The main purpose of the roadmap is to create effective AI systems for quantum calculations, including the optimization of work on graphic processors (GPU). In order to do this, it is necessary to develop database creation methods for modeling and take into account the long -range interactions in molecules. This approach promises to increase the accuracy of accelerating modeling and calculations.
Researchers pay special attention to the potential-ai systems learned by the machine trained on partial data from quantum calculations. This allows them to predict their tool interactions much faster than classical methods. However, he said it is necessary to solve the adaptation of algorithms to work with stronger GPU to get even more accuracy.
Russian scientists showed their development in the field of inertia potentials (MTP), which was not lower in the world analogue, but exceeding performance. These systems can be taught even in separate parts of complex molecules.
Source: Ferra
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