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Tensorial Properties via the Neuroevolution Potential Framework: Fast . . . We apply the resulting framework to construct models for the dipole moment, polarizability, and susceptibility of molecules, liquids, and solids and show that our approach compares favorably with several ML models from the literature with respect to accuracy and computational efficiency
Neuroevolution potential — GPUMD documentation Currently, GPUMD only supports NEP3 and NEP4, as NEP1 and NEP2 are deprecated Both versions are identical for single-component systems For multi-component systems, NEP4 usually has higher accuracy, if all the other hyperparameters are the same
Improving the accuracy of the neuroevolution machine learning potential . . . In a previous paper Fan et al (2021 Phys Rev B 104, 104309), we developed the neuroevolution potential (NEP), a framework of training neural network based machine-learning potentials using a natural evolution strategy and performing molecular dynamics (MD) simulations using the trained potentials
Efficient GPU-Accelerated Training of a Neuroevolution Potential with The GNEP training framework developed in this work is open source and available at https: github com hfood02 GNEP Molecular dynamics simulations were performed using the GPUMD package, available at https: github com brucefan1983 GPUMD
Neuroevolution Potential (NEP) Method - emergentmind. com The Neuroevolution Potential (NEP) method is a machine learning interatomic potential framework that combines local atomic environment descriptors with neural network regression, trained via a separable natural evolution strategy
GPUMD: A package for constructing accurate machine-learned . . . - PubMed We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in Fan et al [Phys Rev B 104, 104309 (2021)] and their implementation in the open-source package gpumd