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Artificial intelligence helps explore chemistry frontiers

The ability to simulate the behavior of systems at the atomic level represents a powerful tool for everything from drug design to materials discovery. A team led by Los Alamos National Laboratory .

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SMU Chemist and Colleagues Develop Machine Learning Model for Atomic-level Interactions

Machine learning interatomic potentials (MLIP)s have become an efficient and less expensive alternative to traditional quantum chemical simulations.

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SMU Chemist Leads in Creating Machine Learning Model for Atoms

SMU Chemist Leads in Creating Machine Learning Model for Atoms
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Researchers develop new machine learning method for modeling of chemical reactions

Researchers from Carnegie Mellon University and Los Alamos National Laboratory have used machine learning to create a model that can simulate reactive processes in a diverse set of organic materia .

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Carnegie Mellon Researchers Develop New Machine Learning Method For Modeling Of Chemical Reactions

Carnegie Mellon Researchers Develop New Machine Learning Method For Modeling Of Chemical Reactions
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