ganscha
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Thomas Unterthiner
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Hartmut Maennel
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Sergii Kashubin
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Daniel Ahlin
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Michael Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
- BASLEARN - TU Berlin/BASF Joint Lab for Machine Learning, Technische Universität Berlin, 10587 Berlin, Germany
| | - Leonardo Medrano Sandonas
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Joshua T Berryman
- 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
| | - Klaus-Robert Müller
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea
- Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
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2
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Yim J, Campbell A, Mathieu E, Foong AYK, Gastegger M, Jiménez-Luna J, Lewis S, Satorras VG, Veeling BS, Noé F, Barzilay R, Jaakkola TS. Improved motif-scaffolding with SE(3) flow matching. ArXiv 2024:arXiv:2401.04082v1. [PMID: 38259348 PMCID: PMC10802670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Protein design often begins with knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models have achieved breakthrough success in designing scaffolds for a diverse range of motifs. However, the generated scaffolds tend to lack structural diversity, which can hinder success in wet-lab validation. In this work, we extend FrameFlow, an SE(3) flow matching model for protein backbone generation, to perform motif-scaffolding with two complementary approaches. The first is motif amortization, in which FrameFlow is trained with the motif as input using a data augmentation strategy. The second is motif guidance, which performs scaffolding using an estimate of the conditional score from FrameFlow, and requires no additional training. Both approaches achieve an equivalent or higher success rate than previous state-of-the-art methods, with 2.5 times more structurally diverse scaffolds. Code: https://github.com/microsoft/frame-flow.
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Affiliation(s)
- Jason Yim
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
| | | | | | | | | | | | | | | | | | | | - Regina Barzilay
- Computer Science and Articial Intelligence Laboratory, Massachusetts Institute of Technology
| | - Tommi S Jaakkola
- Computer Science and Articial Intelligence Laboratory, Massachusetts Institute of Technology
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3
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Lederer J, Gastegger M, Schütt KT, Kampffmeyer M, Müller KR, Unke OT. Automatic identification of chemical moieties. Phys Chem Chem Phys 2023; 25:26370-26379. [PMID: 37750554 PMCID: PMC10548786 DOI: 10.1039/d3cp03845a] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 08/18/2023] [Indexed: 09/27/2023]
Abstract
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this task by constructing atomic representations, from which the properties of interest are predicted. Here, we introduce a method to automatically identify chemical moieties (molecular building blocks) from such representations, enabling a variety of applications beyond property prediction, which otherwise rely on expert knowledge. The required representation can either be provided by a pretrained MPNN, or be learned from scratch using only structural information. Beyond the data-driven design of molecular fingerprints, the versatility of our approach is demonstrated by enabling the selection of representative entries in chemical databases, the automatic construction of coarse-grained force fields, as well as the identification of reaction coordinates.
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Affiliation(s)
- Jonas Lederer
- Berlin Institute of Technology (TU Berlin), 10587 Berlin, Germany.
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Germany
| | - Michael Gastegger
- Berlin Institute of Technology (TU Berlin), 10587 Berlin, Germany.
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Germany
| | - Kristof T Schütt
- Berlin Institute of Technology (TU Berlin), 10587 Berlin, Germany.
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Germany
| | - Michael Kampffmeyer
- Department of Physics and Technology, UiT The Arctic University of Norway, 9019 Tromsø, Norway
| | - Klaus-Robert Müller
- Berlin Institute of Technology (TU Berlin), 10587 Berlin, Germany.
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Germany
- Google Deepmind, Germany
- Department of Artificial Intelligence, Korea University, Seoul 136-713, Korea
- Max Planck Institut für Informatik, 66123 Saarbrücken, Germany
| | - Oliver T Unke
- Berlin Institute of Technology (TU Berlin), 10587 Berlin, Germany.
