AI Article Synopsis
- Semi-empirical quantum chemical methods trade accuracy for faster calculations on large molecules, which is becoming increasingly important for tasks like interactive studies and high-throughput screening.
- Recent shifts in focus prioritize calculation runtimes, as many fast computations require a lot of manual setup that can slow down high-throughput processes.
- This study evaluates how different semi-empirical methods impact calculation speed and data transfer rates across various computing platforms, with findings integrated into the open-source software SCINE Sparrow.
Article Abstract
Semi-empirical quantum chemical approaches are known to compromise accuracy for the feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and data-driven machine learning has shifted the emphasis toward calculation runtimes recently. This comes with new constraints for the software implementation as many fast calculations would suffer from a large overhead of the manual setup and other procedures that are comparatively fast when studying a single molecular structure, but which become prohibitively slow for high-throughput demands. In this work, we discuss the effect of various well-established semi-empirical approximations on calculation speed and relate this to data transfer rates from the raw-data source computer to the results of the visualization front end. For the former, we consider desktop computers, local high performance computing, and remote cloud services in order to elucidate the effect on interactive calculations, for web and cloud interfaces in local applications, and in world-wide interactive virtual sessions. The models discussed in this work have been implemented into our open-source software SCINE Sparrow.
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| http://dx.doi.org/10.1063/5.0136404 | DOI Listing |
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