Frameworks: MATCSSI 2.0: Transforming Interoperability, Automation, and Cloud Integration of Many-Body Electronic Structure Calculations

Many important properties of materials, from light absorption and emission to electrical resistivity and superconductivity, arise from complex interactions between electrons, crystal vibrations, and light. Accurately predicting these properties requires advanced many-body theoretical and computational methods beyond the standard approach in computational materials science based on density functional theory. However, these many-body methods remain difficult due to their mathematical complexity and fragmented software landscape. This project enables broader access to advanced computational methods for materials modeling and design at the atomic scale by developing MATCSSI 2.0, a cloud-integrated platform that streamlines complex many-body calculations. By combining user-friendly tools, interoperability among widely used software, and interactive learning resources, this project lowers barriers to entry and promotes reproducibility and transparency in computational materials science. This project advances the predictive power and accessibility of many-body electronic structure methods that go beyond density functional theory (DFT). This project develops MATCSSI 2.0, a platform that combines a cloud portal hosted at the Texas Advanced Computing Center with interoperable software (such as EPW, BerkeleyGW, and SternheimerGW), a universal abstraction layer (EPWpy), intelligent user support, and end-to-end learning modules. This platform enables widespread adoption of many-body materials modeling and design methods by connecting to a broad array of high-performance computing infrastructure. MATCSSI 2.0 enables researchers to study complex quantum phenomena involving coupled electrons, phonons, and photons, both in and out of equilibrium. It supports research into advanced materials for optoelectronics, superconductivity, and quantum technologies, and facilitates the generation of high-quality many-body datasets for AI-driven materials discovery. The platform is deployed on the cloud via the Texas Advanced Computing Center and is accessible to all researchers without restrictions. Broadening access to these advanced quantum simulation methods helps accelerate the discovery of new materials for advanced applications, such as microelectronics and quantum technologies. It facilitates training a competitive STEM workforce with interdisciplinary expertise in materials science and high-performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.