ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

Smith, J. S.; Isayev, O.; Roitberg, A. E.

doi:10.1039/c6sc05720a

Title: ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

Abstract

Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 ismore »« less

Authors:

^[1];

^[2];

^[1]

Univ. of Florida, Gainesville, FL (United States). Dept. of Chemistry
Univ. of North Carolina, Chapel Hill, NC (United States). Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy

Publication Date:: Wed Feb 08 00:00:00 EST 2017

Research Org.:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: National Institutes of Health (NIH); US Department of the Navy, Office of Naval Research (ONR); Eshelman Institute for Innovation; NVIDIA Corporation; USDOE Laboratory Directed Research and Development (LDRD) Program

OSTI Identifier:: 1624947

Grant/Contract Number:: AC52-06NA25396; GM110077; DMR110088; ACI-1053575

Resource Type:: Accepted Manuscript

Journal Name:: Chemical Science

Additional Journal Information:: Journal Volume: 8; Journal Issue: 4; Journal ID: ISSN 2041-6520

Publisher:: Royal Society of Chemistry

Country of Publication:: United States

Language:: English

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 97 MATHEMATICS AND COMPUTING; 74 ATOMIC AND MOLECULAR PHYSICS; Chemistry

Citation Formats


                    Smith, J. S., Isayev, O., and Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost.  United States: N. p., 2017. 
Web.  doi:10.1039/c6sc05720a.

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                    Smith, J. S., Isayev, O., & Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost.  United States.  https://doi.org/10.1039/c6sc05720a

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                    Smith, J. S., Isayev, O., and Roitberg, A. E. Wed .  
"ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost".  United States.  https://doi.org/10.1039/c6sc05720a.  https://www.osti.gov/servlets/purl/1624947.

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@article{osti_1624947,

  title        = {ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost},

  author       = {Smith, J. S. and Isayev, O. and Roitberg, A. E.},

  abstractNote = {Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set.},

  doi          = {10.1039/c6sc05720a},

  journal      = {Chemical Science},

  number       = 4,

  volume       = 8,

  place        = {United States},

  year         = {Wed Feb 08 00:00:00 EST 2017},

  month        = {Wed Feb 08 00:00:00 EST 2017}

}

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Wang, Bo; Ng, Ho-Leung
Journal of Computer-Aided Molecular Design, Vol. 34, Issue 2
DOI: 10.1007/s10822-019-00275-z

Review of force fields and intermolecular potentials used in atomistic computational materials research
journal, September 2018

Harrison, Judith A.; Schall, J. David; Maskey, Sabina
Applied Physics Reviews, Vol. 5, Issue 3
DOI: 10.1063/1.5020808

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
journal, April 2018

Zhang, Linfeng; Han, Jiequn; Wang, Han
Physical Review Letters, Vol. 120, Issue 14
DOI: 10.1103/physrevlett.120.143001

Systematic Design-of-Experiments, factorial-design approaches for tuning simple empirical water models
journal, June 2019

O’Connor, Diarmuid; English, Niall J.
Molecular Simulation
DOI: 10.1080/08927022.2019.1626987

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
journal, September 2019

Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor
Advanced Materials, Vol. 31, Issue 46
DOI: 10.1002/adma.201902765

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials
text, January 2018

Sosso, Gc; Deringer, Volker; Elliott, Stephen
Apollo - University of Cambridge Repository
DOI: 10.17863/cam.34288

Adiabatic and nonadiabatic energy dissipation during scattering of vibrationally excited CO from Au(111)
journal, November 2019

Huang, Meng; Zhou, Xueyao; Zhang, Yaolong
Physical Review B, Vol. 100, Issue 20
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Evaluation of experimental alkali metal ion–ligand noncovalent bond strengths with DLPNO-CCSD(T) method
journal, July 2019

Maity, Bholanath; Minenkov, Yury; Cavallo, Luigi
The Journal of Chemical Physics, Vol. 151, Issue 1
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Machine learning for molecular and materials science
journal, July 2018

Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh
Nature, Vol. 559, Issue 7715
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wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
journal, June 2018

Gastegger, M.; Schwiedrzik, L.; Bittermann, M.
The Journal of Chemical Physics, Vol. 148, Issue 24
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Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
journal, June 2018

Fujikake, So; Deringer, Volker L.; Lee, Tae Hoon
The Journal of Chemical Physics, Vol. 148, Issue 24
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Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery
journal, September 2018

Oses, Corey; Toher, Cormac; Curtarolo, Stefano
MRS Bulletin, Vol. 43, Issue 9
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Prediction of atomization energy using graph kernel and active learning
journal, January 2019

