{"id":511,"date":"2025-07-28T22:28:35","date_gmt":"2025-07-29T02:28:35","guid":{"rendered":"https:\/\/sites.bu.edu\/theochem\/?page_id=511"},"modified":"2026-04-08T11:30:02","modified_gmt":"2026-04-08T15:30:02","slug":"2025-2026","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/theochem\/events\/2025-2026\/","title":{"rendered":"2025-2026"},"content":{"rendered":"

09\/24\/25 Troy Van Voorhis<\/h2>
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Fragment embedding as a tool for quantum chemistry (and maybe quantum computing) <\/h3>\n
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Department of Chemistry, Massachusetts Institute of Technology<\/a><\/p>\n<\/div>\n

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. <\/p>\n

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 \u201cno downfolding\u201d theorem that implies significant limitations for both quantum computing and computational quantum chemistry.\n<\/p><\/div>\n<\/div>\n

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11\/05\/25 Sijia Dong<\/h2>
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Computational Strategies for Photoenzyme Design: Physics-Based Simulations, Data-Driven Approaches, and Quantum Computing <\/h3>\n
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Department of Chemistry and Chemical Biology, Northeastern University<\/a><\/p>\n<\/div>\n

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. <\/div>\n<\/div>\n

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11\/12\/25 Lucas Bao<\/h2>
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Real-Time Surrogate-Driven Exploration of Structures, Reaction Paths, Adiabatic and Non-Adiabatic Dynamics <\/h3>\n
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Department of Chemistry, Boston College<\/a><\/p>\n<\/div>\n

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. <\/div>\n<\/div>\n

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11\/19\/25 Mingyang Lu<\/h2>
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Computational Modeling of Gene Regulatory Networks Driving Cell State Transitions <\/h3>\n
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Department of Bioengineering, Northeastern University<\/a><\/p>\n<\/div>\n

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.
\nIn 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.
\nIn 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.\n<\/div>\n<\/div>\n

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12\/10\/25 Xinqiang Ding<\/h2>
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Free Energy Calculations Meet Machine Learning and Bayesian Statistics <\/h3>\n
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Department of Chemistry, Tufts University<\/a><\/p>\n<\/div>\n

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. <\/div>\n<\/div>\n

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02\/04\/26 Jason Green<\/h2>
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Nonequilibrium statistical mechanics of dynamically functional materials: problems and principles <\/h3>\n
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Department of Chemistry, University of Massachusetts Boston<\/a><\/p>\n<\/div>\n

By assembling supramolecular materials through synthetic chemical reaction networks, it is becoming possible to mimic some of the remarkable behaviors of biological systems. An influx of reagents can sustain materials that function dynamically–responding to stimuli, converting free energy into mechanical work, and self-healing after perturbation. However, these dissipative materials operate far from equilibrium and require the continuous consumption of energy, introducing fundamental challenges in designing and controlling how effectively such functions are executed. In these systems, molecular-scale chemical reaction networks govern the dissipative assembly and disassembly of mesoscale structures. As a result, material function–and even material lifetime–is often transient, with experiments revealing assembly and response processes that unfold over finite timescales. This tight coupling of dynamics across scales places these materials beyond the scope of traditional thermodynamic descriptions and motivates new approaches to nonequilibrium statistical mechanics. From the geometry of their irreversible dynamics, two promising design principles emerge.<\/p>\n

First, dissipative materials must balance how fast a function is executed against how much energy is consumed in the process. In the first part of the talk, I will introduce thermodynamic speed limits on dissipation that quantify this trade\u0002off. I will discuss recent work suggesting that these bounds can serve as a design principle spanning length and timescales. These speed limits share theoretical connections with regression analysis and, by extension, existing machine learning techniques, offering a potential framework for optimizing energy efficiency and the timing of structure formation in dissipative materials.<\/p>\n

Second, controlling the dynamics of these materials requires designing their nonequilibrium response from the underlying chemical kinetics. In the second part of the talk, I will discuss how open reaction networks can transiently amplify small fluctuations in concentrations and produce large, spontaneous increases in the yield of assembled material. After a small dose of reagent, the chemical kinetics do not simply relax toward a steady state; instead, they can evolve further away from equilibrium and spontaneously assemble thermodynamically unstable products. For a model supramolecular system, this mechanism can produce 400% more material than the steady state yield. By deriving the conditions for this transient amplification, I will show that steady states that break detailed balance can support stronger growth and higher yields. The precise conditions for this behavior arise from the nonnormal stability of these steady states and provide a route to exploit fluctuations in dissipative materials. <\/p><\/div>\n<\/div>\n

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02\/11\/26 Yu-Shan Lin<\/h2>
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Structure prediction of cyclic peptides via molecular dynamics and machine learning <\/h3>\n
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Department of Chemistry, Tufts University<\/a><\/p>\n<\/div>\n

