2022 CTC Summer Research Symposium
Tuesday, Aug. 9, 2022, 3 p.m. through Tuesday, Aug. 9, 2022, 5 p.m.
117/119 Smith Hall & Zoom
The CTC summer researchers will present the research they have conducted this summer.
Eliza Asani - University of Alabama in Huntsville, Goodpaster group
"Fermionic Deep Neural Networks in Quantum Embedding Calculations of the Spin-Splitting Energy of [Fe(H20)6]3+
Victor Drewanz Gnani Ernesto - Rollins College, Neurock group
"Effects of the Copper-Titania Interface in UiO-66 on the Selective Hydrogenation of CO2 to Methanol
Bhavnesh Jangid - Indian Institute of Technology Bombay, Truhlar group
"Calculation of Barrier Heights using Multiconfiguration Pair-Density Functional Theory: Assessment of the Performance of On-Top Density Functionals"
Cameron Khan - Princeton University, Sarupria group
"Ice nucleation dynamics via a seeding technique"
Mario Perez - University of Texas Rio Grande Valley, Siepmann group
"Developing a more representative model chromatographic pore"
Aniruddha Seal - National Institute of Science Education and Research, Sarupria group
"Enhanced Sampling guided design of Peptide-Surface Complexes"
The full program is available at:
Genome Organization through Phase Separation: Random yet Precise
Friday, April 8, 2022, 9:30 a.m.
Massachusetts Institute of Technology
The three-dimensional genome organization plays an essential role in all DNA-templated processes, including gene transcription, gene regulation, DNA replication, etc. Coarse-grained models parameterized to reproduce experimental data via the maximum entropy optimization algorithm serve as effective means to study genome organization at various length scales. They have provided insight into the principles of whole-genome organization and enabled de novo predictions of chromosome structures from epigenetic modifications. In addition, they provided insight into the critical role of the chromatin network in stabilizing multiple liquid droplets. Applications of these models at a near-atomic resolution further revealed physicochemical interactions that drive the phase separation of disordered proteins and dictate chromatin stability in situ.
Bin Zhang attended the University of Science and Technology of China (USTC) as a chemical physics major. After graduating from USTC in 2007, Bin moved to the United States to pursue doctoral research at the California Institute of Technology in Thomas Miller’s group. Upon graduation, Bin accepted a position as a postdoctoral scholar with Peter G. Wolynes at the Center for Theoretical Biological Physics at Rice University. Bin joined MIT faculty as an assistant professor in 2016. His research focuses on studying three-dimensional genome organization with interdisciplinary approaches that combine bioinformatics analysis, computational modeling and statistical mechanical theory. While at MIT, Bin has received awards that include the Scialog Fellowship and the NSF CAREER Award.
Multi-scale computational studies of assembly, regulation, and phase separation in the cell nucleus
Friday, March 18, 2022, 9:30 a.m.
117/119 Smith Hall and Zoom
Iowa State University
Cells of higher organisms are known for the hierarchical self-organization of their genomes, proteome, and associated biochemical reactions. Uncovering the underlying driving forces for cellular self-organization is a topic of significant importance in biosciences. Recent experiments have revealed the ubiquitous presence of nano and microscale membranless compartments in the nuclei of cells, generated through liquid-liquid phase separation of protein and nucleic acid components. Due to the heterogeneous and non-equilibrium environment, nuclear compartmentalization's thermodynamic and kinetic aspects are challenging to study both in vivo and in silico.
Our group is developing and applying multi-scale computational models that use atomistic, coarse-grained, and phase-field techniques to study nuclear compartmentalization at different scales, in and out of equilibrium. In the talk, we will present a selection of recent results on protein-RNA phase transitions, mesoscale nuclear dynamics of chromatin phase separation, and detailed models of biomolecular condensates based on bioinformatics and atomistic simulations.
Davit Potoyan received his Ph.D. in Chemical Physics at the University of Maryland-College Park in 2012. He spent the next few years training as a postdoctoral fellow in the Center for Theoretical Biological Physics at Rice University developing theoretical and computational models for studying gene-regulatory networks and transcription factor DNA assembly. In 2017 Davit joined the Iowa State as a Caldwell Assistant Professor of Chemistry and currently holds a courtesy faculty appointment in BBMB and BCB programs. The research field of Professor Potoyan is in computational biophysics broadly defined. His group is using multi-scale computational tools rooted in statistical physics, bioinformatics, machine learning, and data analytics to work on various biologically motivated problems. Some of the active areas of research in the group include the condensation of disordered proteins and nucleic acids, enzyme dynamics, chromatin organization, and genetic regulatory networks.
