(2fh) Computationally Accelerated Discovery of Atomically and Electronically Tunable Clean Energy Materials

The solutions to many of society’s most pressing problems rely on the discovery of materials with optimal properties that are unique to the application of interest. In many cases, it is not a matter of incremental improvement over existing technologies; rather, it is a need to identify new kinds of materials altogether. This is especially the case in the areas of energy and sustainability where transformational changes often remain the only viable path forward. The conventional trial-and-error approach of materials discovery, however, can be extremely time-consuming and rarely identifies optimal candidates, especially when they exist beyond the limits of our current chemical intuition.

My research group will couple high-throughput quantum-mechanical calculations and data science to discover novel materials that will help address global challenges in energy and sustainability. Specifically, my group will primarily focus on the computationally guided design of synthetically tunable, atomically precise materials — such as porous framework solids and single-atom catalysts — for the conversion of greenhouse gases, sustainable production of valuable chemical products, and realization of improved methods for energy storage.

To achieve these goals, the initial stages of my research program will be dedicated to addressing the following aims:

• Engineering porous framework materials with new types of electronic properties for small molecule activation, such as for nitrogen fixation and carbon dioxide reduction.
• Designing electronically programmable single-atom catalysts for challenging chemical transformations and next-generation energy-storage technologies.
• Developing deep learning models that can circumvent the computational limits of density functional theory for accelerated, multi-scale materials discovery.

Prior Work:

My expertise in quantum-mechanical modeling, high-throughput computational methods, and machine learning within the framework of modern chemical engineering makes me uniquely qualified to address the proposed research aims. During my doctoral research with Prof. Randall Q. Snurr and Prof. Justin M. Notestein at Northwestern University, I developed the first computational approaches to rapidly identify promising metal–organic frameworks (MOFs) — a novel class of tunable, porous solids — for a variety of applications in heterogeneous catalysis, gas separations, and (opto)electronics. Several notable examples from my doctoral research include: the development of new design rules for the catalytic upgrading of methane and other light alkanes into liquid fuels; the discovery of new adsorbents that can purify molecular oxygen from room-temperature air; and the creation of an unprecedented database of computed chemical properties for 20,000 MOFs, which I used to train machine learning models that can identify porous materials with unusual semi-conducting properties.

As an independently funded Miller Research Fellow at the University of California, Berkeley hosted by Prof. Kristin A. Persson, I have built upon the Materials Project — a database of predicted properties for nearly all known solid-state materials — to explore several new research topics. In one area, I have focused on understanding the thermodynamic stability limits of porous framework solids to gain insight into the black box of material synthesizability. Separately, I have also discovered unique bonding behavior in a subset of intermetallic compounds that makes it possible to finely control their electronic states and tailor their reactivity for challenging catalytic transformations.

Selected Awards:

• Miller Research Fellowship, University of California, Berkeley (2021)
• Presidential Fellowship, Northwestern University (2020 – 2021)
• Distinguished Graduate Researcher Award, Northwestern University – Chemical Engineering (2020)
• Outstanding Research Mentor Award, International Institute for Nanotechnology (2020)
• CAS Future Leader, American Chemical Society (2020)
• Ryan Fellowship, International Institute for Nanotechnology (2018 – 2021)
• George Thodos Teaching Assistant Award (x2), Northwestern University – Chemical Engineering (2017 & 2018)
• National Defense Science and Engineering Graduate Fellowship, U.S. Department of Defense (2017)

Selected Publications: (20 total: 12 first-author, 6 second-author)

• A.S. Rosen, V. Fung, P. Huck, C.T. O’Donnell, M.K. Horton, D.T. Truhlar, K.A. Persson, J.M. Notestein, R.Q. Snurr. "High‑Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration." npj Comput. Mat., 8, 112 (2022).
• A.S. Rosen, S.M. Iyer, D. Ray, Z. Yao, A. Aspuru‑Guzik, L. Gagliardi, J.M. Notestein, R.Q. Snurr. "Machine Learning the Quantum‑Chemical Properties of Metal–Organic Frameworks for Accelerated Materials Discovery." Matter, 4, 1578–1597 (2021).
• A.S. Rosen, J.M. Notestein, R.Q. Snurr. “High‑Valent Metal‑Oxo Species at the Nodes of Metal–Triazolate Frameworks: The Effects of Ligand Exchange and Two‑State Reactivity for C–H Bond Activation.” Angew. Chem. Int. Ed., 59, 19494–19502 (2020)
• A.S. Rosen, M.R. Mian, T. Islamoglu, O.K. Farha, J.M. Notestein, R.Q. Snurr. "Tuning the Redox Activity of Metal−Organic Frameworks for Enhanced, Selective O₂ Binding: Design Rules and Ambient Temperature O₂ Chemisorption in a Cobalt−Triazolate Framework." J. Am. Chem. Soc., 142, 4317–4328 (2020)
• A.S. Rosen, J.M. Notestein, R.Q. Snurr. "Structure–Activity Relationships that Identify Metal–Organic Framework Catalysts for Methane Activation." ACS Catal., 9, 3576–3587 (2019).