Updates to the DScribe library: New descriptors and derivatives.

We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DScribe.

Erratum: Data-Driven Materials Science: Status, Challenges, and Perspectives.

[This corrects the article DOI: 10.1002/advs.201900808.

Data-Driven Materials Science: Status, Challenges, and Perspectives.

Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning-typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development.

Chemical diversity in molecular orbital energy predictions with kernel ridge regression.

Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmark datasets. Here, we investigate the performance of machine learning with kernel ridge regression (KRR) for the prediction of molecular orbital energies on three large datasets: the standard QM9 small organic molecules set, amino acid and dipeptide conformers, and organic crystal-forming molecules extracted from the Cambridge Structural Database. We focus on the prediction of highest occupied molecular orbital (HOMO) energies, computed at the density-functional level of theory.