Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Theoretical simulations of electronic excitations and associated processes in molecules are
indispensable for fundamental research and technological innovations. However, such …

Crystal structure prediction is a long-standing challenge in condensed matter and chemical
science. Here we report a machine-learning approach for crystal structure prediction, in …

X-ray absorption near-edge structure (XANES) spectra are the fingerprint of the local atomic
and electronic structures around the absorbing atom. However, the quantitative analysis of …

Machine learning (ML) approaches enable large-scale atomistic simulations with near-
quantum-mechanical accuracy. With the growing availability of these methods, there arises …

The physical sciences community is increasingly taking advantage of the possibilities
offered by modern data science to solve problems in experimental chemistry and potentially …

The rapid growth of materials chemistry data, driven by advancements in large-scale
radiation facilities as well as laboratory instruments, has outpaced conventional data …

Neutron and x-ray scattering represent two classes of state-of-the-art materials
characterization techniques that measure materials structural and dynamical properties with …

We present a quantitatively accurate machine-learning (ML) model for the computational
prediction of core–electron binding energies, from which X-ray photoelectron spectroscopy …

Artificial intelligence‐based methods such as chemometrics, machine learning, and deep
learning are promising tools that lead to a clearer and better understanding of data. Only …