Max Hutchinson
Max Hutchinson
Citrine Informatics
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Cited by
Cited by
LIBXSMM: accelerating small matrix multiplications by runtime code generation
A Heinecke, G Henry, M Hutchinson, H Pabst
SC'16: Proceedings of the International Conference for High Performance …, 2016
Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
B Meredig, E Antono, C Church, M Hutchinson, J Ling, S Paradiso, ...
Molecular Systems Design & Engineering 3 (5), 819-825, 2018
High-dimensional materials and process optimization using data-driven experimental design with well-calibrated uncertainty estimates
J Ling, M Hutchinson, E Antono, S Paradiso, B Meredig
Integrating Materials and Manufacturing Innovation 6, 207-217, 2017
VASP on a GPU: application to exact-exchange calculations of the stability of elemental boron
M Hutchinson, M Widom
Arxiv preprint arXiv:1111.0716, 2011
Overcoming data scarcity with transfer learning
ML Hutchinson, E Antono, BM Gibbons, S Paradiso, J Ling, B Meredig
arXiv preprint arXiv:1711.05099, 2017
On the strong scaling of the spectral element solver Nek5000 on petascale systems
N Offermans, O Marin, M Schanen, J Gong, P Fischer, P Schlatter, ...
Proceedings of the Exascale Applications and Software Conference 2016, 1-10, 2016
Building data-driven models with microstructural images: Generalization and interpretability
J Ling, M Hutchinson, E Antono, B DeCost, EA Holm, B Meredig
Materials Discovery 10, 19-28, 2017
Efficiency of high order spectral element methods on petascale architectures
M Hutchinson, A Heinecke, H Pabst, G Henry, M Parsani, D Keyes
High Performance Computing: 31st International Conference, ISC High …, 2016
Machine learning for alloy composition and process optimization
J Ling, E Antono, S Bajaj, S Paradiso, M Hutchinson, B Meredig, ...
Turbo Expo: Power for Land, Sea, and Air 51128, V006T24A005, 2018
Performance study of sustained petascale direct numerical simulation on Cray XC40 systems
B Hadri, M Parsani, M Hutchinson, A Heinecke, L Dalcin, D Keyes
Concurrency and Computation: Practice and Experience 32 (20), e5725, 2020
Quantifying uncertainty in high-throughput density functional theory: a comparison of AFLOW, Materials Project, and OQMD
VI Hegde, CKH Borg, Z del Rosario, Y Kim, M Hutchinson, E Antono, ...
arXiv preprint arXiv:2007.01988, 2020
ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition
J Ling, E Antono, S Bajaj, S Paradiso, M Hutchinson, B Meredig, ...
Oslo, 2018
Enumeration of octagonal tilings
M Hutchinson, M Widom
Theoretical Computer Science, 40-50, 2015
Using machine learning to explore formulations recipes with new ingredients
ML Hutchinson, ES Kim, RM Latture, SP Paradiso, JB Ling
US Patent 10,984,145, 2021
Solving industrial materials problems by using machine learning across diverse computational and experimental data
M Hutchinson, E Antono, B Gibbons, S Paradiso, J Ling, B Meredig
APS March Meeting Abstracts 2018, K32. 002, 2018
Direct numerical simulation of single mode three-dimensional Rayleigh-Taylor experiments
M Hutchinson
arXiv preprint arXiv:1511.07254, 2015
Performance Study of Sustained Petascale Direct Numerical Simulation on Cray XC40 Systems (Trinity, Shaheen2 and Cori)
B Hadri, M Parsani, M Hutchinson, A Heinecke, L Dalcin, DE Keyes
Cray User Group, 2019
Plane‐Wave Density Functional Theory
M Hutchinson, P Fleurat‐Lessard, A Anciaux‐Sedrakian, D Stosic, ...
Electronic Structure Calculations on Graphics Processing Units: From Quantum …, 2016
The Shirley reduced basis: a reduced order model for plane-wave DFT
M Hutchinson, D Prendergast
arXiv preprint arXiv:1402.7366, 2014
Multivariate prediction intervals for bagged models
B Folie, M Hutchinson
Machine Learning: Science and Technology 4 (1), 015022, 2023
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