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Germany
- Google Deepmind, Germany
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4
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Platero-Rochart D, Krivobokova T, Gastegger M, Reibnegger G, Sánchez-Murcia PA. Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning. J Chem Inf Model 2023; 63:4623-4632. [PMID: 37479222 PMCID: PMC10430765 DOI: 10.1021/acs.jcim.3c00772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Indexed: 07/23/2023]
Abstract
The prediction of enzyme activity is one of the main challenges in catalysis. With computer-aided methods, it is possible to simulate the reaction mechanism at the atomic level. However, these methods are usually expensive if they are to be used on a large scale, as they are needed for protein engineering campaigns. To alleviate this situation, machine learning methods can help in the generation of predictive-decision models. Herein, we test different regression algorithms for the prediction of the reaction energy barrier of the rate-limiting step of the hydrolysis of mono-(2-hydroxyethyl)terephthalic acid by the MHETase ofIdeonella sakaiensis. As a training data set, we use steered quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulation snapshots and their corresponding pulling work values. We have explored three algorithms together with three chemical representations. As an outcome, our trained models are able to predict pulling works along the steered QM/MM MD simulations with a mean absolute error below 3 kcal mol-1 and a score value above 0.90. More challenging is the prediction of the energy maximum with a single geometry. Whereas the use of the initial snapshot of the QM/MM MD trajectory as input geometry yields a very poor prediction of the reaction energy barrier, the use of an intermediate snapshot of the former trajectory brings the score value above 0.40 with a low mean absolute error (ca. 3 kcal mol-1). Altogether, we have faced in this work some initial challenges of the final goal of getting an efficient workflow for the semiautomatic prediction of enzyme-catalyzed energy barriers and catalytic efficiencies.
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Affiliation(s)
- Daniel Platero-Rochart
- Laboratory
of Computer-Aided Molecular Design, Division of Medicinal Chemistry,
Otto-Loewi Research Center, Medical University
of Graz, Neue Stiftingtalstraße 6/III, A-8010 Graz, Austria
| | - Tatyana Krivobokova
- Department
of Statistics and Operations Research, University
of Vienna, Oskar-Morgenstern-Platz 1, A-1090 Vienna, Austria
| | - Michael Gastegger
- Institute
of Software Engineering and Theoretical Computer Science, Machine
Learning Group, Technische Universität, 10587 Berlin, Germany
| | - Gilbert Reibnegger
- Laboratory
of Computer-Aided Molecular Design, Division of Medicinal Chemistry,
Otto-Loewi Research Center, Medical University
of Graz, Neue Stiftingtalstraße 6/III, A-8010 Graz, Austria
| | - Pedro A. Sánchez-Murcia
- Laboratory
of Computer-Aided Molecular Design, Division of Medicinal Chemistry,
Otto-Loewi Research Center, Medical University
of Graz, Neue Stiftingtalstraße 6/III, A-8010 Graz, Austria
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5
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Schütt KT, Hessmann SSP, Gebauer NWA, Lederer J, Gastegger M. SchNetPack 2.0: A neural network toolbox for atomistic machine learning. J Chem Phys 2023; 158:144801. [PMID: 37061495 DOI: 10.1063/5.0138367] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2023] Open
Abstract
SchNetPack is a versatile neural network toolbox that addresses both the requirements of method development and the application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks, and a PyTorch implementation of molecular dynamics. An optional integration with PyTorch Lightning and the Hydra configuration framework powers a flexible command-line interface. This makes SchNetPack 2.0 easily extendable with a custom code and ready for complex training tasks, such as the generation of 3D molecular structures.
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Affiliation(s)
- Kristof T Schütt
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | | | - Niklas W A Gebauer
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Jonas Lederer
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Michael Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
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6
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Westermayr J, Gastegger M, Vörös D, Panzenboeck L, Joerg F, González L, Marquetand P. Deep learning study of tyrosine reveals that roaming can lead to photodamage. Nat Chem 2022; 14:914-919. [PMID: 35655007 DOI: 10.1038/s41557-022-00950-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 04/13/2022] [Indexed: 01/12/2023]
Abstract
Amino acids are among the building blocks of life, forming peptides and proteins, and have been carefully 'selected' to prevent harmful reactions caused by light. To prevent photodamage, molecules relax from electronic excited states to the ground state faster than the harmful reactions can occur; however, such photochemistry is not fully understood, in part because theoretical simulations of such systems are extremely expensive-with only smaller chromophores accessible. Here, we study the excited-state dynamics of tyrosine using a method based on deep neural networks that leverages the physics underlying quantum chemical data and combines different levels of theory. We reveal unconventional and dynamically controlled 'roaming' dynamics in excited tyrosine that are beyond chemical intuition and compete with other ultrafast deactivation mechanisms. Our findings suggest that the roaming atoms are radicals that can lead to photodamage, offering a new perspective on the photostability and photodamage of biological systems.
