Typical scientific software designs make rigid assumptions regarding programming language and data structures, frustrating software interoperability and scientific collaboration. Component-based software engineering is an emerging approach to managing the increasing complexity of scientific software. Component technology facilitates code interoperability and reuse. Through the adoption of methodology and tools developed by the Common Component Architecture Forum, we have developed a component architecture for molecular structure optimization. Using the NWChem and Massively Parallel Quantum Chemistry packages, we have produced chemistry components that provide capacity for energy and energy derivative evaluation. We have constructed geometry optimization applications by integrating the Toolkit for Advanced Optimization, Portable Extensible Toolkit for Scientific Computation, and Global Arrays packages, which provide optimization and linear algebra capabilities. We present a brief overview of the component development process and a description of abstract interfaces for chemical optimizations. The components conforming to these abstract interfaces allow the construction of applications using different chemistry and mathematics packages interchangeably. Initial numerical results for the component software demonstrate good performance, and highlight potential research enabled by this platform.
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JMIR Form Res
January 2025
ICMR-National Institute for Research in Digital Health and Data Science, Ansari Nagar, New Delhi, 110029, India, 91 7840870009.
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View Article and Find Full Text PDFFront Pharmacol
January 2025
Department of Pharmacy, College of Medicine and Health Sciences, Debre Markos University, Debre Markos, Ethiopia.
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View Article and Find Full Text PDFIntroduction: Tuberculosis (TB) poses a significant threat to global health, with millions of new infections and approximately one million deaths annually. Various modeling efforts have emerged, offering tailored data-driven and physiologically-based solutions for novel and historical compounds. However, this diverse modeling panorama may lack consistency, limiting result comparability.
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December 2024
JST-CREST, Saitama, Japan.
In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. We focus on the constituents of polycrystalline materials (i.e.
View Article and Find Full Text PDFMonte Carlo Methods Appl
December 2024
Computer Languages and Systems Software Group, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
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