Driving atomic structures of molecules, crystals, and complex systems with local similarity kernels

TL;DR

LOSIKO is a data-driven method that efficiently determines atomic structures of molecules, crystals, and interfaces by matching local atomic environments with database entries, offering accuracy comparable to quantum chemical methods.

Contribution

The paper introduces LOSIKO, a novel local similarity kernel approach that leverages geometric data and databases for atomic structure determination, reducing computational costs and enabling inverse design.

Findings

Achieves quantum-chemical accuracy using geometric data and databases.

Can incorporate diverse quantum chemical databases for different approximations.

Enables inverse design by curating databases with target features.

Abstract

Accessing structures of molecules, crystals, and complex interfaces with atomic level details is vital to the understanding and engineering of materials, chemical reactions, and biochemical processes. Currently, determination of accurate atomic positions heavily relies on advanced experimental techniques that are difficult to access or quantum chemical calculations that are computationally intensive. We describe an efficient data-driven LOcal SImilarity Kernel Optimization (LOSIKO) approach to obtain atomic structures by matching embedded local atomic environments with that in databases followed by maximizing their similarity measures. We show that LOSIKO solely leverages on geometric data and can incorporate quantum chemical databases constructed under different approximations. By including known stable entries, chemically informed atomic structures of organic molecules, inorganic solids, defects, and complex interfaces can be obtained, with similar accuracy compared to the state-of-the-art quantum chemical approaches. In addition, we show that by carefully curating the databases, it is possible to obtain structures with bias towards target material features for inverse design.