4 papers · 1 filter
An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide
Ganesh Sivaraman, Leighanne Gallington, Anand Narayanan Krishnamoorthy +4
Understanding the structure and properties of refractory oxides are critical for high temperature applications. In this work, a combined experimental and simulation approach uses a…
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…
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…
Bayesian strategies for uncertainty quantification of the thermodynamic properties of materials
Noah H. Paulson, Elise Jennings, Marius Stan
Reliable models of the thermodynamic properties of materials are critical for industrially relevant applications that require a good understanding of equilibrium phase diagrams, th…