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Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery
Author(s) -
PyzerKnapp Edward O.,
Li Kewei,
AspuruGuzik Alan
Publication year - 2015
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.201501919
Subject(s) - artificial neural network , perceptron , computer science , energy (signal processing) , throughput , scale (ratio) , artificial intelligence , machine learning , nanotechnology , materials science , process engineering , engineering , telecommunications , physics , quantum mechanics , wireless
Here, the employment of multilayer perceptrons, a type of artificial neural network, is proposed as part of a computational funneling procedure for high‐throughput organic materials design. Through the use of state of the art algorithms and a large amount of data extracted from the Harvard Clean Energy Project, it is demonstrated that these methods allow a great reduction in the fraction of the screening library that is actually calculated. Neural networks can reproduce the results of quantum‐chemical calculations with a large level of accuracy. The proposed approach allows to carry out large‐scale molecular screening projects with less computational time. This, in turn, allows for the exploration of increasingly large and diverse libraries.