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20242026
most citedFundamental Microscopic Properties as Predictors of Large-Scale Quantities of Interest: Validation through Grain Boundary Energy Trends

6 citations · 6 across the 3 of their papers we have counts for

collaborators

5 papers

quant-ph2026

Resource Analysis for Quantum Simulation of Spatially Varying Transport-Reaction Equations

Benjamin Jasperson, Krishna Garikipati, Siddhartha Srivastava

Spatially varying coefficients are the primary source of circuit complexity in quantum simulation of linear advection-diffusion-reaction equations. This work presents a resource an…

cond-mat.mtrl-sci2026

Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors

Brad L. Boyce, Mitchell A. Wood, Krishna Garikipati +16

Materials behavior is often treated as a deterministic mapping from structure to properties, yet many important phenomena emerge from the conditional activation of multiple mechani…

cs.LG2025

SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges

Ce Ju, Reinmar Kobler, Antoine Collas +3

Neuroimaging provides essential tools for characterizing brain activity, structure, and connectivity through modalities that capture complementary aspects of brain organization. Ac…

cond-mat.mtrl-sci20246 cited

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)…

cond-mat.mtrl-sci2024

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…