CORDIS Project
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This project develops a machine learning approach to predict molecular interactions by combining physical models with data-driven techniques. It aims to provide accurate molecular energy calculations for large molecules, enhancing computational efficiency in chemistry and materials science.
Statistical-learning approaches are emerging as powerful alternatives to direct approaches to solving the electronic Schrödinger equation for determining the energy and other properties of molecules.
Despite the recent success of methods like deep neural networks, these methods are limited to relatively small molecules.
The issue is that predicting long-range intermolecular interactions with machine learning requires sampling the vast diversity of chemical environments that occur on an extended…
UNIVERSITE DU LUXEMBOURG
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