Machine-learned force field transforms computational modeling

Machine-learned force field transforms computational modeling

Examples of coordination geometries adopted by acetate ligands, using descriptive labels suggested in the literature. (28,53)

Modeling the light-driven chemical reactions of semiconducting nanocrystals has been a challenge due to the complex nature of the systems and their size, although these crystals make exceptional materials for optoelectronic devices.

Researchers from Rice University (Rossky Group) and the University of Texas at Austin (Roberts Group) developed a machine-learned force field trained on DFT data to investigate the surface chemistry of one such nanocrystal. In doing so, they were able to show that carboxylate ligands adopt a wide range “tilted-bridge” and “bridge” geometries to passivate the surface of nanocrystals. Their study demonstrates that machine-learned force fields have great potential for use in modeling of semiconducting nanocrystal.

Leave a Reply

Your email address will not be published. Required fields are marked *