Publication date: Jul 18, 2022
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 H2 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.
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File name | Size | Description |
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training.zip
MD5md5:de93b69ab19392ac4900179c58d6dc9e
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201.8 MiB | Input and output files of the four on-the-fly training simulations described in the main text. |
md.zip
MD5md5:529b04c1c5352d8838ea4a9eafc627db
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107.2 MiB | LAMMPS input and output files of the molecular dynamics simulations described in the main text and the custom LAMMPS potential file used to generate the trajectories. |
readme.txt
MD5md5:6b468972d7a65440ebecb9ff36ec15fb
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311 Bytes | README file describing the directories. |
2022.92 (version v1) [This version] | Jul 18, 2022 | DOI10.24435/materialscloud:r0-84 |