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cond-mat.mtrl-sci2019
Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide
Ganesh Sivaraman, Anand Narayanan Krishnamoorthy, Matthias Baur +5
We propose a novel active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our act…
cond-mat.mtrl-sci2019
Quantified Uncertainty in Thermodynamic Modeling for Materials Design
Noah H Paulson, Brandon J Bocklund, Richard A Otis +2
Phase fractions, compositions and energies of the stable phases as a function of macroscopic composition, temperature, and pressure (X-T-P) are the principle correlations needed fo…