LAUSR

NMR experiments, indispensable to chemists in many areas of research, are often run with generic, unoptimized experimental parameters. This approach makes robust and automated acquisition on different samples and instruments extremely challenging. Here, we present NMR-POISE (Parameter Optimization by Iterative Spectral Evaluation), the first demonstration of on-the-fly, sample-tailored, and fully automated optimization of a wide range of NMR experiments. We illustrate how POISE maximizes spectral sensitivity and quality with a diverse set of 1D and 2D examples, ranging from HSQC and NOESY experiments to ultrafast and pure shift techniques. Our Python implementation of POISE has an interface integrated into Bruker's TopSpin software, one of the most widely used platforms for NMR acquisition and automation, allowing NMR optimizations to be run without direct user supervision. We predict that POISE will find widespread usage in academia and industry, where sample-specific and automated experiment optimization is mandatory.