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Found 2 papers, 2 papers with code
Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data
Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science.
Materials Science Disordered Systems and Neural Networks
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Similarly, we show that MEGNet models trained on $\sim 60, 000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.
Ranked #4 on
Formation Energy
on Materials Project
Drug Discovery
Formation Energy
Materials Science
Computational Physics
