Santanu Chaudhuri, PhD

Accelerated Materials Research Lab (AMRL) is working on advancing multiscale science and computational tools using atomistic-to-mesoscale modeling for energy, environment, and manufacturing applications. We focus on the design of polymers, coatings, composites, semiconductors, printable 2D materials, and alloys for different applications. We emphasize on predictive understanding of the role of interfaces in controlling the electrical, chemical, and mechanical properties/performance of materials and devices.

Research Interests

• First-Principles Theory: Density functional theory (DFT) and ab initio calculation of dynamics and reactivity of clusters, bulk periodic solids, and surfaces
• Molecular Dynamics: Classical, reactive, and first-principles molecular dynamics simulations
• Mesoscale and Multiphysics Modeling: Connection of molecular scale averages to the mesoscale using coarse-grained, discrete element, fluid dynamics and finite element models, multiphysics models of materials processing and manufacturing
• Data Science and Machine Learning: high-throughput DFT databased, automation of simulations and learning from molecular dynamics, machine learning for the acceleration of first-principles thermodynamic calculations, machine learning for materials property predictions,  reinforcement learning for steering of experiments and manufacturing processes.
• High-performance Computing: Integration of edge-to-exascale computing resources and machine learning acceleration hardware are crucial for leadership in advanced materials design, scale-up, and manufacturing