The BIDMaP Emerging Scholars Seminar is a dynamic, biweekly forum that brings together emerging researchers—graduate students and postdoctoral scholars—at the cutting edge of physics, chemistry, and materials science to explore how artificial intelligence is reshaping these fields. Each session features a scholar presenting their latest research, with a focus on how AI is transforming our ability to understand, design, and manipulate complex materials and systems.

This seminar offers a unique platform for cross-disciplinary dialogue, where experts from physics, materials science, and computational research converge to share novel developments and applications in materials simulations and AI-driven approaches. The goal is to foster collaboration between BIDMaP postdoctoral fellows and researchers in related fields, sparking fresh ideas and pushing the boundaries of current scientific inquiry.

Past Seminars

25
Mar
trinidad

Trinidad Novoa - Hydrogen-rich superconductors: efficient estimations of Tc from real-space descriptors

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)...
11
Mar
Andrew

Andrew Smith – Simple generalizations in stereoselective catalysis enable the algorithmic discovery of new enantioselective solutions

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...
25
Feb
duo

Hannes Stärk and Bowen Jing – Dirichlet Flow Matching with Applications to DNA Sequence Design

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...
11
Feb
Avijit

Avijit Shee – A static quantum embedding scheme based on coupled cluster theory

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...
28
Jan
Jingyang

Jingyang Wang - Elucidating gas reduction effects of organosilicon additives in lithium-ion batteries

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...
10
Dec
yuan

Yuanran Zhu – Operator-learning approach for predicting nonequilibrium Green's function dynamics

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...
19
Nov
Yuanqi

Yuanqi Du – Accelerating Transition Dynamics Simulation with Recent Advances in Probabilistic Machine Learning

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...
17
Nov
Jiayu

Jiayu Peng – Predicting the Chemical (Dis)order in Multicomponent Materials with High-Throughput Simulations and Representation Learning

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...