Research Interests

Dr. Holber is a Postdoctoral Researcher in the Quantum Simulations Group, Materials Science Diversion. Her research focuses on understanding and engineering materials for energy storage through multiscale modeling and physics informed machine learning techniques.

 

She integrates atomistic simulations, machine learned potentials, and large-scale molecular dynamics to uncover the relationship between ionic transport, and phase stability and structural characteristics. Her work aims to develop predictive computational frameworks connecting atomic-scale mechanisms to macroscopic performance in next-generation energy storage.

 

Subject Matter Expert

Computational material science; Materials for energy storage; Physics informed machine learning; Multiscale modeling; Large scale MD; Atomistic modeling

PhD, Applied Physics and Scientific Computing, University of Michigan, Ann Arbor, Michigan, 2025

MS, Applied Physics, University of Michigan, Ann Arbor, Michigan, 2023

ScB, Physics and Computer Science, Brown University, Providence, Rhode Island, 2019

Selected publications 1. Holber, J., & Garikipati, K. (2026). Physics-and data-driven active learning of neural network representations for free energy density functions of materials from statistical mechanics. Computer Methods in Applied Mechanics and Engineering, 448, 118434.] 2. Holber, Jamie, and Krishna Garikipati. Equivariant graph neural network surrogates for predicting the properties of relaxed atomic configurations. arXiv preprint arXiv:2505.08121 (2025). 3. Livingston, E., Srivastava, S., Holber, J., Mourad, H. M., & Garikipati, K. (2025). Inference of phase field fracture models. arXiv preprint arXiv:2504.17165. 4. Shojaei, M. F., Holber, J., Das, S., Teichert, G. H., Mueller, T., Hung, L., ... & Garikipati, K. (2024). Bridging scales with Machine Learning: From first principles statistical mechanics to continuum phase field computations to study order–disorder transitions in LixCoO2. Journal of the Mechanics and Physics of Solids, 105726. 5. Zhang, X., et al. "mechanoChemML: A software library for machine learning in computational materials physics." Computational Materials Science 211 (2022): 111493. 6. Y.-L. Lee, J. Holber, H.P. Paudel, D.C. Sorescu, D.J. Senor, Y. Duan, Density functional theory study of the point   For a full list, see: Google Scholar

University of Michigan:

• Michigan Institute for Computational Discovery and Engineering (MICDE) Fellow 2022-2023

• Applied Physics Fellow 2021-2022

 

Brown University:

• Mildred Widgoff Prize for Excellence in Thesis Preparation 2019

• Chair’s Award for Excellence in Scholarship and Service to the Physics Department 2019