Tang, Yu-Hang; de Jong, Wibe A.
The Journal of Chemical Physics, Vol. 150, Issue 4
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Extending the accuracy of the SNAP interatomic potential form
journal, June 2018

Wood, Mitchell A.; Thompson, Aidan P.
The Journal of Chemical Physics, Vol. 148, Issue 24
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DeltaDelta neural networks for lead optimization of small molecule potency
journal, January 2019

Jiménez-Luna, José; Pérez-Benito, Laura; Martínez-Rosell, Gerard
Chemical Science, Vol. 10, Issue 47
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Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
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Vasudevan, Rama K.; Choudhary, Kamal; Mehta, Apurva
MRS Communications, Vol. 9, Issue 3
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Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
journal, June 2018

Imbalzano, Giulio; Anelli, Andrea; Giofré, Daniele
The Journal of Chemical Physics, Vol. 148, Issue 24
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Atom-density representations for machine learning
journal, April 2019

Willatt, Michael J.; Musil, Félix; Ceriotti, Michele
The Journal of Chemical Physics, Vol. 150, Issue 15
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Parallelized Natural Extension Reference Frame: Parallelized Conversion from Internal to Cartesian Coordinates: pNatural Extension Reference Frame: Parallelized Conversion from Internal to Cartesian Coordinates
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AlQuraishi, Mohammed
Journal of Computational Chemistry, Vol. 40, Issue 7
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Data‐Driven Materials Science: Status, Challenges, and Perspectives
journal, November 2019

Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart
Advanced Science, Vol. 7, Issue 2
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Reproducing global potential energy surfaces with continuous-filter convolutional neural networks
journal, May 2019

Brorsen, Kurt R.
The Journal of Chemical Physics, Vol. 150, Issue 20
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Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
journal, November 2019

Hernandez, Alberto; Balasubramanian, Adarsh; Yuan, Fenglin
npj Computational Materials, Vol. 5, Issue 1
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Massive computational acceleration by using neural networks to emulate mechanism-based biological models
journal, September 2019

Wang, Shangying; Fan, Kai; Luo, Nan
Nature Communications, Vol. 10, Issue 1
DOI: 10.1038/s41467-019-12342-y

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.
journalarticle, January 2019

Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor
Wiley
DOI: 10.17863/cam.43999

Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels
journal, June 2017

Dral, Pavlo O.; Owens, Alec; Yurchenko, Sergei N.
The Journal of Chemical Physics, Vol. 146, Issue 24
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Role of Water in the Reaction Mechanism and endo / exo Selectivity of 1,3‐Dipolar Cycloadditions Elucidated by Quantum Chemistry and Machine Learning
journal, May 2019

Yang, Xin; Zou, Jun; Wang, Yifei
Chemistry – A European Journal, Vol. 25, Issue 35
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Adaptive coupling of a deep neural network potential to a classical force field
journal, October 2018

Zhang, Linfeng; Wang, Han; E., Weinan
The Journal of Chemical Physics, Vol. 149, Issue 15
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A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
journal, April 2019

Cheng, Lixue; Welborn, Matthew; Christensen, Anders S.
The Journal of Chemical Physics, Vol. 150, Issue 13
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Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles
journal, May 2018

Kolsbjerg, E. L.; Peterson, A. A.; Hammer, B.
Physical Review B, Vol. 97, Issue 19
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Making machine learning a useful tool in the accelerated discovery of transition metal complexes
journal, July 2019

Kulik, Heather J.
WIREs Computational Molecular Science, Vol. 10, Issue 1
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Unsupervised machine learning in atomistic simulations, between predictions and understanding
journal, April 2019

Ceriotti, Michele
The Journal of Chemical Physics, Vol. 150, Issue 15
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The future of force fields in computer-aided drug design
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Cole, Daniel J.; Horton, Joshua T.; Nelson, Lauren
Future Medicinal Chemistry, Vol. 11, Issue 18
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A Critical Review of Machine Learning of Energy Materials
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Multiscale methods in drug design bridge chemical and biological complexity in the search for cures
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Nature Reviews Chemistry, Vol. 2, Issue 4
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Zhu, Junmian; Vuong, Van Quan; Sumpter, Bobby G.
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Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
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Mayr, Andreas; Klambauer, Günter; Unterthiner, Thomas
Chemical Science, Vol. 9, Issue 24
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Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences
journal, August 2019

Herr, John E.; Koh, Kevin; Yao, Kun
The Journal of Chemical Physics, Vol. 151, Issue 8
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Machine Learning a General-Purpose Interatomic Potential for Silicon
journal, December 2018

Bartók, Albert P.; Kermode, James; Bernstein, Noam
Physical Review X, Vol. 8, Issue 4
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Generative Models for Artificially-intelligent Molecular Design
journal, January 2018

