Timothy J. Giese, Ph.D.

Affiliations: 
2005 University of Minnesota, Twin Cities, Minneapolis, MN 
Area:
Physical Chemistry, Molecular Physics
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Darrin M. York grad student 2005 UMN
 (Development and validation of new-generation molecular mechanical force fields and semiempirical Hamiltonians.)
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Publications

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Case DA, Cerutti DS, Cruzeiro VWD, et al. (2025) Recent Developments in Amber Biomolecular Simulations. Journal of Chemical Information and Modeling
Giese TJ, Zeng J, York DM. (2025) Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications. The Journal of Physical Chemistry. B
Giese TJ, Snyder R, Piskulich Z, et al. (2025) FE-ToolKit: A Versatile Software Suite for Analysis of High-Dimensional Free Energy Surfaces and Alchemical Free Energy Networks. Journal of Chemical Information and Modeling
Zeng J, Zhang D, Peng A, et al. (2025) DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials. Journal of Chemical Theory and Computation
Zeng J, Giese TJ, Götz AW, et al. (2025) The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions. Scientific Data. 12: 693
Zeng J, Giese TJ, Zhang D, et al. (2025) DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials. Journal of Chemical Information and Modeling
Wilson TJ, McCarthy E, Ekesan Ş, et al. (2024) The Role of General Acid Catalysis in the Mechanism of an Alkyl Transferase Ribozyme. Acs Catalysis. 14: 15294-15305
Tsai HC, Xu J, Guo Z, et al. (2024) Improvements in Precision of Relative Binding Free Energy Calculations Afforded by the Alchemical Enhanced Sampling (ACES) Approach. Journal of Chemical Information and Modeling
Giese TJ, Zeng J, Lerew L, et al. (2024) Software Infrastructure for Next-Generation QM/MM-ΔMLP Force Fields. The Journal of Physical Chemistry. B
Tao Y, Giese TJ, York DM. (2024) Electronic and Nuclear Quantum Effects on Proton Transfer Reactions of Guanine-Thymine (G-T) Mispairs Using Combined Quantum Mechanical/Molecular Mechanical and Machine Learning Potentials. Molecules (Basel, Switzerland). 29
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