Texas A&M University; Chemistry Dept.
(Co-Lead, Thrust X)
Arróyave is Presidential Impact Fellow, Chancellor EDGES Fellow, and Professor of Materials Science at Texas A&M. He is Director of an NSF Research Traineeship program that provides a template for interdisciplinary doctoral training that we will further expand under reMIND.
Research
Arróyave’s research contributions include discovery of transformation paths in phase-transforming materials, microstructure evolution simulations under multiple fields, and the development of efficient approaches for materials-design landscapes. As part of the reMIND team, he contributes to efforts in inverse design and develop materials design and Bayesian synthesis design approaches cutting across reMIND.
Publications
- Talapatra, A., Boluki, S., Duong, T., Qian, X., Dougherty, E., Arróyave, R. (2018). Autonomous efficient experiment design for materials discovery with Bayesian model averaging. Physical Review Materials, 2(11), 113803. doi: 10.1103/PhysRevMaterials.2.113803
- Couperthwaite, R., Molkeri, A., Khatamsaz, D., Srivastava, A., Allaire, D., Arròyave, R. (2020). Materials design through batch bayesian optimization with multisource information fusion. JOM, 72(12), 4431-4443. doi: 10.1007/s11837-020-04396-x
- Ye, J., Mahmoudi, M., Karayagiz, K., Johnson, L., Seede, R., Karaman, I., Arròyave, R., Elwany, A. (2022). Bayesian calibration of multiple coupled simulation models for metal additive manufacturing: A Bayesian network approach. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 8(1). doi: 10.1115/1.4052270
- Trehern, W., Ortiz-Ayala, R., Atli, K. C., Arroyave, R., & Karaman, I. (2022). Data-driven shape memory alloy discovery using Artificial Intelligence Materials Selection (AIMS) framework. Acta Materialia, 228, 117751. doi: 10.1016/j.actamat.2022.117751
- Wilson, N., Willhelm, D., Qian, X., Arróyave, R., & Qian, X. (2022). Batch active learning for accelerating the development of interatomic potentials. Computational Materials Science, 208, 111330. doi: 10.1016/j.commatsci.2022.111330