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20202026
most citedGenerative deep learning as a tool for inverse design of high-entropy refractory alloys

52 citations · 167 across the 9 of their papers we have counts for

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cond-mat.mtrl-sci2020★ 40 cited

Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability employing Neural Networks

Adam M. Krajewski, Jonathan W. Siegel, Jinchao Xu +1

In the present paper, we introduce a new neural network-based tool for the prediction of formation energies of atomic structures based on elemental and structural features of Voron…