From the start of a synthetic chemist's training, experiments are conducted based on recipes
from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist …

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

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for
fragment-based drug discovery (FBDD) where such transformations require execution in the …

The ability to integrate resources and share knowledge across organisations empowers
scientists to expedite the scientific discovery process. This is especially crucial in addressing …

The explosion in the use of machine learning for automated chemical reaction optimization
is gathering pace. However, the lack of a standard architecture that connects the concept of …

Autonomous flow reactors are becoming increasingly utilized in the synthesis of organic
compounds, yet the complexity of the chemical reactions and analytical methods remains …

Research challenges encountered across science, engineering, and economics can
frequently be formulated as optimization tasks. In chemistry and materials science, recent …

Bayesian optimization is a powerful machine learning technique that is particularly well-
suited for optimizing chemical reactions in the early stages of process development. It can …