.png){kind=icon}
返回
A smooth basis for atomistic machine learning
.png){kind=icon}
OA
AI
DOI：10.1063/5.0124363.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learning frameworks based on correlations of interatomic positions begin with a discretized description of the density of other atoms in the neighborhood of each atom in the system. Symmetry considerations support the use of spherical harmonics to expand the angular dependence of this density, but there is, as of yet, no clear rationale to choose one radial basis over another. Here, we investigate the basis that results from the solution of the Laplacian eigenvalue problem within a sphere around the atom of interest. We show that this generates a basis of controllable smoothness within the sphere (in the same sense as plane waves provide a basis with controllable smoothness for a problem with periodic boundaries) and that a tensor product of Laplacian eigenstates also provides a smooth basis for expanding any higher-order correlation of the atomic density within the appropriate hypersphere. We consider several unsupervised metrics of the quality of a basis for a given dataset and show that the Laplacian eigenstate basis has a performance that is much better than some widely used basis sets and competitive with data-driven bases that numerically optimize each metric. Finally, we investigate the role of the basis in building models of the potential energy. In these tests, we find that a combination of the Laplacian eigenstate basis and target-oriented heuristics leads to equal or improved regression performance when compared to both heuristic and data-driven bases in the literature. We conclude that the smoothness of the basis functions is a key aspect of successful atomic density representations. (c) 2022 Author(s).
期刊
IF:
3.1
论文数:
7.2W
被引数:
23.2W
机构
引用论文
Job tenure and quality of work life of people with psychiatric disabilities working in social enterprises在社会企业工作的精神病患者的工作任期和工作生活质量
PREAI
分享收藏E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentialsE(3)-等变图神经网络，用于数据高效和精确的原子间势.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Atom-centered symmetry functions for constructing high-dimensional neural network potentials以原子为中心的对称函数构造高维神经网络势
PREAI
分享收藏Atomic cluster expansion for accurate and transferable interatomic potentials用于精确和可转移的原子间势的原子簇扩展PHYSICAL REVIEW B
PHYSICAL REVIEW BIF3.7
PREAI
分享收藏
查看更多内容