Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt
Abstract
Abstract Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H 2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.
- Publication Date:
- Research Org.:
- Harvard Univ., Cambridge, MA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)
- OSTI Identifier:
- 1885166
- Alternate Identifier(s):
- OSTI ID: 1904468
- Grant/Contract Number:
- SC0020128; SC0012573; AC02-05CH11231; 1808162; 2003725; DGE1745303
- Resource Type:
- Published Article
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Name: Nature Communications Journal Volume: 13 Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 42 ENGINEERING; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; computational methods; molecular dynamics
Citation Formats
Vandermause, Jonathan, Xie, Yu, Lim, Jin Soo, Owen, Cameron J., and Kozinsky, Boris. Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt. United Kingdom: N. p., 2022.
Web. doi:10.1038/s41467-022-32294-0.
Vandermause, Jonathan, Xie, Yu, Lim, Jin Soo, Owen, Cameron J., & Kozinsky, Boris. Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt. United Kingdom. https://doi.org/10.1038/s41467-022-32294-0
Vandermause, Jonathan, Xie, Yu, Lim, Jin Soo, Owen, Cameron J., and Kozinsky, Boris. Fri .
"Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt". United Kingdom. https://doi.org/10.1038/s41467-022-32294-0.
@article{osti_1885166,
title = {Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt},
author = {Vandermause, Jonathan and Xie, Yu and Lim, Jin Soo and Owen, Cameron J. and Kozinsky, Boris},
abstractNote = {Abstract Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H 2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.},
doi = {10.1038/s41467-022-32294-0},
journal = {Nature Communications},
number = 1,
volume = 13,
place = {United Kingdom},
year = {Fri Sep 02 00:00:00 EDT 2022},
month = {Fri Sep 02 00:00:00 EDT 2022}
}
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1038/s41467-022-32294-0
https://doi.org/10.1038/s41467-022-32294-0
Other availability
Search WorldCat to find libraries that may hold this journal
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
U1 snRNP regulates cancer cell migration and invasion in vitro
journal, January 2020
- Oh, Jung-Min; Venters, Christopher C.; Di, Chao
- Nature Communications, Vol. 11, Issue 1
Generalized Gradient Approximation Made Simple
journal, October 1996
- Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias
- Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868
Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water
journal, March 2021
- Yang, Manyi; Bonati, Luigi; Polino, Daniela
- Catalysis Today
Heterogeneous Catalysis: A Central Science for a Sustainable Future
journal, March 2017
- Friend, Cynthia M.; Xu, Bingjun
- Accounts of Chemical Research, Vol. 50, Issue 3
Origins of structural and electronic transitions in disordered silicon
journal, January 2021
- Deringer, Volker L.; Bernstein, Noam; Csányi, Gábor
- Nature, Vol. 589, Issue 7840
Atomic cluster expansion for accurate and transferable interatomic potentials
journal, January 2019
- Drautz, Ralf
- Physical Review B, Vol. 99, Issue 1
The atomic simulation environment—a Python library for working with atoms
journal, June 2017
- Hjorth Larsen, Ask; Jørgen Mortensen, Jens; Blomqvist, Jakob
- Journal of Physics: Condensed Matter, Vol. 29, Issue 27
Adsorption of hydrogen on a Pt(111) surface
journal, February 1976
- Christmann, K.; Ertl, G.; Pignet, T.
- Surface Science, Vol. 54, Issue 2
On representing chemical environments
journal, May 2013
- Bartók, Albert P.; Kondor, Risi; Csányi, Gábor
- Physical Review B, Vol. 87, Issue 18
Fast Parallel Algorithms for Short-Range Molecular Dynamics
journal, March 1995
- Plimpton, Steve
- Journal of Computational Physics, Vol. 117, Issue 1
Less is more: Sampling chemical space with active learning
journal, June 2018
- Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas
- The Journal of Chemical Physics, Vol. 148, Issue 24
On-the-fly machine learning force field generation: Application to melting points
journal, July 2019
- Jinnouchi, Ryosuke; Karsai, Ferenc; Kresse, Georg
- Physical Review B, Vol. 100, Issue 1
Affinity and Selectivity Assessment of Covalent Inhibitors by Free Energy Calculations
journal, December 2020
- Mihalovits, Levente M.; Ferenczy, György G.; Keserű, György M.
- Journal of Chemical Information and Modeling, Vol. 60, Issue 12
A benchmark database for adsorption bond energies to transition metal surfaces and comparison to selected DFT functionals
journal, October 2015
- Wellendorff, Jess; Silbaugh, Trent L.; Garcia-Pintos, Delfina
- Surface Science, Vol. 640
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
journal, March 2015
- Thompson, A. P.; Swiler, L. P.; Trott, C. R.
