Dr. Schwalbe-Koda's research seeks to accelerate materials synthesis and characterization using a combination of theory, simulations, and machine learning. His group also performs fundamental science research within predictive synthesis science, from automating the interpretation of spectroscopic data to proposing phase transformation mechanisms with data-driven models. His interests also include developing new generative models for functional materials with applications in energy, devices, and more.

• Melle, Nguyen, Lopez, and Schwalbe-Koda. "Automatic Identification of Compounds in Molecular Mixtures from Liquid-Phase Infrared Spectra." Chemical Science 17, 13465 (2026). https://doi.org/10.1039/d6sc01583b
• Guo and Schwalbe-Koda. "Generative Inversion of Spectroscopic Data for Amorphous Structure Elucidation". arXiv:2603.23210 (2026). https://doi.org/10.48550/arXiv.2603.23210
• Yang and Schwalbe-Koda. "A Generative Diffusion Model for Amorphous Materials". npj Computational Materials (2025). https://doi.org/10.1038/s41524-025-01901-1
• Schwalbe-Koda et al. "Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory." Nature Communications 16, 4014 (2025). https://doi.org/10.1038/s41467-025-59232-0
• Schwalbe-Koda, et al. “A priori control of zeolite phase competition and intergrowth with high-throughput simulations.” Science 374 (6565), 308 (2021). https://doi.org/10.1126/science.abh3350
• Schwalbe-Koda, A. R. Tan et al. “Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks.”Nat. Commun. 12, 5104 (2021). https://doi.org/10.1038/s41467-021-25342-8
• Schwalbe-Koda, et al. “Graph similarity drives zeolite diffusionless transformations and intergrowth.” Nat. Mater. 18, 1177 (2019). https://doi.org/10.1038/s41563-019-0486-1

• https://samueli.ucla.edu/ucla-materials-scientist-daniel-schwalbe-koda-wins-second-collaborative-ai-innovation-award/
• https://www.linkedin.com/posts/toyota-research-institute_synthesis-challenge-proposal-winners-activity-7236734746406350848-DYON
• https://samueli.ucla.edu/ucla-materials-science-professor-receives-doe-award-for-materials-synthesis-research/
• Forbes 30 Under 30: Science (2023)

• Ph.D., Materials Science and Engineering, Massachusetts Institute of Technology, 2022
• M.S., Physics, Aeronautics Institute of Technology - Brazil, 2018
• B.S., Electronic Engineering, Aeronautics Institute of Technology - Brazil, 2017

• Boeing Excellence in Teaching Award (2026)
• DOE Early Career Award (2024)
• Scialog Fellow (2024)
• Forbes 30 Under 30: Science (2023)
• LLNL Lawrence Postdoctoral Fellowship (2022)
• MRS Graduate Student Gold Award (2021)
• MIT Energy Fellowship (2020)
• MIT Robert M. Rose Presidential Fellowship (2018)
• Brazilian Ministry of Defense Honor Award (2018)
• Summa cum Laude, Aeronautics Institute of Technology, Brazil (2017)
• Best undergraduate research in Brazil on Exact, Earth Sciences and Mathematics, CNPq (2017)