Precision measurements with ultracold atoms and molecules are primed to probe beyond-the-standard model physics. Isotopologues of homonuclear molecules are a natural testbed for new Yukawa-type mass-dependent forces at nanometer scales, complementing existing mesoscopic-body and neutron scattering experiments. Here, we propose using isotopic shift measurements in molecular lattice clocks to constrain these new interactions from new massive scalar particles in therange: The new interaction would impart an extra isotopic shift to molecular levels on top of one predicted by the standard model. For the strontium dimer, a Hz-level agreement between experiment and theory could constrain the coupling of the new particles to hadrons by up to an order of magnitude over the most stringent existing experiments.
This content will become publicly available on June 1, 2025
Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space.
- PAR ID:
- 10524908
- Publisher / Repository:
- PRX Energy
- Date Published:
- Journal Name:
- PRX Energy
- Volume:
- 3
- Issue:
- 2
- ISSN:
- 2768-5608
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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