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Affiliation(s)
- Julia Westermayr
- Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna, Vienna, Austria.,Department of Chemistry, University of Warwick, Coventry, UK
| | - Michael Gastegger
- Machine Learning Group, Technical University of Berlin, Berlin, Germany
| | - Dóra Vörös
- Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna, Vienna, Austria
| | - Lisa Panzenboeck
- Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna, Vienna, Austria.,Faculty of Chemistry, Department of Analytical Chemistry, University of Vienna, Vienna, Austria
| | - Florian Joerg
- Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna, Vienna, Austria.,Faculty of Chemistry, Institute of Computational Biological Chemistry, University of Vienna, Vienna, Austria
| | - Leticia González
- Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna, Vienna, Austria.,Vienna Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Vienna, Austria
| | - Philipp Marquetand
- Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna, Vienna, Austria. .,Vienna Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Vienna, Austria. .,Research Network Data Science @ Uni Vienna, University of Vienna, Vienna, Austria.
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7
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Gebauer NWA, Gastegger M, Hessmann SSP, Müller KR, Schütt KT. Inverse design of 3d molecular structures with conditional generative neural networks. Nat Commun 2022; 13:973. [PMID: 35190542 PMCID: PMC8861047 DOI: 10.1038/s41467-022-28526-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 01/28/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractThe rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains where reference calculations are sparse. We demonstrate the utility of our method for inverse design by generating molecules with specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.
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8
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Unke OT, Chmiela S, Gastegger M, Schütt KT, Sauceda HE, Müller KR. SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects. Nat Commun 2021; 12:7273. [PMID: 34907176 PMCID: PMC8671403 DOI: 10.1038/s41467-021-27504-0] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/16/2021] [Indexed: 01/12/2023] Open
Abstract
Machine-learned force fields combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current machine-learned force fields typically ignore electronic degrees of freedom, such as the total charge or spin state, and assume chemical locality, which is problematic when molecules have inconsistent electronic states, or when nonlocal effects play a significant role. This work introduces SpookyNet, a deep neural network for constructing machine-learned force fields with explicit treatment of electronic degrees of freedom and nonlocality, modeled via self-attention in a transformer architecture. Chemically meaningful inductive biases and analytical corrections built into the network architecture allow it to properly model physical limits. SpookyNet improves upon the current state-of-the-art (or achieves similar performance) on popular quantum chemistry data sets. Notably, it is able to generalize across chemical and conformational space and can leverage the learned chemical insights, e.g. by predicting unknown spin states, thus helping to close a further important remaining gap for today's machine learning models in quantum chemistry.
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Affiliation(s)
- Oliver T Unke
- Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany.
- DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat), Technische Universität Berlin, 10623, Berlin, Germany.
| | - Stefan Chmiela
- Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany
| | - Michael Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany
- DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat), Technische Universität Berlin, 10623, Berlin, Germany
| | - Kristof T Schütt
- Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany
| | - Huziel E Sauceda
- Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany
- BASLEARN, BASF-TU joint Lab, Technische Universität Berlin, 10587, Berlin, Germany
| | - Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany.
- Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Korea.
- Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123, Saarbrücken, Germany.
- BIFOLD-Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.
- Google Research, Brain team, Berlin, Germany.
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9
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Gastegger M, Schütt KT, Müller KR. Machine learning of solvent effects on molecular spectra and reactions. Chem Sci 2021; 12:11473-11483. [PMID: 34567501 PMCID: PMC8409491 DOI: 10.1039/d1sc02742e] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 07/22/2021] [Indexed: 01/13/2023] Open
Abstract
Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning has extended the boundaries of quantum chemistry by providing highly accurate and efficient surrogate models of electronic structure theory, which previously have been out of reach for conventional approaches. Those models have long been restricted to closed molecular systems without accounting for environmental influences, such as external electric and magnetic fields or solvent effects. Here, we introduce the deep neural network FieldSchNet for modeling the interaction of molecules with arbitrary external fields. FieldSchNet offers access to a wealth of molecular response properties, enabling it to simulate a wide range of molecular spectra, such as infrared, Raman and nuclear magnetic resonance. Beyond that, it is able to describe implicit and explicit molecular environments, operating as a polarizable continuum model for solvation or in a quantum mechanics/molecular mechanics setup. We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction. Based on these results, we use FieldSchNet to design an external environment capable of lowering the activation barrier of the rearrangement reaction significantly, demonstrating promising venues for inverse chemical design.
