Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions.
Sign Up to like & getrecommendations! Published in 2022 at "Chemical communications"
DOI: 10.1039/d2cc01820a
Abstract: Prediction of thermophysical properties from molecular principles requires accurate potential energy surfaces (PES). We present a widely-applicable method to produce first-principles PES from quantum chemistry calculations. Our approach accurately interpolates three-body non-additive interaction data, using… read more here.
Keywords: non additive; quantum chemistry; chemistry; machine learning ... See more keywords
First principles predictions of magneto-optical data for semiconductor point defect identification: The case of divacancy defects in 4H-SiC
Sign Up to like & getrecommendations! Published in 2018 at "New Journal of Physics"
DOI: 10.1088/1367-2630/aaa752
Abstract: Study and design of magneto-optically active single point defects in semiconductors are rapidly growing fields due to their potential in quantum bit (qubit) and single photon emitter applications. ... read more here.
Keywords: predictions magneto; point; first principles; optical data ... See more keywords