Collaborative Research: Framework: Machine Learning Materials Innovation Infrastructure

合作研究：框架：机器学习材料创新基础设施

• 批准号: 1931306
• 负责人: Benjamin Blaiszik
• 金额: $ 40万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931306&HistoricalAwards=false
• 关键词: Collaborative; Research; Framework; Machine; Learning

Machine learning is rapidly changing our society, with computers recently gaining skills in many new tasks. These tasks range from understanding language to driving cars. Materials science and engineering is also being transformed. Many tasks are becoming increasingly accessible to machine learning algorithms. These range from predicting new data to analyzing images. Many basic machine learning algorithms are readily available. However the overall workflow involved in the application of machine learning for materials problems is still largely executed by hand. Getting results out is still done by traditional methods like publishing articles. There is an enormous opportunity to accelerate the growth and impact of machine learning in materials research. This requires improved cyberinfrastructure. This project will develop an approach to accelerate the entire machine learning workflow. Its output will include tools to easily develop datasets, manage model development, and output models. These will be reusable and reproducible for future use. This project will enable materials scientists and engineers to rapidly develop and deploy machine learning models. More importantly, the entire materials community will be able to quickly access these models. It will transform how we discover and develop advanced materials.The project will have three major technical components: (i) A MAterials Simulation Toolkit for Machine Learning (MAST-ML) with workflow tools that will enable local or cloud-based multistep, automated execution of complex machine learning data analysis and model training, codified best practices, increased access to machine learning methods for non-experts, and accelerated model development; (ii) The Foundry Materials Informatics Environment that will provide flexible, integrated, cloud-based management of machine learning materials science and engineering projects, from organizing data to developing models to disseminating results that are machine and human accessible and reproducible in ways that support a networked materials innovation ecosystem, (iii) Representative science applications of machine learning materials science and engineering projects that will support infrastructure development and promotion, as well as demonstrate best practices on state-of-the-art materials science and engineering problems. In addition to its impact on materials science and engineering, this project will develop students and young researchers with the interdisciplinary skills of machine learning and materials science and engineering, and promote these new ideas to the broader materials community. This award is jointly supported by the NSF Office of Advanced Cyberinfrastructure, and the Division of Materials Research within the NSF Directorate of 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.

机器学习正在迅速改变我们的社会，计算机最近在许多新任务中获得了技能。这些任务范围从理解语言到驾驶汽车。材料科学和工程也在发生转变。机器学习算法越来越容易完成许多任务。从预测新数据到分析图像。许多基本的机器学习算法都是现成的。然而，将机器学习应用于材料问题的整个工作流程仍然主要是手工执行的。获得结果仍然是通过发表文章等传统方法完成的。加速机器学习在材料研究中的发展和影响是一个巨大的机会。这需要改善网络基础设施。该项目将开发一种加速整个机器学习工作流程的方法。它的输出将包括易于开发数据集、管理模型开发和输出模型的工具。这些将是可重复使用和可复制的，以供将来使用。该项目将使材料科学家和工程师能够快速开发和部署机器学习模型。更重要的是，整个材料社区将能够快速访问这些模型。它将改变我们发现和开发先进材料的方式。该项目将有三个主要的技术组成部分：(i)机器学习材料模拟工具包（MAST-ML），其工作流程工具将支持本地或基于云的多步骤，自动执行复杂的机器学习数据分析和模型训练，编纂最佳实践，增加非专家使用机器学习方法的机会，并加速模型开发；（ii）铸造材料信息环境，将为机器学习材料科学和工程项目提供灵活、集成、基于云的管理，从组织数据到开发模型，再到传播结果，这些结果以支持网络材料创新生态系统的方式可供机器和人类访问和复制；（iii）机器学习材料科学和工程项目的代表性科学应用，将支持基础设施的发展和推广，并展示最先进的材料科学和工程问题的最佳实践。除了对材料科学与工程产生影响外，该项目还将培养具有机器学习和材料科学与工程跨学科技能的学生和年轻研究人员，并将这些新思想推广到更广泛的材料界。该奖项由美国国家科学基金会高级网络基础设施办公室和美国国家科学基金会数学和物理科学理事会材料研究部联合支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。