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Teresa Head-Gordon

T. Head-Gordon

Born September 28, 1960 in Akron, Ohio.

University Endowed Chair of Computational Science, Department of Chemistry, Bioengineering, Chemical and Biomolecular Engineering, University of California, Berkeley

Email:mhg@cchem.berkeley.edu
Web: external link

B.S. Case Western Reserve University (1979); Ph.D. Carnegie Mellon University (1989); Postdoctoral Member of Technical Staff, AT&T Bell Labs (1990-1992); Member, International Academy of Quantum Molecular Science (2026); Humboldt Research Award (2024); Debye Lecturer, Cornell University (2024); Closs Lecturer, University of Chicago (2022); Allergan Distinguished Lecturer, CSU Long Beach (2021); Fellow, American Chemical Society (2018); Fellow, ReSolv German Center of Excellence, Bochum (2018); Fellow, American Institute of Medical and Biomedical Engineers (2016); Director, CalSolv: Solvation at UC Berkeley (2016-present); Co-Director, MolSSI: Molecular Sciences Software Institute, NSF National Center (2016-2026); Chancellor’s Professor, UC Berkeley (2012-2025); Schlumberger Fellow, Cambridge University, UK (2005); IBM-SUR Award (2001).

Author of:

More than 275 scientific articles in computational and theoretical chemistry and biophysics

Important Contributions:

  • Theoretical models to simulate and interpret observables for wide and small angle X-ray scattering, NMR, terahertz, and dielectric relaxation spectroscopies.
  • Development of many-body models for polarization and charge transfer combined with efficient extended Lagrangian formulations for SCF and SCF-less solutions for their energy and forces to drive molecular simulations.
  • Advancing the role of electric fields for chemical reactivity and catalysis for electrocatalytic surfaces, synthetic enzymes, natural enzyme turnover, and for the emerging area of water and oil microdroplets.
  • Developing new machine learning architectures and their use for molecular dynamics, chemical reactivity, transition state optimization, and conformational generative tasks.
  • Transforming large language models to predict chemical behavior in discovery of new drugs, chemical linkers, transition metal complexes and for synthesis planning.