(2bi) An Information-Driven Approach for Controlling Emergent Order in Soft Materials

Controlling the emergence of order out of disorder is an overarching goal in the engineering of biological and soft materials. Computational approaches have traditionally relied on simulations at varying time and length scales, aided by enhanced sampling techniques, including the recent proliferation of machine learning based methods. An exciting direction in the computational design of soft materials incorporates ideas from information theory into these approaches. My research interests lie at the intersection of molecular simulation, machine learning, and information theory, and I aim to combine all three in a computational approach toward understanding and designing soft materials.

I am particularly excited about taking this approach toward controlling the emergence of order from inherently disordered components, as well as in revealing hidden forms of order or collective motion in complex systems. Both of these themes underlie much of my research career thus far, during which I have built the toolset and experience to lead a team in pursuing a combined data- and information-driven approach toward computational soft materials research.

My Ph.D. work focused on applying enhanced sampling techniques toward studying complex self-aggregating systems that target urgent global challenges. These systems include early-stage aggregation pathways for human islet amyloid polypeptide [1], which is implicated in Type II diabetes, as well as the assembly of metal-organic frameworks [2] and phosphate-sequestering peptide amphiphiles [3], both of which target global water challenges. Beyond performing enhanced sampling in molecular simulations, I have also contributed to open source software that enables broader utilization of these algorithms [4], as well as developed a new enhanced sampling method assisted by machine learning, in which a neural network enables rapid mapping of free energy landscapes [5-6]. I further established my experience in applying machine learning to molecular simulation by analyzing molecular dynamics simulations of nucleosomal DNA using a nonlinear dimensionality reduction technique, demonstrating that such an approach can extract subtle collective motions from simulations that were previously difficult to identify by eye [7].

My postdoctoral work has focused on the study of a non-equilibrium dynamical model known as Biased Random Organization, originally developed to study sheared colloidal suspensions. In this work, I characterize the transition between two types of emergent order using an information-theoretic approach that draws upon the connection between information entropy and thermodynamic entropy. By using a data compression algorithm to quantify changes in entropy, I characterize multiple types of phase transitions and self-organization that arise in this model [8-9].

My future work will be built upon this foundation in using enhanced sampling, machine learning, and information theory to model, characterize, and design various self-organizing systems. My experience in these areas uniquely equips me to attack problems in soft materials engineering at multiple scales through the development and application of state-of-the-art enhanced sampling approaches, drawing upon the advantages conferred by machine learning techniques and exploiting the relationship between thermodynamic and information entropy. These strengths will position my future group to contribute to the basic understanding of soft matter systems, guide the design of functional materials, as well as develop new advanced algorithms to extend the capabilities of molecular simulation. In my poster, I will highlight the specific research directions that I envision for this future work.

Teaching Interests

My aim in teaching is to cultivate a supportive yet challenging learning environment that encourages a sense of curiosity in students, fosters their intuition for scientific and engineering concepts, and equips them with a toolkit to break down physical phenomena, both in the lab and in the everyday world. My attitude has been shaped by my teaching experiences, beginning with my time as an undergraduate at Caltech, where I served two terms as teaching assistant for an undergraduate chemistry laboratory course (second term supported by HHMI) and one term as a dean-appointed tutor for undergraduate Physical Chemistry. I continued to refine my teaching skills during my Ph.D. at the University of Chicago by serving as teaching assistant for graduate-level Polymer Physics & Engineering. Beyond teaching undergraduate and graduate courses, I have honed my teaching abilities through a two-year science communication workshop at the Museum of Science and Industry, designing and teaching my own classes through the Collegiate Scholars Program at the University of Chicago, and teaching a molecular simulation workshop at the MICCoM Summer School. I also spent one year as a Chicago Center for Teaching Fellow, where I learned effective pedagogical practices and delivered my own teaching workshops tailored for STEM.

Based upon my educational background in chemical and molecular engineering, I feel confident in my ability to teach core chemical engineering courses at both the undergraduate and graduate level. My research strengths and past experiences make me particularly well suited for teaching thermodynamics, statistical mechanics, and polymer physics. Taking into account my experiences in materials science, molecular simulation, machine learning, computational soft matter physics, and information theory, I am also eager and excited to teach courses on computational materials science, molecular modeling, and numerical methods.

Selected Awards

Distinguished Young Scholar Seminar, University of Washington, 2022

William Rainey Harper Dissertation Fellowship, University of Chicago, 2018-2019

Chicago Center for Teaching Fellow, University of Chicago, 2018-2019

Best Poster Award, Frontiers of Molecular Engineering Workshop in Chicago IL, 2018

Arts, Culture & Science Initiative Graduate Fellow, University of Chicago, 2015-2016

Howard Hughes Medical Institute Teaching Fellow, Caltech, 2014

Reed and Ruth Brantley Undergraduate Research Fellow, Caltech, 2012

Selected Publications

[1] A.Z. Guo, A.M. Fluitt, J.J. de Pablo "Early-stage human islet amyloid polypeptide aggregation: Mechanisms behind dimer formation," (2018). J Chem Phys.

[2] Y.J. Colón, A.Z. Guo, L.W. Antony, K.Q. Hoffmann, J.J. de Pablo, "Free energy of metal-organic framework self-assembly," (2019). J Chem Phys.

[3] W.C. Fowler, C. Deng, G.M. Griffen, T. Teodoro, A.Z. Guo, M. Zaiden, M. Gottlieb, J.J. de Pablo, M.V. Tirrell, “Harnessing Peptide Binding to Capture and Reclaim Phosphate,” (2021). J Am Chem Soc.

[4] H. Sidky, Y.J. Colón, J. Helfferich, B.J. Sikora, C. Bezik, W. Chu, F. Giberti, A.Z. Guo, X. Jiang, J. Lequieu, J. Li, J. Moller, M.J. Quevillon, M. Rahimi, H. Ramezani-Dakhel, V.S. Rathee, D.R. Reid, E. Sevgen, V. Thapar, M.A. Webb, J.K. Whitmer, J.J. de Pablo, "SSAGES: software suite for advanced general ensemble simulations," (2018). J Chem Phys.

[5] A.Z. Guo, E. Sevgen, H. Sidky, J.K. Whitmer, J.A. Hubbell, J.J. de Pablo, "Adaptive enhanced sampling by force-biasing using neural networks," (2018). J Chem Phys.

[6] E. Sevgen, A.Z.Guo, H. Sidky, J.K. Whitmer, J.J. de Pablo, "Combined force-frequency sampling for simulation of systems having rugged free energy landscapes," (2020). J Chem Theory Comput.

[7] A.Z. Guo, J. Lequieu, J.J. de Pablo, "Extracting collective motions underlying nucleosome dynamics via nonlinear manifold learning," (2019). J Chem Phys.

[8] A.Z Guo, S. Wilken, D. Levine, P.M. Chaikin, “Melting and order in a 2D non-equilibrium dynamical model,” In Preparation.

[9] A.Z Guo, S. Wilken, D. Levine, P.M. Chaikin, “A non-equilibrium dynamical approach toward random close packing,” In Preparation.