Ph.D., Chemical Engineering, Northwestern University, 2021
B.S., Chemical Engineering, Tufts University, 2015
Honors and Awards
- RSC Outstanding Reviewer, Digital Discovery, 2023
- Distinguished Young Scholar, University of Washington – Dept. of Chemical Engineering, 2022
- Miller Research Fellowship, University of California, Berkeley, 2021—2024
- Presidential Fellowship, Northwestern University, 2020—2021
- Distinguished Graduate Researcher Award, Northwestern University, 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 (×2), Northwestern University, 2017 & 2018
- National Defense Science and Engineering Graduate Fellowship, U.S. Dept. of Defense, 2017—2021
- Goldwater Scholarship, Barry Goldwater Scholarship Foundation, 2014
- National Undergraduate Fellowship, Princeton Plasma Physics Laboratory, 2013
Our group's research combines quantum-chemical calculations, high-throughput computing, and machine learning to accelerate the discovery of novel materials that can address global challenges in energy and sustainability.
As quantum-chemical engineers, we specifically focus on the computationally guided design of atomically programmable materials with novel electronic properties for applications in catalysis, chemical separations, and energy storage technologies. We have a complementary interest in understanding the stability and synthesizability of novel materials to guide experiments and to increase the impact of virtual screening studies. On the more fundamental side, we regularly develop and contribute to new computational tools that enable more actionable recommendations to be made in materials discovery campaigns.
Members of our group leverage recent advances in data science, atomistic computational methods, materials chemistry, inorganic chemistry, and solid-state physics to automate the exploration of materials space. To realize the full potential of our materials discovery platforms, our group is highly collaborative; we work alongside both theorists and experimentalists across disciplinary boundaries as well as with tech companies in the areas of deep learning and high-performance computing.
- A.S. Rosen, S. Vijay, K.A. Persson. “Free-Atom-Like d States Beyond the Dilute Limit of Single-Atom Alloys.'' Chem. Sci., 14, 1503—1511 (2023).
- A.S. Rosen, J.M. Notestein, R.Q. Snurr. “Realizing the Data-Driven, Computational Discovery of Metal--Organic Framework Catalysts.” Curr. Opin. Chem. Eng., 35, 100760 (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, 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.'' J. Am. Chem. Soc., 142, 4317—4328 (2020).
- A.S. Rosen. Correlations, Trends and Potential Biases among Publicly Accessible Web-Based Student Evaluations of Teaching.'' Assess. Eval. High. Educ., 43, 31—44 (2018).