1.6k citations · 2.1k across the 5 of their papers we have counts for
5 papers
A Universal Machine Learning Model for Elemental Grain Boundary Energies
Weike Ye, Hui Zheng, Chi Chen +1
The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk…
Accelerating Materials Discovery with Bayesian Optimization and Graph Deep Learning
Yunxing Zuo, Mingde Qin, Chi Chen +4
Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equi…
Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data
Chi Chen, Yunxing Zuo, Weike Ye +2
Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a new pa…
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chi Chen, Weike Ye, Yunxing Zuo +2
Graph networks are a new machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization. Here, we develop universal MatErials Graph Network…
Deep Neural Networks for Accurate Predictions of Garnet Stability
Weike Ye, Chi Chen, Zhenbin Wang +2
Predicting the stability of crystals is one of the central problems in materials science. Today, density functional theory (DFT) calculations are the computational tool of choice t…