From the 1 of 9 linked papers with an AI index.
9 papers
Extracting Atomic Environments for Machine Learning Interatomic Potentials
Jared C. Stimac, Fei Zhou, Kyle Bushick +4
The paper benchmarks methods for extracting small atomic environments from large-scale simulations to enable DFT calculations for training machine‑learning interatomic potentials,…
Inverse design of bespoke interatomic potentials via active learning by information-matching
Yonatan Kurniawan, Logan D. Williams, Amit Samanta +6
Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selec…
Aluminum-Based Superconducting Tunnel Junction Sensors for Nuclear Recoil Spectroscopy
Spencer L. Fretwell, Connor Bray, Inwook Kim +31
The BeEST experiment is searching for sub-MeV sterile neutrinos by measuring nuclear recoil energies from the decay of Be implanted into superconducting tunnel junction (STJ) s…
Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis
Weishi Wang, Mark K. Transtrum, Vincenzo Lordi +2
An adaptive physics-inspired model design strategy for machine-learning interatomic potentials (MLIPs) is proposed. This strategy relies on iterative reconfigurations of composite…
Shake-up and shake-off spectra in the electron capture decay of atomic Be
Mauro Guerra, Inwook Kim, Stephan Friedrich +33
The most stringent laboratory-based experimental limits on the existence of sub-MeV sterile neutrinos are currently set by decay spectroscopy of radioactive Be embedded into su…
Unsupervised Atomic Data Mining via Multi-Kernel Graph Autoencoders for Machine Learning Force Fields
Hong Sun, Joshua A. Vita, Amit Samanta +1
Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and mat…