The prediction of structure at the atomic level is one of the most fundamental challenges in
condensed matter science. Here we survey the current status of the field and consider recent …

Considerable effort has been invested in developing methods for predicting the crystalline
structure (s) of a given compound, ideally starting from no more than a structural formula of …

We demonstrate the application of deep neural networks as a machine-learning tool for the
analysis of a large collection of crystallographic data contained in the crystal structure …

Abstract Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While
CSP can be addressed by employing currently-prevailing generative models (eg diffusion …

Following on from the success of the previous crystal structure prediction blind tests
(CSP1999, CSP2001, CSP2004 and CSP2007), a fifth such collaborative project (CSP2010) …

We report on the organization and outcome of the fourth blind test of crystal structure
prediction, an international collaborative project organized to evaluate the present state in …

Multicomponent solid forms of low molecular weight drugs, such as co-crystals, salts, and co-
amorphous systems, are a result of the combination of an active pharmaceutical ingredient …

Crystal structure prediction has been widely used to accelerate the discovery of new
materials in recent years. Up to this day, it remains a challenge to predict the stable …

Crystal structure prediction (CSP) is now increasingly used in the discovery of novel
materials with applications in diverse industries. However, despite decades of …

Crystal structure prediction (CSP) is the problem of determining the most stable crystalline
arrangements of materials given their chemical compositions. In general, CSP …