Search NASA⌕ Search

Engineering topics

Dong, Sijia S.

Publications and source records attributed to Dong, Sijia S..

3 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Engineering a photoenzyme to use red light

Photoenzymatic reactions involving flavin-dependent “ene”-reductases (EREDs) rely on protein-templated charge transfer (CT) complexes between the cofactor and substrate for radical initiation. These complexes typically absorb in the blue region of the electromagnetic spectrum. Here, we engineered an ERED to form CT complexes that absorb red light. Mechanistic studies indicate that red-light activity is due to the growth of a red-absorbing shoulder off the previously identified cyan absorption feature. Molecular dynamics simulations, docking, and excited-state calculations suggest that the cyan feature involves a π→π* transition on flavin, whereas the red-light absorption is a π→π* transition between flavin and the substrate. Furthermore, differences in the electronic transition are due to changes in the substrate-binding conformation and allosteric tuning of the electronic structure of the cofactor-substrate complex. Microenvironment tuning of the CT complex for red-light activity is observed with other engineered photoenzymatic reactions, highlighting this effect’s generality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

Accurate and efficient calculations of absorption spectra of molecules and materials are essential for the understanding and rational design of broad classes of systems. Solving the Bethe–Salpeter equation (BSE) for electron–hole pairs usually yields accurate predictions of absorption spectra, but it is computationally expensive, especially if thermal averages of spectra computed for multiple configurations are required. We present a method based on machine learning to evaluate a key quantity entering the definition of absorption spectra: the dielectric screening. We show that our approach yields a model for the screening that is transferable between multiple configurations sampled during first principles molecular dynamics simulations; hence it leads to a substantial improvement in the efficiency of calculations of finite temperature spectra. We obtained computational gains of one to two orders of magnitude for systems with 50 to 500 atoms, including liquids, solids, nanostructures, and solid/liquid interfaces. Importantly, the models of dielectric screening derived here may be used not only in the solution of the BSE but also in developing functionals for time-dependent density functional theory (TDDFT) calculations of homogeneous and heterogeneous systems. Overall, our work provides a strategy to combine machine learning with electronic structure calculations to accelerate first principles simulations of excited-state properties.

36 MATERIALS SCIENCE↗