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   Abstract Identifying the nature of dark matter (DM) has long been a pressing question for particle physics. In the face of ever-more-powerful exclusions and null results from large-exposure searches for TeV-scale DM interacting with nuclei, a significant amount of attention has shifted to lighter (sub-GeV) DM candidates. Direct detection of the light DM in our galaxy by observing DM scattering off a target system requires new approaches compared to prior searches. Lighter DM particles have less available kinetic energy, and achieving a kinematic match between DM and the target mandates the proper treatment of collective excitations in condensed matter systems, such as charged quasiparticles or phonons. In this context, the condensed matter physics of the target material is crucial, necessitating an interdisciplinary approach. In this review, we provide a self-contained introduction to direct detection of keV–GeV DM with condensed matter systems. We give a brief survey of DM models and basics of condensed matter, while the bulk of the review deals with the theoretical treatment of DM-nucleon and DM-electron interactions. We also review recent experimental developments in detector technology, and conclude with an outlook for the field of sub-GeV DM detection over the next decade. 

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   https://doi.org/10.1073/pnas.2403497121  

   Liu, Dongyu; Wang, Bipeng; Wu, Yifan; Vasenko, Andrey S; Prezhdo, Oleg V (September 2024, Proceedings of the National Academy of Sciences)

   Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium processes, such as photochemical reactions and charge transport. NA-MD application to condensed phase has drawn tremendous attention recently for development of next-generation energy and optoelectronic materials. Studies of condensed matter allow one to employ efficient computational tools, such as density functional theory (DFT) and classical path approximation (CPA). Still, system size and simulation timescale are strongly limited by costly ab initio calculations of electronic energies, forces, and NA couplings. We resolve the limitations by developing a fully machine learning (ML) approach in which all the above properties are obtained using neural networks based on local descriptors. The ML models correlate the target properties for NA-MD, implemented with DFT and CPA, directly to the system structure. Trained on small systems, the neural networks are applied to large systems and long timescales, extending NA-MD capabilities by orders of magnitude. We demonstrate the approach with dependence of charge trapping and recombination on defect concentration in MoS₂. Defects provide the main mechanism of charge losses, resulting in performance degradation. Charge trapping slows with decreasing defect concentration; however, recombination exhibits complex dependence, conditional on whether it occurs between free or trapped charges, and relative concentrations of carriers and defects. Delocalized shallow traps can become localized with increasing temperature, changing trapping and recombination behavior. Completely based on ML, the approach bridges the gap between theoretical models and realistic experimental conditions and enables NA-MD on thousand-atom systems and many nanoseconds. 

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