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GTC ON-DEMAND

HPC and AI
Presentation
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A Machine Learning Method in Computational Materials Science
Abstract:
We'll discuss our work using neural networks to fit the interatomic potential function and describe how we tested the network's potential function in atomic simulation software. This method has lower computational cost than traditional density functional theory methods. We'll show how our work is applicable to different atom types and architectures and how it avoids relying on the physical model. Instead, it uses a purely mathematical representation, which reduces the need for human intervention.
 
Topics:
HPC and AI, Computational Physics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9843
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