Designing functional materials requires a deep search through multidimensional spaces for
system parameters that yield desirable material properties. For cases where conventional …

Accurate simulations of atomistic systems from first principles are limited by computational
cost. In high-throughput settings, machine learning can reduce these costs significantly by …

Graph neural networks (GNNs) have recently been used to learn the representations of
crystal structures through an end-to-end data-driven approach. However, a systematic top …

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 …

Modern functional materials consist of large molecular building blocks with significant
chemical complexity which limits spectroscopic property prediction with accurate first …

Deep learning (DL) models currently employed in materials research exhibit certain
limitations in delivering meaningful information for interpreting predictions and …

Machine learning (ML) has been widely applied to chemical property prediction, most
prominently for the energies and forces in molecules and materials. The strong interest in …

Among various cathode materials for sodium-ion batteries (SIBs), Na 3 V 2 (PO 4) 3 has an
open and stable three-dimensional framework to reversibly (de) intercalate sodium ions …

The affordable, accurate, and generalizable prediction of spectroscopic observables plays a
key role in the analysis of increasingly complex experiments. In this article, we develop and …

Selecting diverse molecules from unexplored areas of chemical space is one of the most
important tasks for discovering novel molecules and reactions. This paper proposes a new …