GOALI: Frameworks: At-Scale Heterogeneous Data based Adaptive Development Platform for Machine-Learning Models for Material and Chemical Discovery

This project seeks to establish a new technological paradigm and the software infrastructure necessary for the development of Machine Learning (ML) models capable of predicting the properties of unseen molecular and materials systems/structures, thus enabling modeling of atomic behavior and the computational discovery of new molecules and materials at significantly higher throughput than afforded by existing first principles (quantum) methods. ML-enabled materials discovery is poised to play a critical role in addressing modern societal challenges such as energy sustainability and, as such, the technology and infrastructure developed by this project are expected to have a transformative impact across many scientific and engineering domains.

The platform facilitates access, sharing, and discovery of vast amounts of first principles and experimental data, removing inefficiencies and accelerating scientific discovery by enabling the development of ML models on a scale previously inaccessible. To achieve these goals, this project is carried out in partnership with Amazon Web Services (AWS), providing the necessary know-how for the development of specialized open-source tools for training ML models at scale.

This project is committed to the advancement of diversity, equity and inclusiveness in higher education, and as such it incorporates a variety of mechanisms to include underrepresented and low-income students (high-school and undergraduate) in its research activities across the four participating universities (New York University, University of Minnesota, University of Florida, and Brigham Young University), in addition to the mentoring of graduate students, the development of teaching materials, and workshops aimed at industrial outreach and training. To assure alignment between the platform/software and community needs, this project is supported by an Advisory Board of experts in cyberinfrastructure development, machine learning, material and chemical sciences, and STEM outreach who evaluate and provide strategic advice to the PIs.

The key technological advance that serves as the basis of this work are "foundation models", an approach for building ML systems in which a model trained on extremely large amounts of diverse and easily available data can be adapted to diverse applications with a small amount of additional model fitting (fine-tuning). This project thus focuses on the development of a foundation model, called FERMat, for molecular and material property prediction, and ML interatomic potentials for modeling atomic behavior.

FERMat is to be delivered via an integrated adaptive platform in the form of a software package and an online framework for developing and deploying specialized ML models for materials and chemistry applications, called "FERMat Apps". In collaboration with AWS this project seeks to develop open-source software for training foundation models like FERMat at scale on large amounts of highly heterogeneous and multi-modal data.

The high data needs will be met by leveraging and significantly expanding the ColabFit Exchange, an online repository of first principles and experimental data optimized for training of ML models, in cooperation with a large number of materials and molecular data repositories, standards organizations, and existing cyberinfrastructures. FERMat and any ML model derived from it is designed to support uncertainty quantification (based on information geometry, Bayesian, and frequentist approaches) to ensure the robustness of predictions.

As guiding target applications, this project considers two problems of scientific interest: 2D material driven catalysis and the prediction of molecular crystal polymorphs.

This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Materials Research within the Directorate for Mathematical and Physical Sciences.

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.