- Journal of Computational Physics, Vol. 285
High-dimensional neural network potentials for metal surfaces: A prototype study for copper
journal, January 2012
- Artrith, Nongnuch; Behler, Jörg
- Physical Review B, Vol. 85, Issue 4
Quasielastic helium atom scattering measurements of microscopic diffusional dynamics of H and D on the Pt(111) surface
journal, July 1999
- Graham, Andrew P.; Menzel, Alexander; Toennies, J. Peter
- The Journal of Chemical Physics, Vol. 111, Issue 4
SchNet – A deep learning architecture for molecules and materials
journal, June 2018
- Schütt, K. T.; Sauceda, H. E.; Kindermans, P. -J.
- The Journal of Chemical Physics, Vol. 148, Issue 24
Atomistic Adsorption of Oxygen and Hydrogen on Platinum Catalysts by Hybrid Grand Canonical Monte Carlo/Reactive Molecular Dynamics
journal, April 2016
- Gai, Lili; Shin, Yun Kyung; Raju, Muralikrishna
- The Journal of Physical Chemistry C, Vol. 120, Issue 18
Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
journal, May 2021
- Park, Cheol Woo; Kornbluth, Mordechai; Vandermause, Jonathan
- npj Computational Materials, Vol. 7, Issue 1
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
journal, January 2016
- Shapeev, Alexander V.
- Multiscale Modeling & Simulation, Vol. 14, Issue 3
Guidelines to Achieving High Selectivity for the Hydrogenation of α,β-Unsaturated Aldehydes with Bimetallic and Dilute Alloy Catalysts: A Review
journal, October 2020
- Luneau, Mathilde; Lim, Jin Soo; Patel, Dipna A.
- Chemical Reviews, Vol. 120, Issue 23
On the dynamics of H 2 adsorption on the Pt(111) surface: KRAUS and FRANK
journal, May 2017
- Kraus, Peter; Frank, Irmgard
- International Journal of Quantum Chemistry, Vol. 117, Issue 17
Development and testing of a general amber force field
journal, January 2004
- Wang, Junmei; Wolf, Romain M.; Caldwell, James W.
- Journal of Computational Chemistry, Vol. 25, Issue 9
Atomic cluster expansion: Completeness, efficiency and stability
journal, April 2022
- Dusson, Geneviève; Bachmayr, Markus; Csányi, Gábor
- Journal of Computational Physics, Vol. 454
Assessing the performance of recent density functionals for bulk solids
journal, April 2009
- Csonka, Gábor I.; Perdew, John P.; Ruzsinszky, Adrienn
- Physical Review B, Vol. 79, Issue 15
The resurgence of covalent drugs
journal, April 2011
- Singh, Juswinder; Petter, Russell C.; Baillie, Thomas A.
- Nature Reviews Drug Discovery, Vol. 10, Issue 4
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems
journal, August 2017
- Behler, Jörg
- Angewandte Chemie International Edition, Vol. 56, Issue 42
sGDML: Constructing accurate and data efficient molecular force fields using machine learning
journal, July 2019
- Chmiela, Stefan; Sauceda, Huziel E.; Poltavsky, Igor
- Computer Physics Communications, Vol. 240
De novo exploration and self-guided learning of potential-energy surfaces
journal, October 2019
- Bernstein, Noam; Csányi, Gábor; Deringer, Volker L.
- npj Computational Materials, Vol. 5, Issue 1
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
Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference
journal, June 2019
- Jinnouchi, Ryosuke; Lahnsteiner, Jonathan; Karsai, Ferenc
- Physical Review Letters, Vol. 122, Issue 22
On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations
journal, July 2020
- Jinnouchi, Ryosuke; Miwa, Kazutoshi; Karsai, Ferenc
- The Journal of Physical Chemistry Letters, Vol. 11, Issue 17
Machine Learning Estimates of Natural Product Conformational Energies
journal, January 2014
- Rupp, Matthias; Bauer, Matthias R.; Wilcken, Rainer
- PLoS Computational Biology, Vol. 10, Issue 1
Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide
journal, July 2020
- Sivaraman, Ganesh; Krishnamoorthy, Anand Narayanan; Baur, Matthias
- npj Computational Materials, Vol. 6, Issue 1
Evolution of Metastable Structures at Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics
journal, August 2020
- Lim, Jin Soo; Vandermause, Jonathan; van Spronsen, Matthijs A.
