An AI-driven, Cloud-based, Materials Discovery Platform for Nanomaterial Structure: PDFitc
We have developed a cloud-based, AI driven, platform for nanomaterial structure determination: “PDF in the cloud” (PDFitc.org), which consists of various applications for nanostructure determination, including a ML-based classifier for discovering material symmetry from a measured dataset, a high-throughput structure screening tool for predicting the structure of a measured signal, and a data-similarity visualization tool for finding changes in a signal in a time or temperature series.
S. Billinge, Q. Du, D. Hsu (Columbia U.)
Discovery of novel functional materials relies on a quantitative understanding of nanostructure
There is a need for advanced artificial intelligence (AI) and machine learning (ML) approaches to nanostructure determination
We have developed a cloud-based, AI driven, platform for nanomaterial structure determination: “PDF in the cloud” (PDFitc.org).
It consists of various applications for nanostructure determination, including a ML-based classifier for discovering material symmetry from a measured dataset, a high-throughput structure screening tool for predicting the structure of a measured signal, and a data-similarity visualization tool for finding changes in a signal in a time or temperature series.
Uploaded data may be shared with collaborators and machine learned in the future for new materials discoveries.
The suite of Apps will be augmented over time allowing a wider range and more sophisticated analyses to be carried out and enriching the underlying database of PDFs that can then be used for machine learning