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Found 5 papers, 3 papers with code
Quantum materials for energy-efficient neuromorphic computing
Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data.
AtomSets -- A Hierarchical Transfer Learning Framework for Small and Large Materials Datasets
Here we leverage the transfer learning concept and the graph network deep learning framework and develop the AtomSets machine learning framework for consistent high model accuracy at both small and large materials data.
Feature Engineering
Transfer Learning
Materials Science
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
Automated Generation and Ensemble-Learned Matching of X-ray Absorption Spectra
We report the development of XASdb, a large database of computed reference X-ray absorption spectra (XAS), and a novel Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra.
Materials Science
