New tools enable new ways of working, and materials science is no exception. In materials
discovery, traditional manual, serial, and human-intensive work is being augmented by …

The discovery of new materials can bring enormous societal and technological progress. In
this context, exploring completely the large space of potential materials is computationally …

The discovery of novel materials and functional molecules can help to solve some of
society's most urgent challenges, ranging from efficient energy harvesting and storage to …

In the space of only a few years, deep generative modeling has revolutionized how we think
of artificial creativity, yielding autonomous systems which produce original images, music …

Abstract Machine learning-based generative models can generate novel molecules with
desirable physiochemical and pharmacological properties from scratch. Many excellent …

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural
networks such as deep neural networks or recurrent networks drive this area. Numerous …

Diffusion models have become a new SOTA generative modeling method in various fields,
for which there are multiple survey works that provide an overall survey. With the number of …

To the Editor—The profound challenges of drug discovery, coupled with the societal
importance of the task, make it imperative that we investigate novel, creative methods that …

Computational Chemistry is currently a synergistic assembly between ab initio calculations,
simulation, machine learning (ML) and optimization strategies for describing, solving and …

Optimal design of polymers is a challenging task due to their enormous chemical and
configurational space. Recent advances in computations, machine learning, and increasing …