Neural network force field (NNFF) is a method for performing regression on atomic structure–force relationships, bypassing the expensive quantum mechanics calculations that prevent the execution of long ab initio quality molecular dynamics (MD) simulations. However, most NNFF methods for complex multi-element atomic systems indirectly predict atomic force vectors by exploiting just atomic structure rotation-invariant features and network-feature spatial derivatives, which are computationally expensive. Here, we show a staggered NNFF architecture that exploits both rotation-invariant and -covariant features to directly predict atomic force vectors without using spatial derivatives, and we demonstrate 2.2× NNFF–MD acceleration over a state-of-the-art C++ engine using a Python engine. This fast architecture enables us to develop NNFF for complex ternary- and quaternary-element extended systems composed of long polymer chains, amorphous oxide and surface chemical reactions. The rotation-invariant–covariant architecture described here can also directly predict complex covariant vector outputs from local environments, in other domains beyond computational material science.
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The atomic structure–force dataset (for systems A, B and C) is available through a Code Ocean compute capsule (https://doi.org/10.24433/CO.2788051.v1). The Python code for training the NNFF of the DCF approach of this work is available through a Code Ocean compute capsule (https://doi.org/10.24422/CO.2788051.v1).
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We thank S. Falkner and C. Cunha from the Bosch Center for Artificial Intelligence (BCAI) for feedback on NNFF algorithm accuracy improvement. This research used resources of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, which is supported by the Office of Science of the Department of Energy under contract DE-AC05-00OR22725. The research was partially funded by the Advanced Research Projects Agency – Energy (ARPA-E), US Department of Energy, under award no. DE-AR0000775.
The authors declare no competing interests.
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Mailoa, J.P., Kornbluth, M., Batzner, S. et al. A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems. Nat Mach Intell 1, 471–479 (2019). https://doi.org/10.1038/s42256-019-0098-0
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