Hao Tang

Hao Tang is a PhD candidate in materials science and engineering whose research is focused on developing and applying computational methods to simulate physical systems ranging from quantum defects and functional alloys to electronic devices. A second MathWorks Fellowship will enable Hao to extend his work, which draws significantly on MathWorks toolkits for modeling, simulations, data analysis, and visualization. His recent and current projects include: the refinement of a new reinforcement learning method to accelerate the atomistic simulation of defect diffusion; the application of an equivariant graph neural network to improve the accuracy of electronic structure calculations of molecules; the development of a computational method to calculate color centers’ quantum sensing performance; and the creation of new computational methods for evaluating the energy levels, wavefunctions, and lifetime of neutron bound states. Hao’s research has already delivered new discoveries and methods in computational materials science, and his ongoing work has strong potential to push the boundaries of his field while also offering computational tools of value to researchers in many disciplines.