Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Enzymes can be engineered at the level of their amino acid sequences to optimize key
properties such as expression, stability, substrate range, and catalytic efficiency─ or even to …

Deep learning has become a powerful and frequently employed tool for the prediction of
molecular properties, thus creating a need for open-source and versatile software solutions …

Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …

Ligands play a crucial role in enabling challenging chemical transformations with transition
metal-mediated homogeneous catalysts. Despite their undisputed role in homogeneous …

Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires
automated measurements coupled with reliable decision-making,. Most autonomous …

Deep graph neural networks are extensively utilized to predict chemical reactivity and
molecular properties. However, because of the complexity of chemical space, such models …

Artificial intelligence-guided closed-loop experimentation has emerged as a promising
method for optimization of objective functions,, but the substantial potential of this …

Advancing battery technologies requires precise predictions of thermochemical reactions
among multiple components to efficiently exploit the stored energy and conduct thermal …

The integration of artificial intelligence into scientific research opens new avenues with the
advent of GPT-4V, a large language model equipped with vision capabilities. In this study …