Schneider, Gisbert
Molecular Informatics, Vol. 37, Issue 1-2
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Machine learning unifies the modeling of materials and molecules
journal, December 2017

Bartók, Albert P.; De, Sandip; Poelking, Carl
Science Advances, Vol. 3, Issue 12
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Tradeoffs and Compatibilities of Chemical Properties in CpHqFrOs System
journal, July 2019

Okamoto, Yasuharu
Scientific Reports, Vol. 9, Issue 1
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A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
journal, September 2019

Mailoa, Jonathan P.; Kornbluth, Mordechai; Batzner, Simon
Nature Machine Intelligence, Vol. 1, Issue 10
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Machine Learning a General-Purpose Interatomic Potential for Silicon
text, January 2018

Bartók, Ap; Kermode, J.; Bernstein, N.
Apollo - University of Cambridge Repository
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Simulating Diffusion Properties of Solid‐State Electrolytes via a Neural Network Potential: Performance and Training Scheme
journal, December 2019

Marcolongo, Aris; Binninger, Tobias; Zipoli, Federico
ChemSystemsChem, Vol. 2, Issue 3
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Machine learning for the modeling of interfaces in energy storage and conversion materials
journal, July 2019

Artrith, Nongnuch
Journal of Physics: Energy, Vol. 1, Issue 3
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Data‐Driven Materials Science: Status, Challenges, and Perspectives
journal, September 2019

Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart
Advanced Science, Vol. 6, Issue 21
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Operators in quantum machine learning: Response properties in chemical space
journal, February 2019

Christensen, Anders S.; Faber, Felix A.; von Lilienfeld, O. Anatole
The Journal of Chemical Physics, Vol. 150, Issue 6
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Representations and descriptors unifying the study of molecular and bulk systems
journal, December 2019

Rossi, Kevin; Cumby, James
International Journal of Quantum Chemistry, Vol. 120, Issue 8
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Systematic Design-of-Experiments, factorial-design approaches for tuning simple empirical water models
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O’Connor, Diarmuid; English, Niall J.
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Machine-learned multi-system surrogate models for materials prediction
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Nyshadham, Chandramouli; Rupp, Matthias; Bekker, Brayden
npj Computational Materials, Vol. 5, Issue 1
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Atomic partial charge predictions for furanoses by random forest regression with atom type symmetry function
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Wang, Xiaocong; Gao, Jun
RSC Advances, Vol. 10, Issue 2
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From ab initio data to high-dimensional potential energy surfaces: A critical overview and assessment of the development of permutationally invariant polynomial potential energy surfaces for single molecules
journal, November 2019

Brown, Sandra E.
The Journal of Chemical Physics, Vol. 151, Issue 19
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Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
journal, February 2019

Podryabinkin, Evgeny V.; Tikhonov, Evgeny V.; Shapeev, Alexander V.
Physical Review B, Vol. 99, Issue 6
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How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry
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Häse, Florian; Fdez. Galván, Ignacio; Aspuru-Guzik, Alán
Chemical Science, Vol. 10, Issue 8
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Constructing convex energy landscapes for atomistic structure optimization
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Chiriki, Siva; Christiansen, Mads-Peter V.; Hammer, B.
Physical Review B, Vol. 100, Issue 23
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Solving the electronic structure problem with machine learning
journal, February 2019

Chandrasekaran, Anand; Kamal, Deepak; Batra, Rohit
npj Computational Materials, Vol. 5, Issue 1
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Enumeration of de novo inorganic complexes for chemical discovery and machine learning
journal, January 2020

Gugler, Stefan; Janet, Jon Paul; Kulik, Heather J.
Molecular Systems Design & Engineering, Vol. 5, Issue 1
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A shared-weight neural network architecture for predicting molecular properties
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Profitt, Trevor A.; Pearson, Jason K.
Physical Chemistry Chemical Physics, Vol. 21, Issue 47
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Constant size descriptors for accurate machine learning models of molecular properties
journal, June 2018

Collins, Christopher R.; Gordon, Geoffrey J.; von Lilienfeld, O. Anatole
The Journal of Chemical Physics, Vol. 148, Issue 24
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Boltzmann Generators – Sampling Equilibrium States of Many-Body Systems with Deep Learning
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Noe, Frank; Olsson, Simon; Köhler, Jonas
Zenodo
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Boltzmann Generators – Sampling Equilibrium States of Many-Body Systems with Deep Learning
text, January 2019

