Efficient Structure-Informed Featurization and Property Prediction of Ordered, Dilute, and Random Atomic Structures
arxiv(2024)
摘要
Structure-informed materials informatics is a rapidly evolving discipline of
materials science relying on the featurization of atomic structures or
configurations to construct vector, voxel, graph, graphlet, and other
representations useful for machine learning prediction of properties,
fingerprinting, and generative design. This work discusses how current
featurizers typically perform redundant calculations and how their efficiency
could be improved by considering (1) fundamentals of crystallographic (orbits)
equivalency to optimize ordered cases and (2) representation-dependent
equivalency to optimize cases of dilute, doped, and defect structures with
broken symmetry. It also discusses and contrasts ways of (3) approximating
random solid solutions occupying arbitrary lattices under such representations.
Efficiency improvements discussed in this work were implemented within
pySIPFENN or python toolset for Structure-Informed Property and Feature
Engineering with Neural Networks developed by authors since 2019 and shown to
increase performance from 2 to 10 times for typical inputs. Throughout this
work, the authors explicitly discuss how these advances can be applied to
different kinds of similar tools in the community.
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