University at Buffalo (UB)
Wei Chen
PhD
Research Interests:
Data-driven and physics-based modeling of material systems; computational design of multi-principal element materials; materials informatics for accelerated materials and knowledge discovery
Research Interests
- Data-driven and physics-based modeling of material systems with synergistic integration of computational tools for predictive understanding of structure–property–processing relationships. We specialize in atomic and microscale modeling, statistical mechanics, CALPHAD approach, machine learning, and data-science methods.
- Computational design of multi-principal element materials, including high-entropy alloys and ceramics in their bulk or low-dimensional state, through tailored (dis)ordering and defect engineering across length scales for novel structural and functional properties. We are especially interested in creating manufacturing-aware material solutions from this broad design space for extreme-environment applications and sustainable development.
- Materials Informatics for accelerated materials and knowledge discovery enabled with autonomous material data-model pipelines and interpretable artificial intelligence. We develop the computational methodology and cyberinfrastructure to empower high-throughput data fusion and robust materials knowledge generation. Focus will be on applying causal inference and geometric learning for material discovery that spans across diverse materials classes.
- “EAGER: SSMCDAT2023: Data-driven Predictive Understanding of Oxidation Resistance in High-Entropy Alloy Nanoparticles”, National Science Foundation, DMR, PI, 2023-2025.
- “Co-designing Novel Memristor Heterostructures for Brain Inspired Computers”, National Science Foundation, Future of Semiconductors, Co-PI, 2023–2025.
- “CAREER: First-principles Predictive Understanding of Chemical Order in Complex Concentrated Alloys: Structures, Dynamics, and Defect Characteristics”, National Science Foundation, DMR–CMMT, PI, 2020–2025.
- “I-AIM: Interpretable Augmented Intelligence for Multiscale Material Discovery”, National Science Foundation, Harnessing Data Revolution, PI, 2019–2023.
- “Mechanical Activation Enhanced Solid-State Reaction and Electrochemical Properties of NaCrO2”, National Science Foundation, DMR–SSMC, Co-PI, 2017–2020.
- “Thermodynamic Measurements of Binary and Ternary Intermetallic Compounds”, National Science Foundation, DMR–MMN, Co-PI, 2016–2020.