Name | Ms. Kavindri Ranasinghe |
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Organization or Institution | University of Florida |
Presentation Type | Poster |
Topic | Computational Chemistry |
Title | ANAKIN-ME: Using deep learning to develop a chemically accurate and universal potential for the prediction of organic reactions |
Author(s) | Kavindri D. Ranasinghe1, Justin S. Smith1, Olexandr Isayev2, Adrian E. Roitberg1 |
Author Institution(s) | 1. Department of Chemistry, University of Florida, Gainesville FL, USA |
Abstract | Organic chemistry serves as the basis for life, making it the backbone in the synthesis of biologically active compounds, pharmaceuticals and therapeutically important substances. A proper computational description of the physics involved in organic reactions will assist in solving many chemical problems. However, traditional force fields based on classical principles are unable to model bond dissociations, while quantum mechanical (QM) approaches are computationally too intense. Machine learned (ML) potentials have recently shown to be accurate in predicting reaction profiles. However, these ML potentials have only been parametrized for a specific reaction and therefore, lack transferability. To overcome these challenges, we develop the necessary tools and data set to train ANAKIN-ME (ANI), a new type of universal deep learned atomistic potential, to predict reaction profiles with an accuracy of DFT at |