(3cj) Computational Soft Materials Design for Electronics, Energy, and the Environment

Functional polymer and soft materials play a central role in a vast array of fields, ranging from electronics to human health to energy and the environment. To satisfy performance metrics in these myriad applications, researchers are constantly developing new polymer chemistries, architectures, assembly paradigms, and processing schemes. However, controlling the complex interplay between equilibrium driving forces and kinetic limitations in soft materials assembly, though crucial to appropriately harnessing these new techniques, remains a challenge. My independent research program will address this gap by developing and applying machine learning-enabled computational tools to uncover the chemical and physical driving forces underlying self-assembly and phase behavior in fluids and functional soft matter. We will then explore how processing techniques, additives, or chemical transformations shift the equilibrium state and/or assembly kinetics to trap useful nano- and microscale assemblies. We will target applications in soft materials design for optoelectronics, energy producing devices, and environmentally sustainable polymers.

My group will employ a combination of molecular simulation and liquid-state theory techniques, paired with state-of-the-art machine learning tools, to support our efforts in model development, computation, and analysis. We will focus on three key areas: 1) using deep-neural-network potentials to enable multiscale computational study of the solution assembly of environmentally-sustainable polymers, 2) exploiting polymer architecture effects, 'alchemical' transformations, and supramolecular assembly to design functional stimuli-responsive materials, and 3) augmenting Ornstein-Zernike (OZ) liquid-state theory with machine learning techniques to solve key challenges in materials theory. In each of these projects, we will map how the design of macromolecular building blocks (chemistry, architecture, covalent and noncovalent bonding moieties, etc.) affects their interactions and assembly. Then, we will explore how processing techniques can be leveraged to achieve precisely controlled metastable and nonequilibrium nanoscale structures. Our multiscale approach will allow us to efficiently interrogate the molecular-level phenomena underlying soft materials assembly, enabling the design of optimized processes to navigate the structure/function pipeline of materials development for optoelectronics, energy, and the environment.

To establish this independent research program, I will draw on my multifaceted history in materials research. In my Ph.D. work, I developed simulation and theory approaches (JCTC 2016, Macromolecules 2018) to probe the interplay between molecular-level design and solvent processing techniques in the self-assembly of polymer and colloidal systems. For example, in key recent papers I uncovered a novel mechanism that controlled the structure of optical nanomaterials prepared via emulsion assembly of nanoparticle mixtures (Science Advances 2019, MSDE 2020). I also have extensive expertise leveraging coarse-grained molecular simulations to explore polymer architecture effects in films and solutions (Soft Matter 2018, Macromolecules 2019). In my postdoctoral research, I have combined ab-initio quantum chemistry, molecular simulation, and machine learning techniques to probe the structure, properties, and phase behavior of supercooled liquid and glassy water. Using this framework I showed, for the first time, first-principles evidence for a liquid-liquid transition and associated metastable critical point in water, which can help explain many of water's thermophysical anomalies (in preparation 2020). Concurrently, I am using classical molecular models to investigate the structure of glassy water, in which I discovered striking additional signatures of the second critical point (in preparation 2020). My core expertise in simulation and theory is complemented by my experiences performing scattering experiments. During my Ph.D. research, I used neutron and x-ray techniques to study lithium salt-doped block polymer materials for battery electrolytes (Macromolecules 2017, Macromolecules 2018), rationalizing the self-assembly of the salt-doped block polymers through comparison to molecular simulations and strong segregation theory. Additionally, prior to graduate school I worked in semiconductor manufacturing R&D and was recognized for my creative research contributions. As a faculty member I will leverage this diversity of methodological and topical expertise, gained via my academic work and professional experience, to forge strong simulation/experimental collaborations and enrich my interdisciplinary research program.

Teaching Interests

I consider impactful teaching and mentorship to be of equal importance to innovative research, and I have personally experienced the profound effects of faculty-student interactions in fostering informed and engaged future scientists, engineers, and policymakers. As a faculty member I am excited to act on my commitment to teaching by creatively connecting students' classroom work to the world around them, via tangible and relatable examples, assignments, and projects. In my teaching I plan to draw from my hybrid academic and professional background to foster interest in academic research while highlighting the industrial relevance of the subject matter. When explaining key concepts, I will draw on Chemical Engineering's myriad career paths and research interests to help foster curiosity and engagement in my students.

My research, teaching, and professional background has prepared me to teach several core chemical engineering subjects. However, I am particularly interested in designing and leading courses on thermodynamics at the undergraduate and graduate level. Through my teaching assistantship experience at the University of Delaware, I gained a keen appreciation of the challenges and sticking points for students when approaching key concepts in thermodynamics--for example, choosing from the myriad available activity coefficient models in phase equilibria calculations. In my thermodynamics curriculum I hope to clarify the process of problem-solving, help students navigate available methodological choices, and develop an appropriate problem-solving workflow. In addition to my experience teaching thermodynamics, I have also tutored an undergraduate student in fluid mechanics, heat and mass transfer, and chemical kinetics, covering the broad range of core topics that are key to a well-rounded Chemical Engineering education.

