15 citations · 30 across the 12 of their papers we have counts for
13 papers
Extracting Atomic Environments for Machine Learning Interatomic Potentials
Jared C. Stimac, Fei Zhou, Kyle Bushick +4
In order to appropriately capture large-scale material features and emergent phenomena via atomistic simulations, such as Molecular Dynamics (MD), the system scale can range up to…
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…
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…
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…
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…
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…
Fundamental Microscopic Properties as Predictors of Large-Scale Quantities of Interest: Validation through Grain Boundary Energy Trends
Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta +3
Correlations between fundamental microscopic properties computable from first principles, which we term canonical properties, and complex large-scale quantities of interest (QoIs)…
High-Precision Excited-State Nuclear Recoil Spectroscopy with Superconducting Sensors
C. Bray, S. Fretwell, L. A. Zepeda-Ruiz +30
Superconducting sensors doped with rare isotopes have recently demonstrated powerful sensing performance for sub-keV radiation from nuclear decay. Here, we report the first high-re…
Signal processing and spectral modeling for the BeEST experiment
Inwook Kim, Connor Bray, Andrew Marino +35
The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment searches for evidence of heavy neutrino mass eigenstates in the nuclear electron capture decay…
Cross-scale covariance for material property prediction
Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta +4
A simulation can stand its ground against experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of predicti…
Direct Experimental Constraints on the Spatial Extent of a Neutrino Wavepacket
Joseph Smolsky, Kyle G Leach, Ryan Abells +35
Despite their high relative abundance in our Universe, neutrinos are the least understood fundamental particles of nature. They also provide a unique system to study quantum cohere…
LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields
Joshua A. Vita, Amit Samanta, Fei Zhou +1
Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computatio…
The Data Acquisition System for Phase-III of the BeEST Experiment
C. Bray, S. Fretwell, I. Kim +27
The BeEST experiment is a precision laboratory search for physics beyond the standard model that measures the electron capture decay of Be implanted into superconducting tunnel…