Generating Multiscale Amorphous Molecular Structures Using Deep Learning: A Study in 2D
Autor: | Yoshua Bengio, David Yu-Tung Hui, Lena Simine, Michael Kilgour, Nicolas Gastellu |
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Rok vydání: | 2020 |
Předmět: |
Materials science
Mesoscale meteorology Nanoparticle 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Amorphous solid Amorphous carbon Monolayer Benchmark (computing) General Materials Science Statistical physics Physical and Theoretical Chemistry 0210 nano-technology Nanoscopic scale Scaling |
Zdroj: | The journal of physical chemistry letters. 11(20) |
ISSN: | 1948-7185 |
Popis: | Amorphous molecular assemblies appear in a vast array of systems: from living cells to chemical plants and from everyday items to new devices. The absence of long-range order in amorphous materials implies that precise knowledge of their underlying structures throughout is needed to rationalize and control their properties at the mesoscale. Standard computational simulations suffer from exponentially unfavorable scaling of the required compute with system size. We present a method based on deep learning that leverages the finite range of structural correlations for an autoregressive generation of disordered molecular aggregates up to arbitrary size from small-scale computational or experimental samples. We benchmark performance on self-assembled nanoparticle aggregates and proceed to simulate monolayer amorphous carbon with atomistic resolution. This method bridges the gap between the nanoscale and mesoscale simulations of amorphous molecular systems. |
Databáze: | OpenAIRE |
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