Nano-Material Configuration Design with Deep Surrogate Langevin Dynamics



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We consider the problem of optimizing by sampling under multiple black-box constraints in nano-material design. We leverage the posterior regularization framework and show that the constraint satisfaction problem can be formulated as sampling from a Gibbs distribution. The main challenges come from the black-box nature of the constraints obtained by solving complex and expensive PDEs. To circumvent these issues, we introduce Surrogate-based Constrained Langevin dynamics for black-box sampling. We devise two approaches for learning surrogate gradients of the black-box functions: first, by using zero-order gradients approximations; and second, by approximating the Langevin gradients with deep neural networks. We prove the convergence of both approaches when the target distribution is -concave and smooth. We also show the effectiveness of our approaches over Bayesian optimization in designing optimal nano-porous material configurations that achieve low thermal conductivity and reasonable mechanical stability.

This paper has been published at ICLR 2020

Please cite our work using the BibTeX below.

title={Nano-Material Configuration Design with Deep Surrogate Langevin Dynamics},
author={Thanh V. Nguyen and Youssef Mroueh and Samuel Hoffman and Payel Das and Pierre Dognin and Giuseppe Romano and Chinmay Hegde},
booktitle={ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations},
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