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20172026
most citedCrystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

2.9k citations · 3.8k across the 15 of their papers we have counts for

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Showing 2023Show all

2 papers · 1 filter

cond-mat.mtrl-sci2023★ 3 cited

MHub: Unlocking the Potential of Machine Learning for Materials Discovery

Yuanqi Du, Yingheng Wang, Yining Huang +6

We introduce MHub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especial…

cond-mat.supr-con2023★ 56 cited

Inverse Design of Next-generation Superconductors Using Data-driven Deep Generative Models

Daniel Wines, Tian Xie, Kamal Choudhary

Finding new superconductors with a high critical temperature () has been a challenging task due to computational and experimental costs. We present a diffusion model inspired…