General conditions for organic reactions are important but rare, and efforts to identify them usually consider only narrow regions of chemical space. Discovering more general reaction conditions requires considering vast regions of chemical space derived from a large matrix of substrates crossed with a high-dimensional matrix of reaction conditions, rendering exhaustive experimentation impractical. Here, we report a simple closed-loop workflow that leverages data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions. Application to the challenging and consequential problem of heteroaryl Suzuki-Miyaura cross-coupling identified conditions that double the average yield relative to a widely used benchmark that was previously developed using traditional approaches. This study provides a practical road map for solving multidimensional chemical optimization problems with large search spaces.

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based artificial intelligence experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing—and democratizing—scientific discovery.

Mapping primordial reaction networks: Chemists seeking to understand the origins of life have published a wide range of reactions that may have yielded the building blocks of proteins, nucleic acids, and lipids from simple precursors. Wołos et al. scoured the literature to document each such reaction class and then wrote software that applied the reactions first to the simplest compounds such as cyanide, water, and ammonia, and then iteratively to each successive generation of products. The resulting network predicted a variety of previously unappreciated routes to biochemically relevant compounds, several of which the authors validated experimentally. Science , this issue p. eaaw1955

The hexagonal phase of NaYF₄ is one of the most popular hosts for the synthesis of upconverting phosphors. The local structure of the lattice is known to have an impact on the optical properties of the doped NaYF₄, however despite extensive research being conducted in this field, the structure of the crystalline β-NaYF₄ phase is little understood. In this paper, the bulk β-NaYF₄ is investigated by means of Car–Parrinello Molecular Dynamics simulations. Three different space groups proposed in the literature are compared. The results show that models based on P and P2m space groups converge to the same structure with nine-fold coordination sites only, while the model based on the P6₃/m space group is distinct and contains sodium atoms in six-fold coordination sites. Besides coordination numbers, analysis of the distances and average structure, the paper also presents a study of the dynamics of the lattice. Depending on the model, anisotropic thermal vibrations are observed and quantified, as well as oscillations of the sodium atoms between adjacent sites. A different scale of oscillation is observed, depending on the element (sodium or yttrium) and coordination site. In some cases, a reduced symmetry of the coordination shell is observed.