The search for high-temperature superconductors has advanced significantly in the last ten years, with many hydrogen-rich compounds exhibiting superconductivity above 200 K. However, their stability at extreme pressures limits practical applications. Theoretical prediction of new compounds is hindered by expensive calculations of critical temperatures (Tc)...
Structure-activity relationships describe trends between chemical features and subsequent function. As the modeled activity becomes more complex, the relationships drawn often rely on more specific features, inhibiting the realization of general chemical intuition. Notably, trends in stereoselective catalysis constitute one such relationship where complex interactions...
Abstract: We develop Dirichlet flow matching on the simplex based on mixtures of Dirichlet distributions as probability paths. In this framework, we derive a connection between the mixtures' scores and the flow's vector field that allows for classifier and classifier-free guidance. On DNA sequence generation...
Abstract: We develop a static quantum embedding scheme that utilizes different levels of approximations to coupled cluster (CC) theory for an active fragment region and its environment. In this approach, we solve the local fragment problem using a high-level CC method and address the environment...
Abstract: Renewable energy technologies such as lithium-ion batteries have become indispensable in our everyday lives. As current battery research pushes for higher performance and energy density, the operational safety of batteries becomes paramount. Under high voltages and temperatures, nonaqueous organic solvents in liquid-phase lithium-ion battery...
Operator learning has undergone rapid development in recent years and gradually emerged as a transformative machine-learning paradigm. In this talk, I will provide an overview of the operator-learning framework and its applications to address modern computational challenges, with a focus on quantum many-body dynamics. Using...
Recent advancements in machine learning have led to a series of breakthroughs in modeling high-dimensional probability densities, from generative modeling, sampling to stochastic optimal control. In this talk, I will discuss how these computational advancements lead to accelerating the sampling of transition paths in molecular...
The vast structural tunability and compositional diversity of multicomponent materials make them promising for a range of applications, yet the complexity of atomic ordering in such multinary materials, all the way from highly ordered elemental arrangements to fully disordered solid solutions, poses significant challenges in...
