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James Hickman (Assoc)

NRC Postdoctoral Fellow

Research interests

Classical interatomic potential development
Machine learning and artificial neural networks
Thermodynamics and Statistical Mechanics
Solid State Physics
Theoretical and Computational Materials Science
Classical atomic simulations

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Publications

The impact of alloying on defect-free nanoparticles exhibiting softer but tougher behavior

May 4, 2021

Author(s)

Anuj Bisht, Raj Kiran Koju, Yuanshen Qi, James Hickman, Yuri Mishin, Eugen Rabkin

Coarsening of Solid Particles in Liquid Pb-Sn Alloys: Reinterpretation of Data in Light of the TIDC Theory of Coarsening

April 1, 2021

Author(s)

James Hickman, Yuri Mishin, Vidvuds Ozolins, Alan Ardell

Development of a general-purpose machine-learning interatomic potential for aluminum by the physically-informed neural network method

November 19, 2020

Author(s)

Ganga Purja Pun, Vesselin Yamakov, James Hickman, Edward Glaessgen, Yuri Mishin

Interatomic potentials constitute the key component of large-scale atomistic simulations of materials. The recently proposed physically-informed neural network

View more Publications

Created May 31, 2018, Updated December 9, 2022

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