- Journal of the American Chemical Society, Vol. 142, Issue 37
The ReaxFF reactive force-field: development, applications and future directions
journal, March 2016
- Senftle, Thomas P.; Hong, Sungwook; Islam, Md Mahbubul
- npj Computational Materials, Vol. 2, Issue 1
Quantum and classical dynamics of reactive scattering of H 2 from metal surfaces
journal, January 2016
- Kroes, Geert-Jan; Díaz, Cristina
- Chemical Society Reviews, Vol. 45, Issue 13
Hydrogen and Deuterium Exchange on Pt(111) and Its Poisoning by Carbon Monoxide Studied by Surface Sensitive High-Pressure Techniques
journal, October 2006
- Montano, Max; Bratlie, Kaitlin; Salmeron, Miquel
- Journal of the American Chemical Society, Vol. 128, Issue 40
A Molecular Dynamics Study for Dissociation of H2 Molecule on Pt(111) Surface
journal, January 2012
- Tokumasu, Takashi
- Advanced Materials Research, Vol. 452-453
A transferable active-learning strategy for reactive molecular force fields
journal, January 2021
- Young, Tom A.; Johnston-Wood, Tristan; Deringer, Volker L.
- Chemical Science, Vol. 12, Issue 32
Computational aspects of many-body potentials
journal, May 2012
- Plimpton, Steven J.; Thompson, Aidan P.
- MRS Bulletin, Vol. 37, Issue 5
Ab initio molecular dynamics for liquid metals
journal, December 1995
- Kresse, G.
- Journal of Non-Crystalline Solids, Vol. 192-193
On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events
journal, March 2020
- Vandermause, Jonathan; Torrisi, Steven B.; Batzner, Simon
- npj Computational Materials, Vol. 6, Issue 1
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
ReaxFF: A Reactive Force Field for Hydrocarbons
journal, October 2001
- van Duin, Adri C. T.; Dasgupta, Siddharth; Lorant, Francois
- The Journal of Physical Chemistry A, Vol. 105, Issue 41
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007
- Behler, Jörg; Parrinello, Michele
- Physical Review Letters, Vol. 98, Issue 14
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
journal, April 2010
- Bartók, Albert P.; Payne, Mike C.; Kondor, Risi
- Physical Review Letters, Vol. 104, Issue 13
Surface and Subsurface Hydrogen: Adsorption Properties on Transition Metals and Near-Surface Alloys
journal, March 2005
- Greeley, Jeff; Mavrikakis, Manos
- The Journal of Physical Chemistry B, Vol. 109, Issue 8
Parallel reactive molecular dynamics: Numerical methods and algorithmic techniques
journal, April 2012
- Aktulga, H. M.; Fogarty, J. C.; Pandit, S. A.
- Parallel Computing, Vol. 38, Issue 4-5
A systematic DFT study of hydrogen diffusion on transition metal surfaces
journal, September 2012
- Kristinsdóttir, Lilja; Skúlason, Egill
- Surface Science, Vol. 606, Issue 17-18
Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning
journal, January 2009
- Handley, Chris M.; Hawe, Glenn I.; Kell, Douglas B.
- Physical Chemistry Chemical Physics, Vol. 11, Issue 30
Hydrogen storage in carbon nanostructures via spillover
journal, November 2016
- Pyle, Darryl S.; Gray, E. MacA.; Webb, C. J.
- International Journal of Hydrogen Energy, Vol. 41, Issue 42
Molecular adsorption at Pt(111). How accurate are DFT functionals?
journal, January 2015
- Gautier, Sarah; Steinmann, Stephan N.; Michel, Carine
- Physical Chemistry Chemical Physics, Vol. 17, Issue 43
Machine learning based interatomic potential for amorphous carbon
journal, March 2017
- Deringer, Volker L.; Csányi, Gábor
- Physical Review B, Vol. 95, Issue 9
Active learning of uniformly accurate interatomic potentials for materials simulation
journal, February 2019
- Zhang, Linfeng; Lin, De-Ye; Wang, Han
- Physical Review Materials, Vol. 3, Issue 2
Active learning of linearly parametrized interatomic potentials
journal, December 2017
- Podryabinkin, Evgeny V.; Shapeev, Alexander V.
- Computational Materials Science, Vol. 140
Theoretical prediction of high melting temperature for a Mo–Ru–Ta–W HCP multiprincipal element alloy
journal, January 2021
- Hong, Qi-Jun; Schroers, Jan; Hofmann, Douglas
- npj Computational Materials, Vol. 7, Issue 1