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Download fileGeneral Atomic Neighborhood Fingerprint for Machine Learning-Based Methods
journal contribution
posted on 2019-06-06, 00:00 authored by Rohit Batra, Huan Doan Tran, Chiho Kim, James Chapman, Lihua Chen, Anand Chandrasekaran, Rampi RamprasadTo facilitate chemical
space exploration for material screening
or to accelerate computationally expensive first-principles simulations,
inexpensive surrogate models that capture electronic, atomistic, or
macroscopic materials properties have become an increasingly popular
tool over the last decade. The most fundamental quantity common across
all such machine learning (ML)-based methods is the fingerprint used to numerically represent a material or its structure. To increase
the learning capability of the ML methods, the common practice is
to construct fingerprints that satisfy the same symmetry relations
as displayed by the target material property of interest (for which
the ML model is being developed). Thus, in this work, we present a
general, simple, and elegant fingerprint that can be used to learn
different electronic/atomistic/structural properties, irrespective
of their scalar, vector, or tensorial nature. This fingerprint is
based on the concept of multipole terms and can be systematically
increased in sophistication to achieve a desired level of accuracy.
Using the examples of Al, C, and hafnia (HfO2), we demonstrate
the applicability of this fingerprint to easily classify different
atomistic environments, such as phases, surfaces, point defects, and
so forth. Furthermore, we demonstrate the generality and effectiveness
of this fingerprint by building an accurate, yet inexpensive, ML-based
potential energy model for the case of Al using a reference data set
that is obtained from density functional theory computations. Finally,
we note that the fingerprint definition presented here has applications
in fields beyond materials informatics, such as structure prediction,
identification of defects, and detection of new crystal phases.
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Keywords
ML modeltarget material propertysymmetry relationschemical space explorationstructure predictionML methodsmacroscopic materials propertiesmaterial screeningcrystal phasestheory computationsmultipole termsGeneral Atomic Neighborhood Fingerprintreference datamaterials informaticspoint defectstensorial natureMachine Learning-Based Methodsfingerprint definitionenergy modelatomistic environmentsfirst-principles simulations