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Affiliation(s)
- Michael Gastegger
- Machine Learning Group, Technische Universität Berlin 10587 Berlin Germany
| | - Kristof T Schütt
- Machine Learning Group, Technische Universität Berlin 10587 Berlin Germany
- Berlin Institute for the Foundations of Learning and Data 10587 Berlin Germany
| | - Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin 10587 Berlin Germany
- Berlin Institute for the Foundations of Learning and Data 10587 Berlin Germany
- Department of Artificial Intelligence, Korea University Anam-dong, Seongbuk-gu Seoul 02841 Korea
- Max-Planck-Institut für Informatik 66123 Saarbrücken Germany
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10
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Keith JA, Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller KR, Tkatchenko A. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems. Chem Rev 2021; 121:9816-9872. [PMID: 34232033 PMCID: PMC8391798 DOI: 10.1021/acs.chemrev.1c00107] [Citation(s) in RCA: 170] [Impact Index Per Article: 56.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Indexed: 12/23/2022]
Abstract
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
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Affiliation(s)
- John A. Keith
- Department
of Chemical and Petroleum Engineering Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Valentin Vassilev-Galindo
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Bingqing Cheng
- Accelerate
Programme for Scientific Discovery, Department
of Computer Science and Technology, 15 J. J. Thomson Avenue, Cambridge CB3 0FD, United Kingdom
| | - Stefan Chmiela
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Michael Gastegger
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Klaus-Robert Müller
- Machine
Learning Group, Technische Universität
Berlin, 10587, Berlin, Germany
- Department
of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Korea
- Max-Planck-Institut für Informatik, 66123 Saarbrücken, Germany
- Google Research, Brain Team, 10117 Berlin, Germany
| | - Alexandre Tkatchenko
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
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11
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Abstract
In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail, and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs.
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Affiliation(s)
- Oliver
T. Unke
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
- DFG
Cluster of Excellence “Unifying Systems in Catalysis”
(UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
| | - Stefan Chmiela
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
| | - Huziel E. Sauceda
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
- BASLEARN,
BASF-TU Joint Lab, Technische Universität
Berlin, 10587 Berlin, Germany
| | - Michael Gastegger
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
- DFG
Cluster of Excellence “Unifying Systems in Catalysis”
(UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
- BASLEARN,
BASF-TU Joint Lab, Technische Universität
Berlin, 10587 Berlin, Germany
| | - Igor Poltavsky
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Kristof T. Schütt
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
| | - Alexandre Tkatchenko
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Klaus-Robert Müller
- Machine
Learning Group, Technische Universität
Berlin, 10587 Berlin, Germany
- BIFOLD−Berlin
Institute for the Foundations of Learning and Data, Berlin, Germany
- Department
of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea
- Max Planck
Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany
- Google
Research, Brain Team, Berlin, Germany
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12
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Westermayr J, Gastegger M, Schütt KT, Maurer RJ. Perspective on integrating machine learning into computational chemistry and materials science. J Chem Phys 2021; 154:230903. [PMID: 34241249 DOI: 10.1063/5.0047760] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties-be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a tighter and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training.
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Affiliation(s)
- Julia Westermayr
- Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
| | - Michael Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Kristof T Schütt
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Reinhard J Maurer
- Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
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13
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Sauceda HE, Gastegger M, Chmiela S, Müller KR, Tkatchenko A. Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields. J Chem Phys 2020; 153:124109. [DOI: 10.1063/5.0023005] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Huziel E. Sauceda
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg, Luxembourg
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- BASLEARN, BASF-TU Joint Lab, Technische Universität Berlin, 10587 Berlin, Germany
| | - Michael Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- BASLEARN, BASF-TU Joint Lab, Technische Universität Berlin, 10587 Berlin, Germany
- DFG Cluster of Excellence “Unifying Systems in Catalysis” (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
| | - Stefan Chmiela
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, South Korea
- Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany
- Google Research, Brain Team, Berlin, Germany
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg, Luxembourg
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14
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Gastegger M, McSloy A, Luya M, Schütt KT, Maurer RJ. A deep neural network for molecular wave functions in quasi-atomic minimal basis representation. J Chem Phys 2020; 153:044123. [PMID: 32752663 DOI: 10.1063/5.0012911] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The emergence of machine learning methods in quantum chemistry provides new methods to revisit an old problem: Can the predictive accuracy of electronic structure calculations be decoupled from their numerical bottlenecks? Previous attempts to answer this question have, among other methods, given rise to semi-empirical quantum chemistry in minimal basis representation. We present an adaptation of the recently proposed SchNet for Orbitals (SchNOrb) deep convolutional neural network model [K. T. Schütt et al., Nat. Commun. 10, 5024 (2019)] for electronic wave functions in an optimized quasi-atomic minimal basis representation. For five organic molecules ranging from 5 to 13 heavy atoms, the model accurately predicts molecular orbital energies and wave functions and provides access to derived properties for chemical bonding analysis. Particularly for larger molecules, the model outperforms the original atomic-orbital-based SchNOrb method in terms of accuracy and scaling. We conclude by discussing the future potential of this approach in quantum chemical workflows.
