29 citations · 55 across the 14 of their papers we have counts for
15 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…
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…
A probabilistic framework for crystal structure denoising, phase classification, and order parameters
Hyuna Kwon, Babak Sadigh, Sebastien Hamel +3
Atomistic simulations generate large volumes of noisy structural data, yet extracting phase labels and continuous order parameters (OPs) in a robust and general manner remains chal…
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…
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…
Grand canonical generative diffusion model for crystalline phases and grain boundaries
Bo Lei, Enze Chen, Hyuna Kwon +5
The diffusion model has emerged as a powerful tool for generating atomic structures for materials science. This work calls attention to the deficiency of current particle-based dif…
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…
Growth and Structure of alpha-Ta films for Quantum Circuit Integration
Loren D. Alegria, Alex Abelson, Eunjeong Kim +5
Tantalum films incorporated into superconducting circuits have exhibited low surface losses, resulting in long-lived qubit states. Remaining loss pathways originate in microscopic…
Ice phase classification made easy with score-based denoising
Hong Sun, Sebastien Hamel, Tim Hsu +3
Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamic…
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…
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
Daniel Schwalbe-Koda, Sebastien Hamel, Babak Sadigh +2
An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification…
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…
Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models
Hyuna Kwon, Tim Hsu, Wenyu Sun +8
The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework b…