Deep generative models have gained recent popularity for chemical design. Many of these
models have historically operated in 2D space; however, more recently explicit 3D …

The rational design of molecules with desired properties is a long-standing challenge in
chemistry. Generative neural networks have emerged as a powerful approach to sample …

In this mini review, we capture the latest progress of applying artificial intelligence (AI)
techniques based on deep learning architectures to molecular de novo design with a focus …

The computational discovery of novel materials has been one of the main motivations
behind research in theoretical chemistry for several decades. Despite much effort, this is far …

This work addresses one-shot set and graph generation, and, more specifically, the
parametrization of probabilistic decoders that map a vector-shaped prior to a distribution …

Many deep learning (DL)-based molecular generative models have been proposed to
design novel molecules. These models may perform well on benchmarks, but they usually …

Reinforcement learning (RL) has been applied to various domains in computational
chemistry and has found wide‐spread success. In this review, we first motivate the …

The growth of machine learning as a tool for research in computational chemistry is well
documented. For many years, this growth was heavily driven by the paradigms of supervised …

Equilibrium structures determine material properties and biochemical functions. We here
propose to machine learn phase space averages, conventionally obtained by ab initio or …

Reinforcement learning (RL) methods have helped to define the state of the art in the field of
modern artificial intelligence, mostly after the breakthrough involving AlphaGo and the …