Outside of the core curricula, I will develop elective courses in polymer physics, statistical thermodynamics, and molecular simulation and theory to help train the next generation of soft materials scientists. At the University of Delaware, I was awarded the Fraser and Shirley Russell Teaching Fellowship, in which each graduate student fellow collaborates with a faculty member to co-teach a course. In Spring 2019 I designed and taught the senior undergraduate and graduate elective course "Molecular modeling and simulations of soft materials" in collaboration with Prof. Arthi Jayaraman. The course covered theoretical and practical aspects of molecular simulation and theory, with emphasis on polymers and soft materials. Prof. Jayaraman and I collaborated on all aspects of the course; we both delivered lectures, created homework assignments and projects, and assigned grades. My experience in building a new elective class from the ground up provides a unique platform from which I will design and teach similar courses as a faculty member. Overall, my research, professional, and teaching experiences will propel me to be an impactful educator. As part of this role, I am committed to engaging with my students as an empathetic advocate and mentor, and to championing diversity in the field of Chemical Engineering.

Selected Publications (13 total publications, 9 first or co-first author; 2 additional first-author papers in preparation; 1 patent)

T.E. Gartner, III,* C.M. Heil,* A. Jayaraman, Molecular Systems Design and Engineering, 2020, 5, 864-875; *Equal contributions

M. Xiao,* Z. Hu,* T.E. Gartner, III,* X. Yang, W. Li, A. Jayaraman, N.C. Gianneschi, M.D. Shawkey, A. Dhinojwala, Science Advances, 2019, 5, eaax1254; *Equal contributions

T.E. Gartner, III, F.M. Haque, A.M. Gomi, S.M. Grayson, M.J.A. Hore, A. Jayaraman, Macromolecules, 2019, 52 (12), 4579-4589

T.E. Gartner, III, A. Jayaraman, Macromolecules, 2019, 52 (3), 755-786; **Journal cover article, featured in ACS Editors' Choice, journal's #2 most-read article in 2019-2020

T.B. Martin, T.E. Gartner, III, R.L. Jones, C.R. Snyder, A. Jayaraman, Macromolecules, 2018, 51 (8), 2906-2922

T.E. Gartner, III, A. Jayaraman, Soft Matter, 2018, 14, 411-423

T.E. Gartner, III,* M.A. Morris,* C.K. Shelton,* J.A. Dura, T.H. Epps, III, Macromolecules, 2018, 51 (5), 1917-1926; *Equal contributions

T.E. Gartner, III, T. Kubo, Y. Seo, M. Tansky, L.M. Hall, B.S. Sumerlin, T.H. Epps, III, Macromolecules, 2017, 50 (18), 7169-7176

T.E. Gartner, III, T.H. Epps, III, A. Jayaraman, Journal of Chemical Theory and Computation, 2016, 12 (11), 5501-5510

In Preparation

T.E. Gartner, III, L. Zhang, P.M. Piaggi, R. Car, A.Z. Panagiotopoulos, P.G. Debenedetti, 2020, (planned submission to Proceedings of the National Academy of Sciences)

T.E. Gartner, III, R. Car, S. Torquato, P.G. Debenedetti, 2020, (planned submission to Proceedings of the National Academy of Sciences)

Selected Awards

Finalist, APS Division of Polymer Physics Frank J. Padden Jr. Award, APS March Meeting (2019)

1^st Place, AIChE Area 8A Excellence in Graduate Polymer Research Symposium, AIChE Annual Meeting (2018)

Fraser and Shirley Russell Teaching Fellowship, University of Delaware Department of Chemical & Biomolecular Engineering (2018-2019)

1^st Place, AIChE MESD Graduate Student Poster Competition, AIChE Annual Meeting (2017)

Honorable Mention, NSF Graduate Research Fellowship Program (2015)

President's Quality Award, Applied Materials, Inc. (2012)

Contributions to Funded Proposals

NSF, DMR-CMMT 1609543, (Arthi Jayaraman, PI) 9/2016 - 8/2019, $307,257

NSF, XSEDE Allocation MCB100140, (Arthi Jayaraman, PI) 4/2017 - 6/2018, $55,000 approx. value

NSF, XSEDE Allocation MCB100140 Renewal, (Arthi Jayaraman, PI) 7/2018 - 6/2019, $487,856 approx. value

DOE, BES-CSGB NERSC Allocation m3538, (Roberto Car, PI) 1/2020 - 1/2021