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The Materials Intelligence Research (MIR) group at Harvard University is working to:
Develop computational methods and implement software to understand, design and discover materials, starting with their first-principles quantum description
Combine solid-state physics, computational chemistry, and mathematics with machine learning
Study electronic structure and transport, surface reactions, phase transitions, correlated diffusion in liquids and solids, and other materials phenomena relevant for energy storage and conversion.
Deep equivariant neural networks, Bayesian force fields, active learning, exascale reactive MD
Solid-state, ionic liquid, polymer electrolytes, correlated transport
Dynamics of nanoparticles, surface restructuring, heterogeneous reactions, 2D materials
Electrical conductivity, thermoelectric and magneto-transport properties.
Machine learning for development of fast and accurate density functionals
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