Molecular ionization, ion pairing, transport, and speciation are fundamental to processes in biophysical systems and a wide range of industrial applications. All these processes share underlying changes in electrostatic interactions that require sophisticated potential energy functions and adequate sampling to reveal the associated, intricate molecular details. In addition to being centrally important, high-quality experimental data (free energy of solvation, redox potentials, pKa, spectroscopic observables, enzyme kinetics, etc) for these processes provide a rigorous framework for the validation of novel computational methods. Computational methods on multiple scales, ranging from continuum and empirical molecular mechanics models to first-principles quantum chemistry, often fall short for complementary reasons: the various intermolecular interactions are not sufficiently accurate, or the methods are too expensive to accurately model sufficiently large systems. As a result, these computational problems are ideal for developing machine-learned potentials and computational workflows that integrate theoretical and sampling advancements.

The NIST Thermodynamics Research Center maintains one of the largest electronic collections of experimental thermophysical data in the world. Direct access to this information offers unique possibilities for the development and validation of creative multiscale computational approaches to complex electrostatic processes in heterogeneous molecular environments, ranging from “simple” non-ideal aqueous mixtures to complex biological enzymes. In addition, there are opportunities to develop and improve NIST data resources through critical evaluation of the experimental data in collaboration with TRC thermodynamics experts.

key words

hydration; ion pairing; metal ion binding; proton transfer; pKa; redox potentials; free energy; potential of mean force; speciation; molecular simulation; molecular mechanics; quantum mechanics; QM/MM; machine-learned potentials

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants