I-Ta Hsieh

I am a Ph.D. candidate in Materials Science at Brown University, specializing in Computational Chemistry and Materials Science. My research combines quantum simulations, atomistic and multi-scale modeling, and machine learning to investigate defect kinetics, defect chemistry, and electrochemical properties in solid-state materials and hybrid organic–inorganic systems. I develop both theoretical models and data-driven approaches to elucidate the fundamental physical and chemical mechanisms that govern materials behavior, aiming to accelerate the development of next-generation technologies.

What I Do

Computational Materials Science

• Machine learning and data science for identifying chemical and mechanical properties in materials

• Developing new methods for computing mechanical, thermal, chemical, and transport properties of materials.

• Multi-scale modeling: Ab initio calculation, molecular dynamics simulation, Monte Carlo simulation, and finite difference.

Applied Math and Computer Science

• Physics-Informed Neural Networks (PINNs).

• The non-linear dynamics and the geometry in the neural network.

• High performance computation: parallel computing, quantum computing algorithm.

• Information security for web server.