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.

Empa und internationale Partner haben den bislang größten offenen Batterie-Datensatz im neuen Battery Data Format (BDF) veröffentlicht.

The discovery of novel battery materials has been accelerated by advanced modeling and machine learning.

We are proud to mark a 12-year milestone in our successful partnership with REALCAT driving breakthroughs in materials and catalysis discovery through high-throughput automation and advanced characterization.

The composition of reaction mixtures strongly influences the structural evolution and performance of noble metal-based catalysts. In this work, we compared the effect of the simultaneous presence of CO and NO on the noble metal state and CO oxidation activity of Pt/Al₂O₃ and Pt/CeO₂ catalysts under close-to-stoichiometric conditions using complementary in situ/operando X-ray and infrared spectroscopic techniques.

Due to its peculiar properties and strong interaction with noble metals, ceria is widely used as a catalyst support for numerous applications. In this work, morphologically pure and highly crystalline ceria nanocubes and nanorods were prepared to systematically investigate both the impact of the support morphology and Pd–Pt interaction degree on the noble metal-support interplay during CO oxidation.

We report an automated strategy to conduct RAFT copolymerizations using a Chemspeed robotic platform capable of executing batch, incremental, and continuous monomer addition workflows under inert conditions. Copolymerizations of oligo(ethylene glycol) acrylate with benzyl acrylate (as a control) and fluorescein o-acrylate were conducted in toluene, THF, and DMF, with reaction progress monitored via ¹H NMR spectroscopy at defined intervals.