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cond-mat.mtrl-sci2022
Genetic programming-based learning of carbon interatomic potential for materials discovery
Andrew Eldridge, Alejandro Rodriguez, Ming Hu +1
Efficient and accurate interatomic potential functions are critical to computational study of materials while searching for structures with desired properties. Traditionally, poten…
cond-mat.mtrl-sci2020
Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks
Yong Zhao, Kunpeng Yuan, Yinqiao Liu +3
Materials representation plays a key role in machine learning based prediction of materials properties and new materials discovery. Currently both graph and 3D voxel representation…