CREATING AND ADVANCING COMPUTATIONAL METHODOLOGIES TO MODEL MOLECULAR INTERACTIONS AND REACTIVITIES, FROM SIMPLE MOLECULES TO COMPLEX ASSEMBLIES AND NON-EQUILIBRIUM STATES
Stanford chemists are advancing models and computational techniques that allow unprecedented atomistic simulations of molecular behavior, from the simplest of atomic species to molecular dynamics in complex living systems. These collective technologies allow us to address molecular behaviors too complex for experimental methods, and at the same time inform new experimental directions while also identifying new chemical reactivities and reactions.
Structure, Function and Reactivity
New methods that predict and explain how atoms move in molecules are providing a basis for both understanding the behavior of existing molecules and designing new ones. Associated approaches to interactive molecular simulation include a virtual reality based molecular modeling kit that fully understands quantum dynamics, exploiting efficient new methods for solving quantum mechanical problems quickly, using a combination of physical/chemical insights and commodity videogaming hardware.
An alternative approach combines experimental and theoretical techniques to explore the electronic structure of transition metal complexes and its contribution to reactivity. This work employs spectroscopy and electronic structure methods to examine the electronic and geometric structures of transition metal sites in enzymes and catalysts, and relationships of those structures to reactivity and function.
A range of theoretical approaches, molecular mechanics and ab initio simulations are applied to explore problems at the interface of quantum and statistical mechanics, including theories of hydrogen bonding, the interplay between structure and dynamics, systems with multiple time and length-scales, and quantum mechanical effects. Particular current interests include proton and electron transfer in materials and enzymatic systems, atmospheric isotope separation, and controlling the control of catalytic chemical reactivity in heterogeneous environments.
With an approach grounded in equilibrium and nonequilibrium statistical mechanics, we study the self-organization of biological materials using a combination of theoretical analysis, computational modeling, and machine learning techniques. Currently, we are focused on controlling self-assembly in nonequilibrium environments, self-organization of polyelectrolyte nanoparticles for encapsulation, and the biogenesis of bacterial microcompartments.