A robust ability to target specific protein\u2013protein interactions (PPIs) and protein surfaces, both intracellular and extracellular, would provide control of diverse cellular functions for both fundamental research and therapeutic intervention. However, PPIs and protein surfaces are challenging targets for small molecules. Cyclic peptides offer a promising solution, owing to their inherently large surface area and ability to easily mimic functional groups and structures at protein interfaces. Cyclic peptides can also be proteolytically stable and membrane-permeable, providing access to high-value intracellular targets.<\/p>\n

Unfortunately, minimal structural information is available for cyclic peptides in solution, making it difficult to perform structure-based design or understand why sequence-similar cyclic peptides can display vastly different binding affinities, membrane-permeability, and other properties. Critically, NMR studies show that most cyclic peptides reported thus far, including the approved cyclic peptide drugs, adopt multiple conformations in solution, existing as structural ensembles. Robust experimental methods to structurally characterize each conformation in a structural ensemble do not currently exist.<\/p>\n

Since structural information for most cyclic peptides is impractical to obtain by experiments, computational approaches provide an optimal alternative. Molecular dynamics (MD) simulation, particularly explicit-solvent MD, has long been used to provide high-quality predictions of the structures, dynamics, and functions of peptides and proteins. However, the ring strain in small cyclic peptides often leads to high free energy barriers between conformations that prevent sampling of all relevant structures by standard MD. Larger cyclic peptides can also present significant issues, because they often have vast conformational ensembles. Therefore, running standard, plain MD simulation in an explicit solvent at 300K typically provides insufficient conformational sampling of cyclic peptides even after weeks of simulation. I will describe enhanced sampling methods and how they can be tailored to cyclic peptides to efficiently sample their conformations and accurately characterize their solution structural ensembles using explicit-solvent MD.<\/p>\n

In addition, I will describe how we develop StrEAMM (Structural Ensembles Achieved by Molecular dynamics and Machine learning) models for cyclic peptides by combining MD simulation and machine learning. We can now provide simulation-quality cyclic peptide structure predictions in seconds. We expect this capability to rapidly predict cyclic peptide structures, enabling researchers to understand the structural basis for the diverse properties of cyclic peptides and greatly accelerating the development of this unique class of molecules. <\/p><\/div>\n<\/div>\n

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02\/18\/26 Yihan Shao<\/h2>
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Physics and Machine Learning Based Methods for Modeling Enzyme Reactions and Bioimaging Probes <\/h3>\n
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Department of Chemistry, Brandeis University<\/a><\/p>\n<\/div>\n

In this seminar, we will present computational methods from our lab for modeling enzymatic reactions and bioimaging probes. In the first part of the seminar, we will focus on the identification of the minimum free energy pathways, which is essential to fully understand an enzyme reaction. To reduce the steep computational cost of these free energy simulations, we have adapted the multiple time-step integration algorithm and machine learning potentials in these simulations. We will showcase the power of our simulation protocols with chorismate mutase and CRISPR-Cas9 enzymes. In the second part, we will look at small molecule bioimaging probes. Specifically, we will show how to analyze the interactions between chromophore orbitals and substituent (or solvent) orbitals, and explain how electron-donating and withdrawing groups could modulate the chromophore emission wavelengths. <\/div>\n<\/div>\n

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02\/25\/26 Connor Coley<\/h2>
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Computer-aided synthesis planning: the basics & beyond <\/h3>\n
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Department of Chemical Engineering, Massachusetts Institute of Technology<\/a><\/p>\n<\/div>\n

Much progress has been made in recent years toward the development of computer-aided synthesis planning (CASP) programs capable of recommending retrosynthetic pathways to molecules of interest. Modern programs make use of machine learning techniques to learn feasible retrosynthetic disconnections from databases of published organic reactions. In the first half of the talk, I will discuss the key components of CASP and the variety of approaches that have been taken by our group and others to address them. In the second half, I will focus on a couple of recent research directions we have undertaken to (i) improve our ability to ideate pathways for complex molecules, (ii) make recommended pathways more experimentally actionable, and (iii) critically evaluate the feasibility of proposed transformations. <\/div>\n<\/div>\n

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03\/04\/26 Ksenia Bravaya<\/h2>
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Non-Hermitian quantum mechanics methods for electronic resonances: Electronic structure and nuclear dynamics <\/h3>\n
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Department of Chemistry, Boston University<\/a><\/p>\n<\/div>\n

Metastable electronic states, or resonances, are states that lie in the electron detachment\/ionization continuum and cannot be described with conventional electronic structure methods developed for bound electronic states. Non-Hermitian quantum mechanics methods provide an elegant framework that allows one to extend bound-state methods to electronic resonances. In the first part of my talk, I will introduce non-Hermitian quantum mechanics methods for resonances using model problems, and connect time-independent and time-dependent approaches for characterizing resonances.\u00a0 In the second part of the talk, I will introduce a projected complex absorbing potential (CAP) method and demonstrate how it can be effectively combined with various bound-state electronic structure methods, extending their applicability to electronic resonances. I will show examples of using the developed methods for characterizing resonances in representative molecular systems, as well as discuss the role of non-valence resonances in reactions initiated by electron impact. Finally, I will briefly discuss non-adiabatic nuclear dynamics methods that can be used to simulate chemical processes that proceed through metastable electronic states. <\/div>\n<\/div>\n

