Machine Learning for Materials & Molecules
We are the MIND Lab, based in the Department of Mechanical Engineering at Binghamton University. Our research integrates artificial intelligence, materials science, and computational chemistry to accelerate discovery and design of advanced materials for energy innovations, next-generation materials, and data-driven insights.
The group focuses on:
Generative AI for atomic and molecular design
We apply generative models—including diffusion models—to explore and generate structures like amorphous carbon, nanoporous catalysts, and grain boundaries.
Data-driven spectroscopy interpretation
We use neural networks to connect experimental spectra (e.g., XANES) with structural features in disordered materials.
Structure–property learning
Our models identify how molecular structure and composition affect key performance indicators like catalytic activity, hydrogen storage capacity, or degradability.
Environmentally relevant materials
We predict degradation pathways, bioactivity, and defluorination potential for persistent chemicals like PFAS.
Application-driven insights
Our research supports design goals in clean energy, advanced coatings, separations, and catalysis by linking atomistic modeling with real-world targets.
