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2017 GTC San Jose

S7517 - Mastering Computational Chemistry with Deep Learning

Session Speakers
Session Description

Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. We'll demonstrate several examples how a deep neural network trained on quantum mechanical (QM) DFT calculations can learn an accurate and fully transferable potential for organic molecules and materials. In a recent paper, (1) we introduced ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies), or ANI for short. ANI is a new method designed with the intent of developing fully transferrable neural network potentials that utilize symmetry functions to build single-atom atomic environment vectors as a molecular representation. Through a series of case studies, we'll show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems than those included in the training dataset, with root mean square errors as low as 0.56 kcal/mol. As the results clearly show, the ANI method is a potential game changer for molecular simulation.


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Talk
Computational Biology Computational Chemistry Deep Learning and AI Healthcare and Life Sciences
Healthcare & Life Sciences Higher Education / Research
25 minutes
Session Schedule