Degree: Ph.D. from the Massachusetts Institute of Technology
Research: Computational Materials Science
Joined LLNL: June 2022
Research at LLNL: Daniel uses high-performance computing and machine learning methods to design new energy materials. He is particularly interested in modeling materials synthesis, helping to translate theoretical predictions into experimental discovery. His research topics range from in silico tailoring of catalyst structures to developing robust atomistic machine learning models. Through this integrated computational platform, Daniel seeks to accelerate the design of increasingly complex materials for sustainable applications.
Bio: Daniel Schwalbe-Koda is a Lawrence Fellow in the Physical and Life Sciences Directorate. He earned a B.Sc. in Electrical Engineering (2017) and a M.Sc. in Physics (2018) from the Aeronautics Institute of Technology, Brazil. He completed a Ph.D. in Materials Science and Engineering from the Massachusetts Institute of Technology (2022), with a focus on high-throughput simulations and machine learning for nanoporous materials. Daniel is a recipient of Forbes’ “30 Under 30: Science” accolade, the MRS Graduate Student Gold award, the MIT Energy Fellowship, and the MIT Presidential Fellowship.
Joined the PLS/MSD Directorate in 2022