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Center for Computing Research

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Publication	Type	Year
Training Machine Learned Interatomic Potentials for Chemical Complexity - Application to Refractory Complex Concentrated Alloys Gordon Research Conference - Computational Materials Science and Engineering Megan Jeanne McCarthy, Jacob Kyle Startt, Remi Philippe Michel Dingreville, Mitchell Wood	Conference Poster – 2022 Conference Poster	2022
FitSNAP: Machine Learned Potentials for LAMMPS Gordon Research Conference (Computational Materials Science and Engineering) Andrew Dale Rohskopf, Charlie Sievers, Megan Jeanne McCarthy, James Michael Goff, Ember Layne Sikorski, Aidan P. Thompson, Mitchell Wood	Conference Poster – 2022 Conference Poster	2022
Machine-Learned Interatomic Potential Development for W-ZrC for Nuclear Fusion Gordon Research ConferenceComputational Materials Science and Engineering Ember Layne Sikorski, Mary Alice Cusentino, Megan Jeanne McCarthy, Julien Tranchida, Mitchell Wood, Aidan P. Thompson	Conference Poster – 2022 Conference Poster	2022
Training Machine Learned Interatomic Potentials for Chemical Complexity - Application to Refractory CCAs The Minerals, Metals, and Materials Society (TMS) 2023 Annual Meeting and Exhibition Megan Jeanne McCarthy, Jacob Kyle Startt, Remi Philippe Michel Dingreville, Aidan P. Thompson, Mitchell Wood	Abstract – 2022 Abstract	2022
Building a new generation of multiscale materials models with machine-learned interatomic potentials Machine Learning Deep Learning Workshop Megan Jeanne McCarthy, Aidan P. Thompson, Mitchell Wood	Conference Presentation – 2022 Conference Presentation	2022
Machine-Learned Interatomic Potential Development for H Trapping in ZrC strengthened W Tms 2023 Ember Layne Sikorski, Mary Alice Cusentino, Megan Jeanne McCarthy, Julien Tranchida, Mitchell Wood, Aidan P. Thompson	Abstract – 2022 Abstract	2022
Molecular Dynamics Simulations of Mixed Materials Effects in Tungsten Tms 2023 Mary Alice Cusentino, Megan Jeanne McCarthy, Ember Layne Sikorski, Mitchell Wood, Aidan P. Thompson	Abstract – 2022 Abstract	2022
Exploring refractory complex concentrated alloy behavior in the fusion reactor environment with a machine-learned interatomic potential PSI-2525th International Conference on Plasma Surface Interaction in Controlled Fusion Devices Megan Jeanne McCarthy, Jacob Kyle Startt, Remi Philippe Michel Dingreville, Mitchell Wood	Conference Presentation – 2022 Conference Presentation	2022
Machine-Learned Interatomic Potential Development for W-ZrC for Nuclear Fusion Gordon Research ConferenceComputational Materials Science and Engineering Ember Layne Sikorski, Mary Alice Cusentino, Megan Jeanne McCarthy, Julien Tranchida, Mitchell Wood, Aidan P. Thompson	Abstract – 2022 Abstract	2022
Machine Learned Interatomic Potential Development of W-ZrC for Fusion Divertor Microstructure and Thermomechanical Properties Multiscale Materials Modeling Ember Layne Sikorski, Mary Alice Cusentino, Megan Jeanne McCarthy, Julien Tranchida, Mitchell Wood, Aidan P. Thompson	Abstract – 2022 Abstract	2022
Training Machine Learned Interatomic Potentials for Chemical Complexity - Application to Refractory CCAs Gordon Research Conference - Computational Materials Science and Engineering Megan Jeanne McCarthy, Jacob Kyle Startt, Remi Philippe Michel Dingreville, Aidan P. Thompson, Mitchell Wood	Abstract – 2022 Abstract	2022
Exploring the behavior of MoNbTaTi refractory CCAs across composition space using a machine learned interatomic potential The 10th International Conference on Multiscale Materials Modeling Megan Jeanne McCarthy, Jacob Kyle Startt, Remi Philippe Michel Dingreville, Aidan P. Thompson, Mitchell Wood	Abstract – 2022 Abstract	2022
Molecular Dynamics Modeling of Hydrogen and Nitrogen Implantation in Tungsten Using Machine Learned Interatomic Potentials The 10th International Conference on Multiscale Materials Modeling Mary Alice Cusentino, Megan Jeanne McCarthy, Ember Layne Sikorski, Mitchell Wood, Aidan P. Thompson	Abstract – 2022 Abstract	2022
Training Machine Learned Interatomic Potentials for Chemical Complexity - Application to Refractory CCAs APS March Meeting 2022 Megan Jeanne McCarthy, Jacob Kyle Startt, Remi Philippe Michel Dingreville, Mitchell Wood	Conference Presentation – 2022 Conference Presentation	2022
Expediting the materials discovery process of MPEAs through atomistic modeling and machine learning techniques TMS 2022 Annual Meeting & Exhibition Jacob Kyle Startt, Mitchell Wood, Megan Jeanne McCarthy, Andrew Kustas, Sean Donegan, Remi Philippe Michel Dingreville	Conference Presentation – 2022 Conference Presentation	2022
Local and Near-Boundary Environments in NbMoTaW Refractory Multi-Principal Element Alloy 2nd World Congress on High Entropy Alloys (HEA 2021) Doruk Aksoy, Megan Jeanne McCarthy, Ian Geiger, Timothy Rupert	Conference Presentation – 2021 Conference Presentation	2021
Exploring refractory complex concentrated alloy behavior in the fusion reactor environment with a machine-learned interatomic potential PSI-25 25th International Conference on Plasma Surface Megan Jeanne McCarthy, Jacob Kyle Startt, Remi Philippe Michel Dingreville, Mitchell Wood, Aidan P. Thompson	Abstract – 2021 Abstract	2021
Training Machine Learned Interatomic Potentials for Chemical Complexity - Application to Refractory CCAs American Physical Society, March Meeting 2021 Megan Jeanne McCarthy, Jacob Kyle Startt, Remi Philippe Michel Dingreville, Mitchell Wood	Abstract – 2021 Abstract	2021
Document Title	Type	Year