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GTC ON-DEMAND

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Abstract:
Nucleation is ubiquitous, the start of numerous phase-transition processes such as the growth of a crystal seed from a liquid during freezing, the spontaneous self-assembly of a virus from protein subunits, and the onset of the early inflationary universe shortly after the big bang. We'll discuss how we're exploring the limits of nucleation theory. We will explain how pushing a system faster and further from equilibrium allows us to probe the formation of matter under extreme conditions in the laboratory and on newly discovered super-Earths outside our solar system. We will share recent discoveries of nucleation made possible by algorithmic performance improvements recently enabled by GPUs. We'll also highlight how learning to control nucleation processes at the nanoscale allows us to design a new generation of materials that will revolutionize digital technology, clean-energy generation and storage, and nanoscale medicine.
Nucleation is ubiquitous, the start of numerous phase-transition processes such as the growth of a crystal seed from a liquid during freezing, the spontaneous self-assembly of a virus from protein subunits, and the onset of the early inflationary universe shortly after the big bang. We'll discuss how we're exploring the limits of nucleation theory. We will explain how pushing a system faster and further from equilibrium allows us to probe the formation of matter under extreme conditions in the laboratory and on newly discovered super-Earths outside our solar system. We will share recent discoveries of nucleation made possible by algorithmic performance improvements recently enabled by GPUs. We'll also highlight how learning to control nucleation processes at the nanoscale allows us to design a new generation of materials that will revolutionize digital technology, clean-energy generation and storage, and nanoscale medicine.  Back
 
Topics:
Computational Physics, Algorithms & Numerical Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9235
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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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