Computational Chemistry using Cloud HPC

High-Performance Computing (HPC) is essential for tasks like battery simulation or drug development modeling in industries like material science and biopharmaceutical research. These fields rely on HPC for its precision and power. However, moving these tasks to the cloud, while beneficial for scalability, introduces challenges in resource management, data handling, and performance consistency. Covalent Cloud offers a fully Pythonic and backend-agnostic approach to creating and managing high-compute workflows, allowing researchers to concentrate on addressing complex scientific problems without getting entangled in the technicalities of cloud computing and script management.

Let’s dive into a computational chemistry example for molecular modeling, specifically calculating the nitrogen bond energy, using Covalent Cloud. Calculating the single point energy is common exercise in material science that also scales well when analyzing other complex systems.

In traditional cloud settings, the initial step often involves creating a Docker image to set up the required environment. This can be a time-consuming and intricate process. Covalent Cloud, however, simplifies this setup significantly.

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