Elements: Collaborative Research: Community-driven Environment of AI-powered Noise Reduction Services for Materials Discovery from Electron Microscopy Data

The goal of this project is to create cyberinfrastructure (CI) powered by artificial intelligence (AI) for sustained innovation in materials science. Deep understanding of materials is critical for progress in technologies related to energy, communication, construction, transportation and human health. The revolutionary progress of deep learning has been enabled by the availability of open-source AI models and open-access benchmark databases.

However, the existing codebases and datasets relevant to image processing focus mostly on photographic images. In order to promote the sustained development of AI technology that can have significant impact in materials science, it is critical to provide data and AI models that are tailored to this domain. The developed CI will address this need by providing software to process images obtained from electron-microscopes, a technique enabling atoms to be visualized, and has the potential to enable transformative breakthroughs in varied and important areas of materials science.

The CI is explicitly designed to foster the growth of a sustainable community of users and developers of AI technology at the intersection of the materials and data science communities, and to empower materials scientists to simulate their own datasets and develop their own AI models for scientific discovery. The developed AI-powered CI will therefore enable transformative progress in atomic-level understanding of materials, which will have broader impacts in health, energy, environment, and biotechnology.

The CI environment will contribute to training materials scientists in AI technology, connecting them to the AI community, and providing software, data, and support materials to initiate them in AI-powered research. Educational and outreach plans are designed to facilitate interactions between the materials science and AI communities. Outreach activities specifically targeted to the general public, and to high-school teachers and their students, will expose them to materials science, electron microscopy, and AI.

The project is committed to providing opportunities to women and underrepresented groups and will prioritize diversity in collaboration with the NYU Center for Data Science diversity committee.

Developing a fundamental understanding of atomic level structure and dynamics is critical for transformative advances in materials science. Aberration-corrected transmission electron microscopy is a primary tool to accomplish this goal. Unfortunately, the information content of microscopy data may be severely limited by poor signal-to-noise ratios.

This is particularly true for radiation sensitive materials and experiments where high time resolution is required to investigate dynamic kinetic processes. AI methodology can exploit prior information about material structure by training deep neural nets with extensive simulations. These approaches may significantly outperform existing state-of-the-art methods, especially for non-periodic structures, including defects, interfaces, and surfaces.

The developed CI will provide AI noise reduction services which will yield immediate advances and impacts for zeolites, metal organic frameworks, protein-material interfaces, liquid phase nucleation and growth, liquid-solid interfaces, and fluxional behavior in catalytic nanoparticles. In addition, the project will advance methodology for the design of AI-oriented CI.

The CI is strategically designed to create a holistic environment for the use and development of AI technology in a specific scientific domain. It will attract domain scientists with little AI expertise, by providing software where the AI technology is transparent to the end user. Exposure to the technology will motivate the scientific community to design and train their own models, which will be facilitated by the open-source codebase in the AI repository.

The open-access database combined with the repository will attract AI practitioners with little domain expertise, by giving them access to well-curated data and a clear specification of the relevant AI tasks. These services will be jump-started and supported through multiple educational and outreach activities.

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.