@article{424b67ba71824d299a1a5c5bb21496ca,
title = "Active learning of uniformly accurate interatomic potentials for materials simulation",
abstract = "An active learning procedure called deep potential generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: Exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg, and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.",
author = "Linfeng Zhang and Lin, {De Ye} and Han Wang and Roberto Car and Weinan E",
note = "Funding Information: The work of L.Z. and W.E is supported in part by Major Program of NNSFC under Grant No. 91130005, ONR Grant No. N00014-13-1-0338, and NSFC Grant No. U1430237. The work of L.Z. and R.C. is supported in part by the DOE with Award No. DE-SC0019394. The work of H.W. is supported by the National Science Foundation of China under Grants No. 11501039, No. 11871110, and No. 91530322, and the National Key Research and Development Program of China under Grants No. 2016YFB0201200 and No. 2016YFB0201203. The work of D.Y.L. and H.W. is supported by the Science Challenge Project No. JCKY2016212A502. We are grateful for computing time provided in part by the National Energy Research Scientific Computing Center (NERSC), the Terascale Infrastructure for Groundbreaking Research in Science and Engineering (TIGRESS) High Performance Computing Center and Visualization Laboratory at Princeton University, the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund under Grant No. U1501501. Publisher Copyright: {\textcopyright} 2019 American Physical Society.",
year = "2019",
month = feb,
day = "25",
doi = "10.1103/PhysRevMaterials.3.023804",
language = "English (US)",
volume = "3",
journal = "Physical Review Materials",
issn = "2475-9953",
publisher = "American Physical Society",
number = "2",
}