2 papers
cond-mat.mtrl-sci2025
Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
Jiaxiang Li, Junwei Feng, Jie Luo +9
Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure predicti…
cond-mat.supr-con2024
Data-driven design of high-temperature superconductivity among ternary hydrides under pressure
Bowen Jiang, Xiaoshan Luo, Toshiaki Iitaka +6
Recently, ternary clathrate hydrides are promising candidates for high-temperature superconductor. However, it is a formidable challenge to effectively hunt high-temperature superc…