5 papers
Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability
Zhong Zhang, Giacomo Acciarini, Dario Izzo +2
Low-thrust trajectory design relies heavily on repeated evaluations of fuel consumption and transfer feasibility, which require expensive optimal control solutions. In this work, w…
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…