Machine learning the quantum flux–flux correlation function for catalytic surface reactions

Abstract

A dataset of fully quantum flux-flux correlation functions and reaction rate constants was constructed for organic heterogeneous catalytic surface reactions. Gaussian process regressors were successfully fitted to training data to...

Computational and data-driven modelling of solid polymer electrolytes

“…Efforts have been made to promote data sharing for both experimentalists and theorists in public repository. 163,249,250 With such repositories, experimentalists are expected to document accurate experimental data about SPEs such as ionic conductivity, mechanical properties, and morphological information. Meanwhile, computational chemists are expected to modify existing descriptors or design new descriptors that are compatible for more complicated polymer systems that can be benecial for establishing a more comprehensive database.…”

What is missing in autonomous discovery: open challenges for the community

“…Once created, digital data can easily be stored on a hard disk as a series of named les, yet this approach does little to ensure that the data can be found, understood, and reused in the future by collaborators, the community, or machines, without insight from the original data creators. 48 This problem is further exacerbated by the fact that most researchers are accustomed to the le systems of their laptops, but lack the background, training, or incentive to use shared community databases, common metadata standards, unique data identiers, and well-dened vocabularies. Furthermore, there are additional technical challenges connected to choosing appropriate databases (hierarchical, relational, non-relational) and data storage with appropriate data protection, safety, and maintenance (local or cloud).…”