Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Atomistic simulations are routinely employed in academia and industry to study the behavior
of molecules, materials, and their interfaces. Central to these simulations are force fields …

Machine-learned interatomic potentials are fast becoming an indispensable tool in
computational materials science. One approach is the ephemeral data-derived potential …

We present machine learning models for predicting experimental hydration free energies of
molecules without any atom-, bond-, or geometry-specific input feature. Four types of …

Optimizing a target function over the space of organic molecules is an important problem
appearing in many fields of applied science but also a very difficult one due to the vast …

Molecular design requires systematic and broadly applicable methods to extract structure–
property relationships. The focus of this study is on learning thermodynamic properties from …

The paper by No\'e et al.[F. No\'e, S. Olsson, J. K\" ohler and H. Wu, Science, 365: 6457
(2019)] introduced the concept of Boltzmann Generators (BGs), a deep generative model …

The ensemble properties of a system are obtained by averaging over the properties
calculated for the various configurations it can have at a finite temperature and thus cannot …

Science is and always has been based on data, but the terms' data-centric'and the'4th
paradigm'of materials research indicate a radical change in how information is retrieved …

The need for software applications that can assist with mental disorders has never been
greater. Individuals suffering from mental illnesses often avoid consultation with a …