Noe, Frank; Olsson, Simon; Köhler, Jonas
Zenodo
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Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials
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Sosso, Gabriele C.; Deringer, Volker L.; Elliott, Stephen R.
Molecular Simulation, Vol. 44, Issue 11
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Toward Design of Novel Materials for Organic Electronics
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Friederich, Pascal; Fediai, Artem; Kaiser, Simon
Advanced Materials, Vol. 31, Issue 26
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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
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Smith, Justin S.; Nebgen, Benjamin T.; Zubatyuk, Roman
Nature Communications, Vol. 10, Issue 1
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Systematic Design-of-Experiments, factorial-design approaches for tuning simple empirical water models
text, January 2019

O’Connor, Diarmuid; English, Niall J.
Taylor & Francis
DOI: 10.6084/m9.figshare.8282693.v1

De novo exploration and self-guided learning of potential-energy surfaces
text, January 2019

Bernstein, Noam; Csányi, Gábor; Deringer, Volker L.
Apollo - University of Cambridge Repository
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Extensive deep neural networks for transferring small scale learning to large scale systems
text, January 2017

Mills, Kyle; Ryczko, Kevin; Luchak, Iryna
arXiv
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The TensorMol-0.1 Model Chemistry: a Neural Network Augmented with Long-Range Physics
preprint, January 2017

Yao, Kun; Herr, John E.; Toth, David W.
arXiv
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Deep Reinforcement Learning for De-Novo Drug Design
text, January 2017

Popova, Mariya; Isayev, Olexandr; Tropsha, Alexander
arXiv
DOI: 10.48550/arxiv.1711.10907

Extending the Accuracy of the SNAP Interatomic Potential Form
text, January 2017

Wood, Mitchell A.; Thompson, Aidan P.
arXiv
DOI: 10.48550/arxiv.1711.11131

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
text, January 2017

Fujikake, So; Deringer, Volker L.; Lee, Tae Hoon
arXiv
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SchNet - a deep learning architecture for molecules and materials
text, January 2017

Schütt, Kristof T.; Sauceda, Huziel E.; Kindermans, Pieter-Jan
arXiv
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Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment
text, January 2017

Herr, John E.; Yao, Kun; McIntyre, Ryker
arXiv
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Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
text, January 2018

Imbalzano, Giulio; Anelli, Andrea; é, Daniele Giofr
arXiv
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Adaptive coupling of a deep neural network potential to a classical force field
text, January 2018

Zhang, Linfeng; Wang, Han; E., Weinan
arXiv
DOI: 10.48550/arxiv.1806.01020

Machine-learned multi-system surrogate models for materials prediction
text, January 2018

Nyshadham, Chandramouli; Rupp, Matthias; Bekker, Brayden
arXiv
DOI: 10.48550/arxiv.1809.09203

Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
text, January 2018

Zhang, Linfeng; Lin, De-Ye; Wang, Han
arXiv
DOI: 10.48550/arxiv.1810.11890

Unsupervised machine learning in atomistic simulations, between predictions and understanding
text, January 2019

Ceriotti, Michele
arXiv
DOI: 10.48550/arxiv.1902.05158

A Novel Approach to Describe Chemical Environments in High Dimensional Neural Network Potentials
text, January 2019

Kocer, Emir; Mason, Jeremy K.; Erturk, Hakan
arXiv
DOI: 10.48550/arxiv.1907.02374

FCHL revisited: faster and more accurate quantum machine learning
text, January 2019

Christensen, Anders S.; Bratholm, Lars A.; Faber, Felix A.
arXiv
DOI: 10.48550/arxiv.1909.01946

Local invertibility and sensitivity of atomic structure-feature mappings
preprint, January 2021

Pozdnyakov, Sergey N.; Zhang, Liwei; Ortner, Christoph
arXiv
DOI: 10.48550/arxiv.2109.11440

Local invertibility and sensitivity of atomic structure-feature mappings
journal, January 2021

Pozdnyakov, Sergey N.; Zhang, Liwei; Ortner, Christoph
Open Research Europe, Vol. 1
DOI: 10.12688/openreseurope.14156.1

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Structure and dynamics of warm dense aluminum: a molecular dynamics study with density functional theory and deep potential
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Data sampling scheme for reproducing energies along reaction coordinates in high-dimensional neural network potentials
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Machine learning for interatomic potential models
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Machine-learned multi-system surrogate models for materials prediction
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Modelling of framework materials at multiple scales: current practices and open questions
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Use of molecular dynamics fingerprints (MDFPs) in SAMPL6 octanol–water log P blind challenge
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RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules
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Hammett neural networks: prediction of frontier orbital energies of tungsten–benzylidyne photoredox complexes
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Isotope effects in liquid water via deep potential molecular dynamics
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A novel approach to describe chemical environments in high-dimensional neural network potentials
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Machine learning molecular dynamics for the simulation of infrared spectra
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