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Affiliation(s)
- M Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - A McSloy
- Department of Chemistry, University of Warwick, Gibbet Hill Road, CV4 7AL Coventry, United Kingdom
| | - M Luya
- Department of Chemistry, University of Warwick, Gibbet Hill Road, CV4 7AL Coventry, United Kingdom
| | - K T Schütt
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - R J Maurer
- Department of Chemistry, University of Warwick, Gibbet Hill Road, CV4 7AL Coventry, United Kingdom
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15
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Westermayr J, Gastegger M, Marquetand P. Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics. J Phys Chem Lett 2020; 11:3828-3834. [PMID: 32311258 PMCID: PMC7246974 DOI: 10.1021/acs.jpclett.0c00527] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 04/20/2020] [Indexed: 05/26/2023]
Abstract
In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties-multiple energies, forces, and different couplings-for photodynamics simulations. We simplify such simulations substantially by (i) a phase-free training skipping costly preprocessing of raw quantum chemistry data; (ii) rotationally covariant nonadiabatic couplings, which can either be trained or (iii) alternatively be approximated from only ML potentials, their gradients, and Hessians; and (iv) incorporating spin-orbit couplings. As the deep-learning method, we employ SchNet with its automatically determined representation of molecular structures and extend it for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on two polyatomic molecules and paves the way toward efficient photodynamics simulations of complex systems.
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Affiliation(s)
- Julia Westermayr
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - Michael Gastegger
- Machine
Learning Group, Technical University of
Berlin, 10587 Berlin, Germany
| | - Philipp Marquetand
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
- Vienna
Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
- Data
Science @ Uni Vienna, University of Vienna, Währinger Str. 29, 1090 Vienna, Austria
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16
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17
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Schütt KT, Gastegger M, Tkatchenko A, Müller KR, Maurer RJ. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. Nat Commun 2019; 10:5024. [PMID: 31729373 PMCID: PMC6858523 DOI: 10.1038/s41467-019-12875-2] [Citation(s) in RCA: 179] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 09/25/2019] [Indexed: 12/03/2022] Open
Abstract
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry. Machine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.
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Affiliation(s)
- K T Schütt
- Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany
| | - M Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany
| | - A Tkatchenko
- Physics and Materials Science Research Unit, University of Luxembourg, L-1511, Luxembourg, Luxembourg.
| | - K-R Müller
- Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany. .,Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Korea. .,Max-Planck-Institut für Informatik, Saarbrücken, Germany.
| | - R J Maurer
- Department of Chemistry, University of Warwick, Gibbet Hill Road, CV4 7AL, Coventry, UK.
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18
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Westermayr J, Gastegger M, Menger MFSJ, Mai S, González L, Marquetand P. Machine learning enables long time scale molecular photodynamics simulations. Chem Sci 2019; 10:8100-8107. [PMID: 31857878 PMCID: PMC6849489 DOI: 10.1039/c9sc01742a] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 08/02/2019] [Indexed: 02/04/2023] Open
Abstract
Photo-induced processes are fundamental in nature but accurate simulations of their dynamics are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynamics on nanosecond time scales, which are otherwise out of reach with contemporary approaches. Instead of expensive quantum chemistry during molecular dynamics simulations, we use deep neural networks to learn the relationship between a molecular geometry and its high-dimensional electronic properties. As an example, the time evolution of the methylenimmonium cation for one nanosecond is used to demonstrate that machine learning algorithms can outperform standard excited-state molecular dynamics approaches in their computational efficiency while delivering the same accuracy.
