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GTC ON-DEMAND

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Abstract:

Molecular dynamics simulation of matter far from equilibrium presents one possible approach to the discovery of non-equilibrium constitutive relations but are limited to coarse-grained hamiltonians that include electronic effects only implicitly. We'll explore the possibility that deep neural networks -- when trained over the appropriate atomic states -- may provide the hamiltonian for a molecular dynamics simulation, thus providing a sub-grid representation of variables at spatial and temporal scales that cannot otherwise be explicitly resolved. The advent of GPU-accelerated training of deep neural networks, and specifically recent improvements to the CuDNN library, now makes it feasible to handle the large and high dimensional datasets incumbent to such systems. Finally, we'll elucidate a few of the challenges inherent in DNN-coupled dynamics, such as obeying the constraints of momentum and energy conservation.

Molecular dynamics simulation of matter far from equilibrium presents one possible approach to the discovery of non-equilibrium constitutive relations but are limited to coarse-grained hamiltonians that include electronic effects only implicitly. We'll explore the possibility that deep neural networks -- when trained over the appropriate atomic states -- may provide the hamiltonian for a molecular dynamics simulation, thus providing a sub-grid representation of variables at spatial and temporal scales that cannot otherwise be explicitly resolved. The advent of GPU-accelerated training of deep neural networks, and specifically recent improvements to the CuDNN library, now makes it feasible to handle the large and high dimensional datasets incumbent to such systems. Finally, we'll elucidate a few of the challenges inherent in DNN-coupled dynamics, such as obeying the constraints of momentum and energy conservation.

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Topics:
Artificial Intelligence and Deep Learning, Computational Biology & Chemistry, Computational Physics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7373
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