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physics.comp-ph2023★ 1 cited
Band-gap regression with architecture-optimized message-passing neural networks
Tim Bechtel, Daniel T. Speckhard, Jonathan Godwin +1
Graph-based neural networks and, specifically, message-passing neural networks (MPNNs) have shown great potential in predicting physical properties of solids. In this work, we trai…
physics.comp-ph2023★ 1 cited
Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models
Daniel T. Speckhard, Christian Carbogno, Luca Ghiringhelli +3
The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ult…