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Affiliation(s)
- Julia Westermayr
- Institute of Theoretical Chemistry , Faculty of Chemistry , University of Vienna , 1090 Vienna , Austria .
| | - Michael Gastegger
- Machine Learning Group , Technical University of Berlin , 10587 Berlin , Germany
| | - Maximilian F S J Menger
- Institute of Theoretical Chemistry , Faculty of Chemistry , University of Vienna , 1090 Vienna , Austria . .,Dipartimento di Chimica e Chimica Industriale , University of Pisa , Via G. Moruzzi 13 , 56124 Pisa , Italy
| | - Sebastian Mai
- Institute of Theoretical Chemistry , Faculty of Chemistry , University of Vienna , 1090 Vienna , Austria .
| | - Leticia González
- Institute of Theoretical Chemistry , Faculty of Chemistry , University of Vienna , 1090 Vienna , Austria .
| | - Philipp Marquetand
- Institute of Theoretical Chemistry , Faculty of Chemistry , University of Vienna , 1090 Vienna , Austria .
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19
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20
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Abstract
SchNetPack is a toolbox for the development and application of deep neural networks that predict potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training, and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models. For now, SchNetPack includes implementations of (weighted) atom-centered symmetry functions and the deep tensor neural network SchNet, as well as ready-to-use scripts that allow one to train these models on molecule and material datasets. Based on the PyTorch deep learning framework, SchNetPack allows one to efficiently apply the neural networks to large datasets with millions of reference calculations, as well as parallelize the model across multiple GPUs. Finally, SchNetPack provides an interface to the Atomic Simulation Environment in order to make trained models easily accessible to researchers that are not yet familiar with neural networks.
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Affiliation(s)
- K T Schütt
- Machine Learning Group , Technische Universität Berlin , 10587 Berlin , Germany
| | - P Kessel
- Machine Learning Group , Technische Universität Berlin , 10587 Berlin , Germany
| | - M Gastegger
- Machine Learning Group , Technische Universität Berlin , 10587 Berlin , Germany
| | - K A Nicoli
- Machine Learning Group , Technische Universität Berlin , 10587 Berlin , Germany
| | - A Tkatchenko
- Physics and Materials Science Research Unit , University of Luxembourg , L-1511 Luxembourg , Luxembourg
| | - K-R Müller
- Machine Learning Group , Technische Universität Berlin , 10587 Berlin , Germany.,Department of Brain and Cognitive Engineering , Korea University , Anam-dong, Seongbuk-gu, Seoul 02841 , South Korea.,Max-Planck-Institut für Informatik , Saarbrücken , Germany
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21
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Gastegger M, Schwiedrzik L, Bittermann M, Berzsenyi F, Marquetand P. wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials. J Chem Phys 2018; 148:241709. [DOI: 10.1063/1.5019667] [Citation(s) in RCA: 145] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Affiliation(s)
- M. Gastegger
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - L. Schwiedrzik
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - M. Bittermann
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - F. Berzsenyi
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - P. Marquetand
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
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22
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Gastegger M, Behler J, Marquetand P. Machine learning molecular dynamics for the simulation of infrared spectra. Chem Sci 2017; 8:6924-6935. [PMID: 29147518 PMCID: PMC5636952 DOI: 10.1039/c7sc02267k] [Citation(s) in RCA: 246] [Impact Index Per Article: 35.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 08/08/2017] [Indexed: 12/28/2022] Open
Abstract
Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n-alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.
