Continuous and optimally complete description of chemical environments using Spherical Bessel descriptors
Continuous and optimally complete description of chemical environments using Spherical Bessel descriptors
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DOI：
10.1063/1.5111045
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发表时间：
2020-01-01
期刊：
影响因子：
1.6
通讯作者：
Erturk, Hakan
中科院分区：
文献类型：
--
作者：
Kocer, Emir;Mason, Jeremy K.;Erturk, Hakan
最近，机器学习势已成为将电子结构方法的高精度与经典原子间势的速度相结合的候选者。机器学习潜力的一个重要组成部分是通过一组描述符对局部原子环境的描述。理想情况下，这些应该对物理系统的对称性保持不变，相对于原子位置（包括原子离开环境时）可两次微分，并且完全允许重构原子环境直至对称。更强的最佳完整性条件要求用尽可能少的描述符数量来满足完整性条件。有证据表明，最近提出的球面贝塞尔 (SB) 描述符的更新版本满足前两个属性和最佳完整性的必要条件。原子位置平滑重叠 (SOAP) 描述符和 Zernike 描述符是 SB 描述符的天然对应物，包含在内是为了进行比较。 SOAP 描述符的标准构造不满足最佳完整性的条件，而且计算速度比 SB 描述符慢一个数量级。 (c) 2020 年作者。除非另有说明，所有文章内容均根据知识共享署名 (CC BY) 许可证 (http://creativecommons.org/licenses/by/4.0/) 获得许可。
Recently, machine learning potentials have been advanced as candidates to combine the high-accuracy of electronic structure methods with the speed of classical interatomic potentials. A crucial component of a machine learning potential is the description of local atomic environments by some set of descriptors. These should ideally be invariant to the symmetries of the physical system, twice-differentiable with respect to atomic positions (including when an atom leaves the environment), and complete to allow the atomic environment to be reconstructed up to symmetry. The stronger condition of optimal completeness requires that the condition for completeness be satisfied with the minimum possible number of descriptors. Evidence is provided that an updated version of the recently proposed Spherical Bessel (SB) descriptors satisfies the first two properties and a necessary condition for optimal completeness. The Smooth Overlap of Atomic Position (SOAP) descriptors and the Zernike descriptors are natural counterparts of the SB descriptors and are included for comparison. The standard construction of the SOAP descriptors is shown to not satisfy the condition for optimal completeness and, moreover, is found to be an order of magnitude slower to compute than that of the SB descriptors. (c) 2020 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).