1.8k citations · 2.1k across the 5 of their papers we have counts for
5 papers
Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification
Cameron Gruich, Varun Madhavan, Yixin Wang +1
It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimati…
Two-to-three dimensional transition in neutral gold clusters: the crucial role of van der Waals interactions and temperature
Bryan R. Goldsmith, Jacob Florian, Jin-Xun Liu +6
We predict the structures of neutral gas-phase gold clusters (, = 513) at finite temperatures based on free-energy calculations obtained by replica-exchange ab initio…
New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides
Christopher J. Bartel, Christopher Sutton, Bryan R. Goldsmith +4
Predicting the stability of the perovskite structure remains a longstanding challenge for the discovery of new functional materials for many applications including photovoltaics an…
Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery
Mario Boley, Bryan R. Goldsmith, Luca M. Ghiringhelli +1
Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or…
Uncovering structure-property relationships of materials by subgroup discovery
B. R. Goldsmith, M. Boley, J. Vreeken +2
Subgroup discovery (SGD) is presented here as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-scie…