Molecular view of the plasticization process of poly(vinyl) alcohol
Friday, Dec. 17, 2021, 4 p.m.
117/119 Smith Hall and Zoom
Ernesto C. Cortés-Morales
Atomistic simulations are useful to address local interaction processes in a variety of materials, examples of this include self-assembly phenomena in polymers and host-guest binding applied to drug delivery and design. When designing simulations while incorporating advanced sampling methods in order to bias the potential energy surface towards pre-set slow collective variables, the simulation improves its efficiency thus allowing the treatment of realistic complex systems. In this work, we will follow the analysis of free energies governing the interactions of complex systems by employing the Artificial Neural Network (ANN) sampling method developed by Sidky and Whitmer. The discussion will highlight the configurational sampling using atomistic simulations of a polymer chain model interacting with different small-weight molecules, representing here the well-known plasticization process. We will focus on conformational and hydrogen-bond structure changes induced in polymer chain globules by the plasticizer molecules, while hypothesizing that hydrogen bonding plays an important role in the incorporation into polymer materials and thus, in the observed mechanical properties. The findings derived from this study showcase physical features relevant to the design of tailored materials, and the methods developed here are intended to be the first part of a robust framework applicable to an assortment of experimental materials designed for industrial purposes.
Design of Peptide Hydrogels for Drug Delivery: An Experimental and Computational Study
Friday, Dec. 17, 2021, 4 p.m.
117/119 Smith Hall and Zoom
Neetu Singh Yadav
Self-assembling peptide nanostructures have shown to be of great importance in nature and have many promising applications, for example, in medicine as drug delivery vehicles, biosensors and antivirals. There are numerous interesting candidate molecules within the sequence space built from the 20 amino acids. However, the immense complexity and variety spanned makes it difficult to screen and predict the supramolecular behavior merely based on sequence. Here, we employ a synergistic simulation and experimental approach that can be applied to explore the peptide space and identify peptides for drug delivery applications. These methods cover a broad range of length and time scales, from the very short (i.e., atomic level), via all-atom molecular dynamics (MD) simulations, up to the macroscopic one, via scanning electron microscopy (SEM) experiments. We explore the self-assembly of RAE, RAEF and ALKx (namely ALK1, ALK2 and ALK3) peptides in water. The circular dichroism (CD) spectra of peptides illustrates the beta sheet rich superstructures, whereas scanning electron microscopy (SEM) analysis shows the peptide morphology with several hundreds of nanometers of length. The MD simulation provides mechanistic insight into the crucial roles of hydrophilic and hydrophobic amino acids in the assembly of ALKx peptide derivatives; it reveals that assembly capability is reduced by increasing the length of hydrophilic Lys residues in ALKx peptides. Experiments and simulation results are in qualitative agreement. Based on various measures, the strength of the self-assembly propensity of the peptides in aqueous solutions attains the following order: ALK1=RAE>ALK2>ALK3>RAEF. Together this data provides insights into the mechanisms of self-assembly of model peptides. These results will enable bottom-up design of novel peptides for use in drug delivery.
How Dimensionality, Topology, and Void Space Influence the vander Waals Scaling Landscape across the Nanoscale
Friday, Nov. 19, 2021, 9 a.m.
Robert A. DiStasio Jr.
Recent experiments on non-covalent interactions at the nanoscale have challenged the basic assumptions of commonly used particle- or fragment-based models for describing van der Waals(vdW) or dispersion forces. In the first part of this talk, we demonstrate that a qualitatively correct description of the vdW interactions between polarizable nanostructures over a wide range of finite distances can only be attained by accounting for the wavelike nature of charge density fluctuations. By considering a diverse set of materials and biological systems with markedly different dimensionalities, topologies, and polarizabilities, we find a visible enhancement in the non-locality of the charge density response in the range of 10 to 20 nanometers. These collective wavelike fluctuations are responsible for the emergence of non-trivial modifications of the power laws that govern non-covalent interactions at the nanoscale.
