Reconfigurable Electronic Materials Inspired by Nonlinear Neuron Dynamics

Raymundo Arróyave

Texas A&M University; Chemistry Dept.
(Co-Lead, Thrust X)

rarroyave@tamu.edu

Computational Materials Science Lab Website

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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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