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Atomic Energies from a Convolutional Neural Network
journal contribution
posted on 2018-05-29, 00:00 authored by Xin ChenXin Chen, Mathias S. Jørgensen, Jun Li, Bjørk HammerUnderstanding
interactions and structural properties at the atomic
level is often a prerequisite to the design of novel materials. Theoretical
studies based on quantum-mechanical first-principles calculations
can provide this knowledge but at an immense computational cost. In
recent years, machine learning has been successful in predicting structural
properties at a much lower cost. Here we propose a simplified structure
descriptor with no empirical parameters, “k-Bags”, together
with a scalable and comprehensive machine learning framework that
can deepen our understanding of atomic properties of structures. This
model can readily predict structure-energy relations that can provide
results close to the accuracy of ab initio methods. The model provides
chemically meaningful atomic energies enabling theoretical analysis
of organic and inorganic molecular structures. Utilization of the
local information provided by the atomic energies significantly improves
upon the stochastic steps in our evolutionary global structure optimization,
resulting in a much faster global minimum search of molecules, clusters,
and surfaced supported species.