158 citations · 227 across the 6 of their papers we have counts for
6 papers · 1 filter
AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculations
Penghui Yang, Zhonghan Zhang, Yue Li +6
Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting a…
Discovering physical mechanisms from experiment-simulation mismatches
Yue Li, Penghui Yang, Yushan Xiao +7
Scientific discovery often begins where observation and prediction disagree. As computation and machine learning survey chemical space, experiment-simulation mismatches are exposed…
MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys
Yanchen Deng, Chendong Zhao, Yixuan Li +10
The discovery of advanced metallic alloys is hindered by vast composition spaces, competing property objectives, and real-world constraints on manufacturability. Here we introduce…
Autonomous Multi-objective Alloy Design through Simulation-guided Optimization
Penghui Yang, Chendong Zhao, Bijun Tang +11
Alloy discovery is constrained by vast compositional spaces, competing objectives, and prohibitive experimental costs. Although simulations and machine learning have each accelerat…
Machine learning driven synthesis of few-layered WTe2
Manzhang Xu, Bijun Tang, Chao Zhu +14
Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device a…
Machine learning-guided synthesis of advanced inorganic materials
Bijun Tang, Yuhao Lu, Jiadong Zhou +6
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complex…