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

该项目旨在建立一种新的技术范例和开发机器学习(ML)模型所需的软件基础设施，该模型能够预测看不见的分子和材料系统/结构的性质，从而能够以比现有第一原理(量子)方法所提供的显著更高的吞吐量来建模原子行为和计算发现新的分子和材料。支持ML的材料发现将在应对能源可持续性等现代社会挑战方面发挥关键作用，因此，该项目开发的技术和基础设施预计将在许多科学和工程领域产生革命性影响。该平台促进了对大量第一性原理和实验数据的访问、共享和发现，通过支持以以前无法访问的规模开发ML模型，消除了低效并加速了科学发现。为了实现这些目标，该项目与Amazon Web Services(AWS)合作实施，为开发专门的开源工具大规模培训ML模型提供必要的技术诀窍。该项目致力于促进高等教育的多样性、公平性和包容性，因此它纳入了各种机制，以便在四所参与大学(纽约大学、明尼苏达大学、佛罗里达大学和杨百翰大学)的研究活动中纳入代表性不足的低收入学生(高中生和本科生)，此外还为研究生提供指导、编写教材和举办旨在产业推广和培训的讲习班。为了确保平台/软件与社区需求之间的一致性，该项目得到了网络基础设施开发、机器学习、材料和化学科学以及STEM推广方面的专家咨询委员会的支持，他们评估并向私营部门提供战略建议。作为这项工作基础的关键技术进步是“基础模型”，这是一种建立ML系统的方法，在这种方法中，根据非常大量的多样化和容易获得的数据训练的模型可以通过少量的额外模型拟合(微调)来适应不同的应用。因此，该项目专注于开发一个称为费马的基础模型，用于分子和材料性质预测，以及用于模拟原子行为的ML原子间势。费马将通过一个集成的自适应平台以软件包和在线框架的形式提供，用于开发和部署材料和化学应用的专门ML模型，称为“费马应用”。与AWS合作，该项目寻求开发开源软件，用于大规模培训像Fermat这样的基础模型，以处理大量高度异质和多模式的数据。高数据需求将通过与大量材料和分子数据存储库、标准组织和现有的网络基础设施合作，利用并显著扩展ColabFit Exchange，这是一个针对ML模型的培训而优化的第一原理和实验数据的在线存储库。Fermat及其衍生的任何ML模型旨在支持不确定性量化(基于信息几何、贝叶斯和频率法)，以确保预测的稳健性。作为指导性的目标应用，该项目考虑了两个具有科学意义的问题：2D材料驱动催化和分子晶体多态预测。这一奖项由高级网络基础设施办公室由数学和物理科学局内的材料研究部联合支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.