publications

2025

1. Fundamental Microscopic Properties as Predictors of Large-Scale Quantities of Interest: Validation through Grain Boundary Energy Trends

   Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta, and 3 more authors

   Acta Materialia, 2025

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   Correlations between fundamental microscopic properties computable from first principles, which we term canonical properties, and complex large-scale quantities of interest (QoIs) provide an avenue to predictive materials discovery. We propose that such correlations can be efficiently discovered through simulations utilizing approximate interatomic potentials (IPs), which serve as an ensemble of "synthetic materials." As a proof of principle we build a regression model relating canonical properties to the symmetric tilt grain boundary (GB) energy curves in face-centered cubic crystals, characterized by the scaling factor in the universal lattice matching model of Runnels et al. (2016), which we take to be our QoI. Our analysis recovers known correlations of GB energy to other properties and discovers new ones. We also demonstrate, using available density functional theory (DFT) GB energy data, that the regression model constructed from IP data is consistent with DFT results, confirming the assumption that the IPs and DFT belong to same statistical pool and thereby validating the approach. Regression models constructed in this fashion can be used to predict large-scale QoIs based on first-principles data and provide a general method for training IPs for QoIs beyond the scope of first-principles calculations.

2. Cross-Scale Covariance for Material Property Prediction

   Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta, and 4 more authors

   npj Computational Materials, 2025

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   A simulation can stand its ground against an experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale ( 10^8 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤10^2 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale “strength-on-predictors” regression model. This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study.

2024

1. A data-driven method for optimization of classical interatomic potentials

   Benjamin A. Jasperson, and Harley T. Johnson

   MRS Advances, Mar 2024

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2023

1. A Dual Neural Network Approach to Topology Optimization for Thermal-Electromagnetic Device Design

   Benjamin A. Jasperson, Michael G. Wood, and Harley T. Johnson

   Computer-Aided Design, Dec 2023

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2021

1. Optically-triggered optical limiters for short-wavelength infrared sensor protection

   Michael G. Wood, Alec McKay, Theodore J. Morin, and 7 more authors

   In 2021 conference on lasers and electro-optics (CLEO), Dec 2021

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2014

1. Thin film heat flux sensors fabricated on copper substrates for thermal measurements in microfluidic environments

   Benjamin A Jasperson, Joshua Schmale, Weilin Qu, and 2 more authors

   Journal of Micromechanics and Microengineering, Dec 2014

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2010

1. Comparison of Micro-Pin-Fin and Microchannel Heat Sinks Considering Thermal-Hydraulic Performance and Manufacturability

   B.A. Jasperson,  Yongho Jeon, K.T. Turner, and 2 more authors

   IEEE Transactions on Components and Packaging Technologies, Mar 2010

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2. A Thin-Film Heat Flux Sensor Fabricated on Copper for Heat Transfer Measurements in Parallel Channel Heat Sinks

   Benjamin A. Jasperson, Frank E. Pfefferkorn, Weilin Qu, and 1 more author

   In , May 2010

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   A combination of lithography-based microfabrication and micro end milling is used to manufacture thin-film resistance temperature detector (RTD) heat flux sensors on bulk copper substrates. The fabrication process uses photoresist patterning, metal deposition, and lift-off to build the sensor and micro-end milling to segment the sensors. Micro end milling tests were performed to establish determine the optimum conditions for sensor removal which minimized delamination and burr formation. It was determined that starting on the backside (opposite the sensor) of the copper wafer and machining through to the thin film layers resulted in the least amount of burr formation.

2008

1. Experimental Study of Adiabatic Water Liquid-Vapor Two-Phase Pressure Drop Across an Array of Staggered Micro-Pin-Fins

   Christopher A. Konishi, Weilin Qu, Ben Jasperson, and 2 more authors

   In Volume 10: Heat Transfer, Fluid Flows, and Thermal Systems, Parts A, B, and C, Jan 2008

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   This study concerns pressure drop of adiabatic water liquid-vapor two-phase flow across an array of 1950 staggered square micro-pin-fins having a 200×200 micron cross-section by a 670 micron height. The ratios of longitudinal pitch and transverse pitch to pin-fin equivalent diameter are equal to 2. An inline immersion heater upstream of the micro-pin-fin test module was employed to produce liquid-vapor two-phase mixture, which flowed across the micro-pin-fin array. The test module was well insulated to maintain an adiabatic condition. Four maximum mass velocities of 184, 235, 337, and 391 kg/m2s, and a range of vapor qualities for each maximum mass velocity were tested. Measured pressure drop increases drastically with increasing vapor quality. Nine existing two-phase pressure drop models and correlations were assessed. The Lockhart-Martinelli correlation for laminar liquid-laminar vapor combination in conjunction with a single-phase friction factor correlation proposed for the present micro-pin-fin array provided the best agreement with the data.