Despite the importance of porous two-dimensional (2D) molecules and materials in advanced technological applications, the question of how the void space in these systems affects the vdW scaling landscape has been largely unanswered. In the second part of this talk, we present a series of analytical and numerical models demonstrating that the mere presence of a pore leads to markedly different vdW scaling across non-asymptotic distances, with certain relative pore sizes yielding effective power laws ranging from simple monotonic decay to the formation of minima, extended plateaus, and even maxima. These models are in remarkable agreement with first-principles approaches for the 2D building blocks of covalent organic frameworks (COFs), and reveal that COF macrocycle dimers and periodic bilayers exhibit unique vdW scaling behavior that is quite distinct from their non-porous analogs. These findings extend across a range of distances relevant to the nanoscale, and represent a hitherto unexplored avenue towards governing the self-assembly of complex nanostructures from porous 2D molecules and materials.
Robert A. DiStasio Jr. is currently an Assistant Professor of Chemistry and Chemical Biology at Cornell University. His research group focuses on development, implementation, and application of novel methodologies that extend the frontiers of Electronic Structure Theory in complex condense-phase environments. Born and raised in Brooklyn, NY, DiStasio was the first member of his family to attend college. He graduated summa cum laude from Portland State University in 2003 with degrees in Chemistry and Biology while working with Carl C. Wamser and the late George S. Hammond. DiStasio then relocated to the Bay Area to begin graduate studies at UC Berkeley with Martin Head-Gordon. In 2009, he received a Ph.D. in Theoretical Chemistry for his work on local and canonical approximations in Møller-Plesset perturbation theory with applications to dispersion interactions. This was followed by postdoctoral research at Princeton, where he worked with Roberto Car, Salvatore Torquato, and Frank H. Stillinger, as well as Alexandre Tkatchenko and Matthias Scheffler (at the Fritz Haber Institute of the Max Planck Society in Berlin).
DiStasio has given more than 75 seminars and colloquia worldwide, published more than 60 articles in peer-reviewed academic journals, and is an active contributor to the Q-Chem and Quantum ESPRESSO software packages. He is the proud recipient of the Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF) andthe 2020 American Chemical Society (ACS) Open Eye Outstanding Junior Faculty Award in Computational Chemistry. In 2020, DiStasio was also awarded a Sloan Research Fellowship from the Alfred P. Sloan Foundation.
Machine learning for bond breaking: Training CASPT2 level neural network potentials for carbon carbon dissociation in alkanes
Friday, April 16, 2021, 12:45 p.m.
Neural Network potentials are developed which accurately make and break bonds for use in molecular simulations. Large, condensed phased, and extended systems remain a challenge for theoretical studies due to the compromise between accuracy and computational cost in calculations. Machine learning methods provide a new approach to solve this trade off by leveraging large datasets to train on highly accurate calculations on small molecules and then extending learned features to predict larger systems. Specifically, neural network potentials have shown great promise to achieve high accuracy calculations with low computational cost. In this project, we introduce a novel method to combat the accuracy versus cost battle in computational chemistry.
We designed a training algorithm to generate neural network potentials to predict molecular energies of specific systems of interest; this method aims to use the minimum amount of data of smaller/sub systems to train neural network potentials to the accuracy level of advanced ab initio methods. Starting with a neural network potential trained at the density functional theory (DFT) level, we generate homolytic carbon-carbon bond dissociation data of small size alkanes with DFT energies to train the potentials to accurately predict bond dissociation at the DFT level. Then, using transfer learning, we retrained the neural network potential to complete active space second-order perturbation theory (CASPT2) level of accuracy. We demonstrate that the neural network potential only requires bond dissociation data of a few small alkanes to accurately predict bond dissociation energy in larger alkanes. This training algorithm can be further applied to any type of bond or any level of theory.
Density matrix embedding theory methods for non-equilibrium electron dynamics in extended systems
Friday, March 19, 2021, 12:45 p.m.
The simulation of non-equilibrium electron dynamics in extended systems provides a challenge for theoretical methods due to the need to treat both large system sizes and electron correlation. In this talk I will present our work developing extensions of density matrix embedding theory (DMET) methods to treat real-time dynamics. In DMET, the total system is partitioned into an impurity corresponding to a region of interest coupled to the surrounding environment, which is efficiently represented by a quantum bath of the same size as the impurity. We take advantage of this partitioning to develop equations of motion for the coupled impurity and bath embedding problem that allow for efficient and accurate simulation of real-time dynamics. The methodology is able to simulate non-equilibrium electron dynamics in the presence of strong correlation, reaching total system sizes unobtainable by conventional methodology.
AI-enabled Discovery of Macromolecular Structure, Dynamics, and Function
Friday, Jan. 22, 2021, 4 p.m.
