2025-2026
09/24/25 Troy Van Voorhis
Fragment embedding as a tool for quantum chemistry (and maybe quantum computing)
Fragment embedding methods have a long history in chemistry. At a basic level, embedding methods divide a large system into many smaller fragments that can be simulated individually and then combined to make predictions about the whole system. In the first part of the talk, we will discuss recent advances in bootstrap embedding (BE) that allow the description of overlapping fragments. Using appropriately-sized fragments, BE is able to describe molecules and solids with complex unit cells containing hundreds of atoms while recovering ~99.9% of the correlation energy. We will discuss applications of the method to describe conducting polymers, 2D-materials and protein-ligand binding.
In the second part of the talk, we will discuss potential applications of quantum computing to chemistry. We will discuss the concrete reasons we should expect that quantum hardware will significantly impact the way computational chemistry operates in the longer term. We will explore the most likely near term applications (some of which involve fragment embedding ideas). We will finally discuss the ultimate limitations of quantum computing – things that one will not ever be able to do with quantum hardware. In particular, we will discuss a recent proof of a “no downfolding” theorem that implies significant limitations for both quantum computing and computational quantum chemistry.
11/05/25 Sijia Dong
Computational Strategies for Photoenzyme Design: Physics-Based Simulations, Data-Driven Approaches, and Quantum Computing
Photoenzymes are emerging protein-based photocatalysts that are repurposed from natural enzymes for non-natural reactions difficult for small-molecule catalysts. They exhibit extraordinary selectivity, scalability, and tunability, and offer a promising new toolbox for solar to chemical energy conversion and chemical synthesis. However, the understanding and design of photoenzymes pose several challenges. First, accurate first-principles simulations of the electronic structure of macromolecules are usually computationally expensive, especially those that involve strong electron correlation. In this talk, I will discuss our computational strategies, including data-driven methods and quantum computing, particularly quantum annealing, to tackle this challenge. Second, existing enzyme design strategies do not consider electronic excited states, and photoenzyme engineering has mainly relied on directed evolution. I will discuss our work on physics-informed computational photoenzyme design, where we combine physics-based simulations and data-driven methods to elucidate the design strategies for photoenzymes and other macromolecular photocatalysts.
11/12/25 Lucas Bao
Real-Time Surrogate-Driven Exploration of Structures, Reaction Paths, Adiabatic and Non-Adiabatic Dynamics
Accurate predictions of molecular structures and reaction pathways often rely on expensive ab initio electronic-structure calculations, with costs that grow steeply in ab initio molecular dynamics (AIMD). While neural-network potential energy surfaces (PESs) offer relief, they are data-intensive: they typically demand substantial training sets, significant pretraining effort, and careful, representative selection of training structures. This talk presents a data-efficient alternative: on-the-fly construction and exploration of PESs using nonparametric Bayesian surrogates that require no pretraining. Our framework embeds prior physical knowledge through non-constant prior mean functions (e.g., simple physical models or low-level semiempirical Hamiltonian), yielding a physically informed, real-time learner with built-in uncertainty quantification to select the next ab initio queries. We show that this surrogate-driven approach substantially reduces electronic-structure calls for geometry optimization, conformational ensemble refinement, reaction path determination (gas, bulk, and interfaces), and excited-state photodynamics, while producing on-the-fly surrogate PESs that closely track ground-truth surfaces. These surrogates can then be reused as hierarchical priors for higher-level quantum-chemistry tasks or as guidance for steering nuclear motion along dynamical trajectories beyond the Born-Oppenheimer approximation.
11/19/25 Mingyang Lu
Computational Modeling of Gene Regulatory Networks Driving Cell State Transitions
My lab focuses on developing computational methodologies that reveal how gene regulatory networks control cell state transitions during normal development and disease progression. By integrating top-down bioinformatics with bottom-up systems biology modeling, we aim to elucidate the interplay between gene network topology, single-cell gene expression dynamics, and cell phenotypic transitions. A cornerstone of our work is an ensemble-based mathematical modeling framework, RACIPE (Random Circuit Perturbation), which generates ensembles of models directly from gene circuit topology by randomly sampling kinetic parameters. This framework enables unbiased exploration of gene expression states without specifying kinetic parameters or parameter fitting and reveals biologically relevant states determined by network structure.
In the first part of my presentation, I will describe the methodological foundation and generalizations of RACIPE, including extensions for modeling networks with multiple regulatory types such as microRNA-based regultion, incorporation of intrinsic and extrinsic noise, and applications in circuit motif analysis and gene network optimization. While our studies are primarily computational and numerical, they raise several theoretical questions relevant to statistical physics and nonlinear dynamics that may be of interest to the audience.
In the second part, I will discuss two major applications of our recent work: (1) network coarse-graining methods that capture essential dynamics in a reduced gene circuit without requiring an explicit Hamiltonian, and (2) dissection of reversible and irreversible cell state transitions by using single-cell gene expression profiles and network topology. Together, these studies illustrate how our computational modeling framework reveals the design principles of gene regulation and offers new insights into the mechanisms that govern cell fate determination.
In the first part of my presentation, I will describe the methodological foundation and generalizations of RACIPE, including extensions for modeling networks with multiple regulatory types such as microRNA-based regultion, incorporation of intrinsic and extrinsic noise, and applications in circuit motif analysis and gene network optimization. While our studies are primarily computational and numerical, they raise several theoretical questions relevant to statistical physics and nonlinear dynamics that may be of interest to the audience.
In the second part, I will discuss two major applications of our recent work: (1) network coarse-graining methods that capture essential dynamics in a reduced gene circuit without requiring an explicit Hamiltonian, and (2) dissection of reversible and irreversible cell state transitions by using single-cell gene expression profiles and network topology. Together, these studies illustrate how our computational modeling framework reveals the design principles of gene regulation and offers new insights into the mechanisms that govern cell fate determination.
12/10/25 Xinqiang Ding
Free Energy Calculations Meet Machine Learning and Bayesian Statistics
Computing free energy differences is needed in many areas of science and engineering. For physical and chemical processes, free energy determines the relative stability of different states, which makes it a key quantity in understanding the thermodynamics of a process. Although the concept of free energy originates from thermodynamics, it reaches beyond and is frequently encountered in fields such as machine learning (ML) and Bayesian statistics. Because of its broad importance, researchers from various fields have been developing methods to calculate free energy. The benefit of having researchers from various fields working on similar problems is that new ideas and methods can be borrowed and adapted from one field to another. In ML, computing free energy is often not the primary objective but rather an intermediate step required for training or evaluating probabilistic models. When training probabilistic models of data in ML, the repeated computation of free energy becomes a significant computational bottleneck. This challenge has motivated the development of ML methods and training algorithms that circumvent the need for free energy calculations altogether. Interestingly, these models and methods, originally designed to avoid computing free energies, have proven to be effective tools for estimating free energy themselves. In part I of the talk, I will first discuss classical methods for computing free energy. I will then discuss how to leverage recent generative models in ML for efficient free energy estimation. In part II of the talk, I will introduce a Bayesian statistical framework for free energy calculation and show how it can be used to improve the accuracy of free energy estimates by incorporating prior knowledge in a principled manner.
