Bariana Bowman University of Florida 2025-26 Software Fellow “Development of a foundation model that integrates topological information with crystal structure data to predict superconductivity and other key condensed matter properties, creating a generalizable AI framework for materials discovery.”
Austin Wallace Georgia Tech 2025-26 Software Fellow “Improvements in open-source symmetry-adapted perturbation theory in Psi4 to facilitate developing more data efficient and accurate atom-pairwise neural networks in QCMLForge.”
Trine Quady UC Berkeley 2025-26 Software Fellow ” Development of an unusually aggressive local correlation electronic structure framework to achieve low-scaling wave function-level energy differences at modest system sizes.”
Shehan Parmar Georgia Tech 2025-26 Software Fellow “Accelerating materials discovery through scalable, high-throughput molecular dynamics pipelines and automated, ab initio-based, polarizable force field development.”
Weiliang Luo MIT 2025-26 Software Fellow “Development of a neural network potential active learning workflow for enzymatic catalysis study, which integrates cutting-edge quantum chemistry and machine learning techniques in a user-friendly way and democratizes the machine learning for enzyme simulation to the whole community of biochemistry”
Kenneth Lopez Perez University of Florida 2025-26 Software Fellow “Efficient similarity-based cheminformatics tools for drug design.”
Christopher Hillenbrand Yale University 2025-26 Software Fellow “User-friendly implementations of real-time correlated electron dynamics methods in PySCF for molecules and materials”
Arthur Lin University of Wisconsin – Madison 2025-26 Software Fellow “Design and implementation of machine-learned potentials for anisotropic coarse-grained simulations.”
Robin Grotjahn (Santa Clara University-Award Fellow) Santa Clara University High Performance Computing Applications in a General Chemistry Laboratory