1.8k citations · 1.9k across the 4 of their papers we have counts for
5 papers
Interpretable machine learning to understand the performance of semi local density functionals for materials thermochemistry
Santosh Adhikari, Christopher J. Bartel, Christopher Sutton
This study investigates the use of machine learning (ML) to correct the enthalpy of formation (Hf) from two separate DFT functionals, PBE and SCAN, to the experimental Hf across 10…
From structure mining to unsupervised exploration of atomic octahedral networks
R. Patrick Xian, Ryan J. Morelock, Ido Hadar +2
Understanding the spatial arrangements of atom-centered coordination octahedra is crucial for relating structures to properties for many materials families. Traditional case-by-cas…
Improving the reliability of machine learned potentials for modeling inhomogenous liquids
Kamron Fazel, Nima Karimitari, Tanooj Shah +2
The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical and biological process…
NOMAD 2018 Kaggle Competition: Solving Materials Science Challenges Through Crowd Sourcing
Christopher Sutton, Luca M. Ghiringhelli, Takenori Yamamoto +7
Machine learning (ML) is increasingly used in the field of materials science, where statistical estimates of computed properties are employed to rapidly examine the chemical space…
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…