Lattice Study of the Extent of the Conformal Window in Two-Color Yang-Mills Theory

We perform a lattice calculation of the Schr\"odinger functional running coupling in SU(2) Yang-Mills theory with six massless Wilson fermions in the fundamental representation. The aim of this work is to determine whether the above theory has an infrared fixed point. Due to sensitivity of the $SF$ renormalized coupling to the tuning of the fermion bare mass we were unable to reliably extract the running coupling for stronger bare couplings

Approaching Conformality with Ten Flavors

We present first results for lattice simulations, on a single volume, of the low-lying spectrum of an SU(3) Yang-Mills gauge theory with ten light fermions in the fundamental representation. Fits to the fermion mass dependence of various observables are found to be globally consistent with the hypothesis that this theory is within or just outside the strongly-coupled edge of the conformal window, with mass anomalous dimension consistent with 1 over the range of scales simulated. We stress that we cannot rule out the possibility of spontaneous chiral-symmetry breaking at scales well below our infrared cutoff. We discuss important systematic effects, including finite-volume corrections, and consider directions for future improvement.Comment: 7 pages, 3 figures. Submitted to Physical Review Letters. v2: corrected global fits. v3: corrected estimation of confidence interval

WW Scattering Parameters via Pseudoscalar Phase Shifts

Using domain-wall lattice simulations, we study pseudoscalar-pseudoscalar scattering in the maximal isospin channel for an SU(3) gauge theory with two and six fermion flavors in the fundamental representation. This calculation of the S-wave scattering length is related to the next-to-leading order corrections to WW scattering through the low-energy coefficients of the chiral Lagrangian. While two and six flavor scattering lengths are similar for a fixed ratio of the pseudoscalar mass to its decay constant, six-flavor scattering shows a somewhat less repulsive next-to-leading order interaction than its two-flavor counterpart. Estimates are made for the WW scattering parameters and the plausibility of detection is discussed.Comment: 8 pages, 6 figure

Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data

Small molecules in biological samples are studied to provide information about disease states, environmental toxins, natural product drug discovery, and many other applications. The primary window into the composition of small molecule mixtures is tandem mass spectrometry (MS2), which produces data that are of high sensitivity and part per million resolution. We adopt multi-scale sinusoidal embeddings of the mass data in MS2 designed to meet the challenge of learning from the full resolution of MS2 data. Using these embeddings, we provide a new state of the art model for spectral library search, the standard task for initial evaluation of MS2 data. We also introduce a new task, chemical property prediction from MS2 data, that has natural applications in high-throughput MS2 experiments and show that an average $R^2$ of 80\% for novel compounds can be achieved across 10 chemical properties prioritized by medicinal chemists. We use dimensionality reduction techniques and experiments with different floating point resolutions to show the essential role multi-scale sinusoidal embeddings play in learning from MS2 data

MS2Mol: A transformer model for illuminating dark chemical space from mass spectra

The ability to identify small molecules in complex samples from their mass spectra is among the grand challenges of analytical chemistry. Improvements to this ability could significantly advance fields as diverse as drug discovery, diagnostics, environmental science, and synthetic biology. A primary bottleneck is that standard structure elucidation technologies are limited to identifying only those molecules that are contained in databases of known spectra or molecular structures and are therefore not well suited to identifying the vast majority of potentially billions of natural metabolites, whose structures are not yet catalogued. To improve the identification of molecular structures within this vast dark chemical space, we present MS2Mol, a de novo structure prediction model based on a generative sequence to sequence transformer. We also release EnvedaDark, a first-of-its-kind data set for benchmarking identification performance on unknown metabolites. EnvedaDark contains experimental mass spectra from 226 natural products not currently found in major databases. We demonstrate on this challenging dataset that MS2Mol is able to predict 21% of molecular structures to within a close-match accuracy threshold and 62% to within meaningful similarity, both of which are significant improvements over the closest match retrieved using standard database methods. We further present a confidence scorer that enables practical usage for novel molecule discovery and enriches the accuracy on meaningfully-similar and close-match thresholds to 98% and 63%, respectively, for the top 10% most confident predictions