John Bonini is a researcher focused on developing and applying first principles and data-driven methods for modeling and predicting properties in material and chemical systems. His work aims to connect a system’s microscopic structure to its useful properties, such as dielectric, piezoelectric, ferroelectric, and hall effect properties, which are essential for various sensors, motors, and memory devices.
Bonini’s research involves studying magnon-phonon coupling in strong spin-orbit coupling systems and strong light-matter coupling in optical cavities, resulting in “polaritonic” excitations. He is also interested in multi-scale approaches that use parameters from smaller simulations to model larger scale properties, enabling the simulation of a wider variety of systems in a more realistic manner.
Bonini’s methods are based on first principles quantum simulations, primarily using density functional theory (DFT) calculations. He is currently working on combining these established physics-informed methods with machine learning tools, particularly automatic differentiation, to extend the range of physics they can model and efficiently quantify the sensitivity of results to simulation parameters.
Source: https://www.nist.gov/people/john-bonini
Keywords: Density Functional Theory, Quantum Simulations, Machine Learning, Density Functional Perturbation Theory, Wannierization
