165 citations · 193 across the 8 of their papers we have counts for
14 papers · 1 filter
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…
AtomSets -- A Hierarchical Transfer Learning Framework for Small and Large Materials Datasets
Chi Chen, Shyue Ping Ong
Predicting materials properties from composition or structure is of great interest to the materials science community. Deep learning has recently garnered considerable interest in…
Bridging the Gap Between Simulated and Experimental Ionic Conductivities in Lithium Superionic Conductors
Ji Qi, Swastika Banerjee, Yunxing Zuo +5
Lithium superionic conductors (LSCs) are of major importance as solid electrolytes for next-generation all-solid-state lithium-ion batteries. While molecular dynamics…
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…
Genetic Algorithm-Guided Deep Learning of Grain Boundary Diagrams: Addressing the Challenge of Five Degrees of Freedom
Chongze Hu, Yunxing Zuo, Chi Chen +2
Grain boundaries (GBs) often control the processing and properties of polycrystalline materials. Here, a potentially transformative research is represented by constructing GB prope…