We explore different ways to simplify the evaluation of the smooth overlap of atomic positions (SOAP) many-body atomic descriptor [Bartók et al., Phys. Rev. B 87, 184115 (2013)]. Our aim is to improve the computational efficiency of SOAP-based similarity kernel construction. While these improved atomic descriptors can be used for general characterization and interpolation of atomic properties, their main target application is accelerated evaluation of machine-learning-based interatomic potentials within the Gaussian approximation potential (GAP) framework [Bartók et al., Phys. Rev. Lett. 104, 136403 (2010)]. We achieve this objective by expressing the atomic densities in an approximate separable form, which decouples the radial and angular channels. We then express the elements of the SOAP descriptor (i.e., the expansion coefficients for the atomic densities) in analytical form given a particular choice of radial basis set. Finally, we derive recursion formulas for the expansion coefficients. This new SOAP-based descriptor allows for tenfold speedups compared to previous implementations, while improving the stability of the radial expansion for distant atomic neighbors, without degradation of the interpolation power of GAP models.
PANNA 2.0: Efficient neural network interatomic potentials and new architectures
Scuola Internazionale Superiore di Studi Avanzati（的里雅斯特国际高等研究院）；University of California Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Nvidia Corporation（英伟达公司）；Harvard University（哈佛大学）
LATTE: an atomic environment descriptor based on Cartesian tensor contractions
Scuola Internazionale Superiore di Studi Avanzati（国际高等研究学院）；CNR-IOM DEMOCRITOS, Istituto Officina dei Materiali（国家研究委员会材料研究所DEMOCRITOS项目组）；NVIDIA Corporation（英伟达公司）
Massive Discovery of Low-Dimensional Materials from Universal Computational Strategy
Nanoscience Center, Department of Physics, University of Jyväskylä（于韦斯屈莱大学）；Department of Physics, Chalmers University of Technology（查尔姆斯理工大学）；Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu（奥卢大学）
Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
Center for Computational Science & Engineering, Schwarzman College of Computing, MIT（计算科学与工程中心，计算机科学学院，麻省理工学院）；Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick（预测建模中心，工程学院，沃里克大学）；NVIDIA
qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations
College of Physical Science and Technology, Bohai University（渤海大学物理科学与技术学院）；Suzhou Laboratory（苏州实验室）；Chalmers University of Technology（查尔姆斯理工大学）；Department of Materials Science and Engineering, Westlake University（西湖大学材料科学与工程系）；School of Science, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）理学院）；Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM), Department of Materials Science and Engineering, Southern University of Science and Technology（南方科技大学材料科学与工程系微/纳孔功能材料深圳市重点实验室）；Department of Materials Science and Engineering, City University of Hong Kong（香港城市大学材料科学与工程系）