George Mason University
Biology has undergone many disruptions and revolutions that have opened or redirected entire domains of scientific enquiry. Anfinsen showed us that protein tertiary structure was largely encoded in the amino-acid sequence. John Kendrew’s famous sentences “The way in which the chain of amino acid units in a protein molecule is coiled and folded in space has been worked out for the first time. The protein is myoglobin, the molecule of which contains 2,600 atoms.” instigated decades of computational studies on macromolecular structure, dynamics, and function, starting with the seminal work of Scheraga, Karplus, Levitt, Warshel, and others. Some of the most interesting computational concepts and techniques were debuted in these studies, many of which we would now categorize as “artificial intelligence” (AI). In my own work, I focused heavily, and still do, on the question of knowledge representation, which is central to AI. Then came the data revolution and the revival of neural networks. What was old became new. My laboratory showed data-driven models to be more powerful in many respects, yet not always satisfying. And now comes news DeepMind’s Alphafold2 has solved a 50-year grand challenge in biology. Many of us wonder what these increasingly more frequent AI disruptions mean for our disciplines, our students, and our careers. I will conclude this talk with my admittedly biased perspective and argue that, while we still need to figure out a new collaborative medium, these AI disruptions provide wonderful opportunities to get into complex, messy, integrative scientific enquiries.
Dr. Amarda Shehu is a Professor in the Department of Computer Science in the Volgenau School of Engineering with affiliated appointments in the Department of Bioengineering and School of Systems Biology at George Mason University. She is also Co-Director of the Center for Advancing Human-Machine Partnerships (CAHMP), a Transdisciplinary Center for Advanced Study at George Mason University. Shehu obtained her Ph.D. in Computer Science from Rice University in 2008, where she was also an NIH pre-doctoral fellow. Shehu's research focuses on novel algorithms in artificial intelligence and machine learning to bridge between computer and information sciences, engineering, and the life sciences. In particular, her laboratory has made many contributions in bioinformatics and computational biology regarding the relationship between macromolecular sequence, structure, dynamics, and function. Shehu has published over 130 technical papers with postdoctoral, graduate, undergraduate, and high-school students. She is the recipient of an NSF CAREER Award, and her research is regularly supported by various NSF programs, as well as state and private research awards. Shehu is also the recipient of the 2018 Mason University Teaching Excellence Award, the 2014 Mason Emerging Researcher/Scholar/Creator Award, and the 2013 Mason OSCAR Undergraduate Mentor Excellence Award. She currently serves as Program Director at the National Science Foundation in the Information and Intelligent Systems Division of the Computer and Information Science and Engineering Directorate.
Optimizing Free Energy Estimation with Machine Learning
Thursday, Dec. 10, 2020, Noon
Senior Research Scientist
Computer-based free energy estimation has been an active field of research for decades, with many successful applications in physics, materials science and biology. Free energy perturbation (FEP)  is a bedrock technique for estimation of free energy differences. A requirement for fast and reliable convergence of the estimator, however, is that the respective distributions share a large overlap in configuration space. An elegant strategy to address this problem is to increase overlap using configuration space mappings. This approach, known as Targeted Free Energy Perturbation (TFEP) , has the appealing property that it can achieve immediate convergence when combined with a perfect mapping. Defining a good high-dimensional mapping, however, is a challenging task.
In this talk, I will present our recent work  in which we turn TFEP into a machine learning problem. In our approach, we represent the mapping by a deep neural network whose parameters are optimized so as to maximize overlap. We test our targeted estimators on a prototypical solvation system for which we generate training data using molecular dynamics simulations. Our neural network architecture respects two important symmetries of the system, namely periodic boundary conditions and permutational symmetry of identical particles. We observe that our technique leads to an improved accuracy of the free energy estimates compared to baselines, without requiring any additional data for training the network.
 R. W. Zwanzig, J. Chem. Phys. 22, 1420 (1954).
 C. Jarzynski, Phys. Rev. E 65, 046122 (2002).
 This research by P. Wirnsberger, A.J. Ballard, G. Papamakarios, S. Abercrombie, S. Racanière, A. Pritzel, D. Jimenez Rezende, and C. Blundell has been first published in J. Chem. Phys. 153 , 144112 (2020), with the permission of AIP Publishing. PW and AJB contributed equally to this work.
Peter is a Senior Research Scientist at DeepMind. His research interests include developing new machine learning algorithms as well as applying them to problems that lie at the intersection of machine learning and physics. Peter obtained his PhD in Theoretical Chemistry from the University of Cambridge, where he worked with Daan Frenkel on quantifying polarization effects in molecular systems far from thermal equilibrium.