Ruthenium oxides (RuOx) are promising alternatives to iridium catalysts for the oxygen-evolution reaction in proton-exchange membrane water electrolysis but lack stability in acid. Alloying with other elements can improve stability and performance but enlarges the search space.

Microwave technology offers rapid, selective, and efficient heating, making it a valuable tool for process intensification. In this context, this study employed microwave energy for rapid reaction optimization and reliable kinetic analysis for the catalytic conversion of glucose. Dehydration (DeH) and retro-aldol condensation (RAC) are two main routes for the catalytic conversion of glucose into valuable platform chemicals such as levulinic acid, methyl lactate, and other byproducts.

Bridging AI and self-driving laboratories, we introduce the first fully-automated, closed-loop molecular discovery cycle, exemplified by the identification of novel JAK inhibitors. With minimal human intervention, we combined AI-driven molecular design and retrosynthesis with IBM’s synthesis automation system RoboRXN and Arctoris’ Ulysses platform for automated in-vitro screening.

Autonomous laboratories, also known as self-driving labs, have emerged as a powerful strategy to accelerate chemical discovery. By highly integrating different key parts including artificial intelligence (AI), robotic experimentation systems and automation technologies into a continuous closed-loop cycle, autonomous laboratories can efficiently conduct scientific experiments with minimal human intervention.

Selective hydrogenation plays a critical role in modern synthetic chemistry, particularly in the pharmaceutical industry, where the production of chiral molecules with high enantiomeric purity is essential for the efficacy and safety of active pharmaceutical ingredients (APIs). 

Mobile robotic chemists are a fast growing trend in the field of chemistry and materials research. However, so far these mobile robots lack workflow awareness skills. 

The integration of automated synthesis and machine learning (ML) is transforming analytical chemistry by enabling data-driven approaches to method development. Chromatographic column selection, a critical yet time-consuming step in separation science, stands to benefit substantially from such advances.

The development of a general autonomous platform for organic synthesis that enables faster, flexible and efficient delivery of target molecules is an attractive strategy for many fields such as drug discovery and material sciences.

We are proud to spotlight our partnership with Ames National Laboratory, a key leader in the U.S. Department of Energy’s historic Genesis Mission—an initiative harnessing the power of Artificial Intelligence (AI) to transform American science and innovation.

Herein, we describe the development of the high-throughput experimentation platform and function in AbbVie’s Discovery Chemistry organization.