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Tom Arbaugh

Showing results (1-10 of 4) with videos related to

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The Journal of Chemical Physics|January 15, 2025
Computationally efficient machine-learned model for GST phase change materials via direct and indirect learningOwen R Dunton, Tom Arbaugh, Francis W Starr
The Journal of Physical Chemistry. C, Nanomaterials and Interfaces|June 17, 2026
Characterizing the Peierls-Like Distortion and Phase Behavior in Amorphous Ge-Sb Mixtures Using a Machine-Learned PotentialOwen R Dunton, Tom Arbaugh, Francis W Starr
STAR Protocols|January 3, 2023
Unified machine learning protocol for copolymer structure-property predictionsLei Tao, Tom Arbaugh, John Byrnes, et al.
Nanoscale|November 23, 2022
Molecular self-assembled monolayers anomalously enhance thermal conductance across polymer-semiconductor interfacesJinlong He, Lei Tao, Weikang Xian, et al.
Pageof 1

Showing results (1-10 of 4) with videos related to

Sort By:
Pageof 1
The Journal of Chemical Physics|January 15, 2025
Computationally efficient machine-learned model for GST phase change materials via direct and indirect learningOwen R Dunton, Tom Arbaugh, Francis W Starr
The Journal of Physical Chemistry. C, Nanomaterials and Interfaces|June 17, 2026
Characterizing the Peierls-Like Distortion and Phase Behavior in Amorphous Ge-Sb Mixtures Using a Machine-Learned PotentialOwen R Dunton, Tom Arbaugh, Francis W Starr
STAR Protocols|January 3, 2023
Unified machine learning protocol for copolymer structure-property predictionsLei Tao, Tom Arbaugh, John Byrnes, et al.
Nanoscale|November 23, 2022
Molecular self-assembled monolayers anomalously enhance thermal conductance across polymer-semiconductor interfacesJinlong He, Lei Tao, Weikang Xian, et al.
Pageof 1