Electronic structure theory describes the motions of electrons in atoms or molecules, and provides a versatile framework for the calculation of molecular geometries, chemical bonding, electronic and spectroscopic properties, reaction barriers, intermolecular interactions, and more. Wave function theory-based methods such as coupled-cluster methods, provide accurate results in a systematic manner, but they typically carry a significant computational cost.
One of the targets of research in Vogiatzis’s group is to accelerate electronic structure theory calculations using machine-learning, which is the field of study that allows computers to learn connections in data without explicit programming.
Machine learning has changed our lives through improved speech recognition, automated vehicle operation, optimized web searching and recommendation, and beyond. Graduate student Jacob Townsend mentions, “Our goal is to take this technology, and allow our calculations to learn from previously executed calculations without introducing any approximations or alchemical approaches. Therefore, the desirable accuracy is reached with significantly less computational effort.”
In their recent publication entitled “Data-Driven Acceleration of the Coupled-Cluster Singles and Doubles Iterative Solver” published in the Journal of Physical Chemistry Letters, the team introduces a novel strategy to accomplish this speedup in a goal to change the way we will execute calculations in the future.