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03\/18\/26 Bin Zhang<\/h2>
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Learning Energy Landscape and Structures with Generative AI <\/h3>\n
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Department of Chemistry, Massachusetts Institute of Technology<\/a><\/p>\n<\/div>\n

Generative AI has advanced rapidly in recent years, enabling the modeling and synthesis of highly complex data. In this talk, I will discuss several key ideas underlying generative models from a theoretical chemistry perspective. Rather than viewing these methods as purely data-driven tools, I will emphasize their connections to statistical mechanics and molecular modeling.<\/p>\n

I will then describe how generative approaches can be applied to biomolecular systems, including the modeling of protein conformational ensembles and 3D chromatin organization. Beyond generating structures, I will discuss how protein language models can be leveraged to infer effective energy landscapes. Such learned energy landscapes may extend generative modeling to regimes where structural training data are sparse, including intrinsically disordered proteins and biomolecular condensates. <\/p><\/div>\n<\/div>\n

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04\/01\/26 Wenjie Dou<\/h2>
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Excited-state electronic structure and non-adiabatic dynamics for molecular spin systems <\/h3>\n
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School of Science, Westlake University<\/a><\/p>\n<\/div>\n

The electronic structure theory and non-adiabatic dynamics of complex systems are very challenging due to the steep scaling of the current computational methods. I will first present our newly developed\u00a0stochastic second-order coupled cluster method (sRI-CC2) for calculating the excited\u00a0state of large systems. Using the set of stochastic orbitals, we decouple the crucial 4-index electron repulsion integrals into stochastic resolution of identity. These techniques allow a remarkable scaling reduction from O(N^5) to O(N^3), which allows us to calculate the excited state properties for systems with thousands of electrons. I will then present a dynamical approach for open quantum systems\u2014Memory kernel coupling theory. This approach builds upon the memory kernel formalism and avoid the calculation of projected dynamics by further decomposing the memory kernel into auxiliary kernel functions. We employ this approach to study the spin-phonon relaxation process in a molecular qubit. The numerical results successfully explain novel spin relaxation phenomena observed in experiments. <\/div>\n<\/div>\n

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04\/22\/26 William Jacobs<\/h2>
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Learning Thermodynamics from Sequence: Predicting and Designing Complex Molecular Mixtures <\/h3>\n
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Department of Chemical and Biological Engineering, Princeton University<\/a><\/p>\n<\/div>\n

Biological and synthetic materials derive function from sequence-encoded interactions that govern self-organization in multicomponent molecular mixtures. Predicting how molecular sequences determine thermodynamic behavior and material properties is therefore a central challenge across biophysics and molecular engineering. In this talk, I introduce a machine-learned thermodynamic framework that maps molecular sequences directly to context-dependent chemical potentials\u2014without requiring direct free-energy calculations. The approach learns low-dimensional, physically interpretable representations that define a thermodynamic geometry in which distances encode differences in behavior across molecular mixtures. Using intrinsically disordered proteins as a model system, I demonstrate how this framework enables quantitative prediction of partitioning and multicomponent phase diagrams, while revealing how sequence composition and patterning jointly control mixture behavior. More broadly, this work outlines a path toward quantitative, design-oriented thermodynamic models predicted directly from sequence, bridging statistical physics, machine learning, and molecular engineering. <\/div>\n<\/div>\n

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04\/29\/26 Qiang Cui<\/h2>
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A Random Walk in Membrane Biophysics <\/h3>\n
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Department of Chemistry, Boston University<\/a><\/p>\n<\/div>\n

Membrane biophysics encompasses a broad range of phenomena spanning molecular to mesoscopic scales. In this talk, I will discuss several of our recent research directions in this area, including transport through ion channels and transporters, lipid membrane remodeling, and interactions between biomolecular condensates and membranes.<\/p>\n

The first part of the talk will take a pedagogical perspective, introducing computational approaches commonly used to study membrane-associated problems, from detailed atomistic models (including QM\/MM and polarizable force fields) to coarse-grained and continuum descriptions. Using examples from our own work, I will highlight the unique strengths, limitations, and open challenges associated with each level of modeling.<\/p>\n

In the second part, I will illustrate how these approaches can be applied to gain mechanistic insight into a range of biological problems that span diverse length and time scales. Together, these examples underscore both the opportunities and the ongoing challenges in developing predictive, multiscale descriptions of membrane phenomena. <\/p><\/div>\n<\/div>\n

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