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Affiliation(s)
- Michael Gastegger
- University of Vienna , Faculty of Chemistry , Institute of Theoretical Chemistry , Währinger Str. 17 , 1090 Vienna , Austria . ; ; Tel: +43 1 4277 52764
| | - Jörg Behler
- Universität Göttingen , Institut für Physikalische Chemie , Theoretische Chemie , Tammannstr. 6 , 37077 Göttingen , Germany
| | - Philipp Marquetand
- University of Vienna , Faculty of Chemistry , Institute of Theoretical Chemistry , Währinger Str. 17 , 1090 Vienna , Austria . ; ; Tel: +43 1 4277 52764
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23
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Gastegger M, Kauffmann C, Behler J, Marquetand P. Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes. J Chem Phys 2016; 144:194110. [DOI: 10.1063/1.4950815] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Michael Gastegger
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, Vienna, Austria
| | - Clemens Kauffmann
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, Vienna, Austria
| | - Jörg Behler
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, Universitätsstraße 150, Bochum, Germany
| | - Philipp Marquetand
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, Vienna, Austria
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24
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Berger A, Kostyan MK, Klose SI, Gastegger M, Lorbeer E, Brecker L, Schinnerl J. Loganin and secologanin derived tryptamine-iridoid alkaloids from Palicourea crocea and Palicourea padifolia (Rubiaceae). Phytochemistry 2015; 116:162-169. [PMID: 26043882 DOI: 10.1016/j.phytochem.2015.05.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Revised: 03/16/2015] [Accepted: 05/22/2015] [Indexed: 06/04/2023]
Abstract
During comparative analysis on Palicourea species from Costa Rica, two unusual loganin derived tryptamine-iridoid alkaloids were isolated from an accession of Palicourea crocea. Besides the already known brachycerine (2), palicroceaine (1) features a novel hexacyclic backbone. A second provenance, however, yielded strictosidinic acid (3), belonging to the more common secologanin derived tryptamine-iridoid alkaloids, such as those found in Palicourea padifolia. From this species, strictosidine (4), lyaloside (5) and its derivative (E)-O-(6')-(4″-hydroxy-3″,5″-dimethoxy)-cinnamoyl lyaloside (6) could be isolated. A herbarium specimen-based screening was performed, indicating some degree of regional differentiation in alkaloid content and biosynthetic pathways within the widespread and variable Pal. crocea. It further shows its differentiation from the related strictosidine containing Palicourea croceoides. The occurrence of loganin derived tryptamine-iridoid alkaloids in Pal. crocea, Psychotria brachyceras and Psychotria brachypoda, all putatively unrelated members of the Palicourea s.l. clade, is a noteworthy exception within the genus, otherwise largely characterized by secologanin-derived tryptamine-iridoid alkaloids.
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Affiliation(s)
- Andreas Berger
- Chemodiversity Research Group, Division of Systematic and Evolutionary Botany, University of Vienna, Rennweg 14, A-1030 Vienna, Austria
| | - Maria Katharina Kostyan
- Chemodiversity Research Group, Division of Systematic and Evolutionary Botany, University of Vienna, Rennweg 14, A-1030 Vienna, Austria
| | - Simon Immo Klose
- Institute of Organic Chemistry, University of Vienna, Währinger Strasse 38, A-1090 Vienna, Austria
| | - Michael Gastegger
- Institute of Organic Chemistry, University of Vienna, Währinger Strasse 38, A-1090 Vienna, Austria
| | - Eberhard Lorbeer
- Institute of Organic Chemistry, University of Vienna, Währinger Strasse 38, A-1090 Vienna, Austria
| | - Lothar Brecker
- Institute of Organic Chemistry, University of Vienna, Währinger Strasse 38, A-1090 Vienna, Austria
| | - Johann Schinnerl
- Chemodiversity Research Group, Division of Systematic and Evolutionary Botany, University of Vienna, Rennweg 14, A-1030 Vienna, Austria.
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25
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Abstract
Artificial neural networks (NNs) represent a relatively recent approach for the prediction of molecular potential energies, suitable for simulations of large molecules and long time scales. By using NNs to fit electronic structure data, it is possible to obtain empirical potentials of high accuracy combined with the computational efficiency of conventional force fields. However, as opposed to the latter, changing bonding patterns and unusual coordination geometries can be described due to the underlying flexible functional form of the NNs. One of the most promising approaches in this field is the high-dimensional neural network (HDNN) method, which is especially adapted to the prediction of molecular properties. While HDNNs have been mostly used to model solid state systems and surface interactions, we present here the first application of the HDNN approach to an organic reaction, the Claisen rearrangement of allyl vinyl ether to 4-pentenal. To construct the corresponding HDNN potential, a new training algorithm is introduced. This algorithm is termed "element-decoupled" global extended Kalman filter (ED-GEKF) and is based on the decoupled Kalman filter. Using a metadynamics trajectory computed with density functional theory as reference data, we show that the ED-GEKF exhibits superior performance - both in terms of accuracy and training speed - compared to other variants of the Kalman filter hitherto employed in HDNN training. In addition, the effect of including forces during ED-GEKF training on the resulting potentials was studied.
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Affiliation(s)
- Michael Gastegger
- Institute of Theoretical Chemistry, University of Vienna , Währinger Str. 17, 1090 Vienna, Austria
| | - Philipp Marquetand
- Institute of Theoretical Chemistry, University of Vienna , Währinger Str. 17, 1090 Vienna, Austria
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