Noah H. Paulson
Noah H. Paulson
Computational Scientist, Argonne National Laboratory
Verified email at - Homepage
Cited by
Cited by
Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics
NH Paulson, MW Priddy, DL McDowell, SR Kalidindi
Acta Materialia 129, 428-438, 2017
Correlations between thermal history and keyhole porosity in laser powder bed fusion
NH Paulson, B Gould, SJ Wolff, M Stan, AC Greco
Additive Manufacturing 34, 101213, 2020
Feature engineering for machine learning enabled early prediction of battery lifetime
NH Paulson, J Kubal, L Ward, S Saxena, W Lu, SJ Babinec
Journal of Power Sources 527, 231127, 2022
Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures
NH Paulson, MW Priddy, DL McDowell, SR Kalidindi
Materials & Design 154, 170-183, 2018
Quantified uncertainty in thermodynamic modeling for materials design
NH Paulson, BJ Bocklund, RA Otis, ZK Liu, M Stan
Acta Materialia 174, 9-15, 2019
Principles of the battery data genome
L Ward, S Babinec, EJ Dufek, DA Howey, V Viswanathan, M Aykol, ...
Joule 6 (10), 2253-2271, 2022
Strategies for rapid parametric assessment of microstructure-sensitive fatigue for HCP polycrystals
MW Priddy, NH Paulson, SR Kalidindi, DL McDowell
International Journal of Fatigue 104, 231-242, 2017
Bayesian strategies for uncertainty quantification of the thermodynamic properties of materials
NH Paulson, E Jennings, M Stan
International Journal of Engineering Science 142, 74-93, 2019
A convolutional neural network model for battery capacity fade curve prediction using early life data
S Saxena, L Ward, J Kubal, W Lu, S Babinec, N Paulson
Journal of Power Sources 542, 231736, 2022
Reduced-order microstructure-sensitive protocols to rank-order the transition fatigue resistance of polycrystalline microstructures
NH Paulson, MW Priddy, DL McDowell, SR Kalidindi
International Journal of Fatigue 119, 1-10, 2019
Thermodynamics of monoclinic and tetragonal hafnium dioxide (HfO2) at ambient pressure
JJ Low, NH Paulson, M D'Mello, M Stan
Calphad 72, 102210, 2021
Flame spray pyrolysis optimization via statistics and machine learning
NH Paulson, JA Libera, M Stan
Materials & Design 196, 108972, 2020
Uncertainty quantification and propagation in CALPHAD modeling
P Honarmandi, NH Paulson, R Arróyave, M Stan
Modelling and Simulation in Materials Science and Engineering 27 (3), 034003, 2019
Comparison of statistically-based methods for automated weighting of experimental data in CALPHAD-type assessment
NH Paulson, S Zomorodpoosh, I Roslyakova, M Stan
Calphad 68, 101728, 2020
Computational fluid dynamics modeling and analysis of silica nanoparticle synthesis in a flame spray pyrolysis reactor
D Dasgupta, P Pal, R Torelli, S Som, N Paulson, J Libera, M Stan
Combustion and Flame 236, 111789, 2022
Flame stability analysis of flame spray pyrolysis by artificial intelligence
J Pan, JA Libera, NH Paulson, M Stan
The International Journal of Advanced Manufacturing Technology 114 (7), 2215 …, 2021
Uncertainty quantification in atomistic modeling of metals and its effect on mesoscale and continuum modeling: A review
JJ Gabriel, NH Paulson, TC Duong, F Tavazza, CA Becker, S Chaudhuri, ...
Jom 73, 149-163, 2021
Intelligent agents for the optimization of atomic layer deposition
NH Paulson, A Yanguas-Gil, OY Abuomar, JW Elam
ACS Applied Materials & Interfaces 13 (14), 17022-17033, 2021
Insights from Computational Studies on the Anisotropic Volume Change of LixNiO2 at High States of Charge (x < 0.25)
JC Garcia, J Gabriel, NH Paulson, J Low, M Stan, H Iddir
The Journal of Physical Chemistry C 125 (49), 27130-27139, 2021
Bayesian automated weighting of aggregated DFT, MD, and experimental data for candidate thermodynamic models of aluminum with uncertainty quantification
JJ Gabriel, NH Paulson, TC Duong, CA Becker, F Tavazza, UR Kattner, ...
Materialia 20, 101216, 2021
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Articles 1–20