AI Institute: Planning: Institute for AI-Enabled Materials Discovery, Design, and Synthesis

人工智能研究所：规划：人工智能材料发现、设计和合成研究所

• 批准号: 2020243
• 负责人: Vasant Honavar
• 金额: $ 50万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020243&HistoricalAwards=false
• 关键词: AI; Institute; Planning; Enabled; Materials

Non-technical Description: Scientific progress is being increasingly enabled by the ability to examine natural phenomena through the use of computation. The emergence of “big data”, and advances in machine learning have dramatically accelerated some of the key steps in science, e.g., data acquisition and model fitting. However, other key elements of the scientific process, e.g., generating hypotheses, designing, prioritizing and executing experiments, integrating data, models, and simulations, drawing inferences and constructing explanations, reconciling scientific arguments, and communicating across disciplines, remain largely untouched by the advances in artificial intelligence (AI). Accelerating scientific progress, potentially by several orders of magnitude, by effectively addressing these bottlenecks presents a grand challenge for AI. Materials discovery, design and synthesis provides an excellent testbed for addressing the AI grand challenges presented by scientific discovery: The demand for new materials for applications ranging from energy technologies (batteries, solar cells, energy harvesting technologies) to sensors, artificial organs and computing technologies (e.g., quantum computers) far exceeds the capabilities of traditional materials design and synthesis, and takes years to decades of effort. This project brings together an interdisciplinary team of researchers with complementary expertise in AI and Material Science to launch a planning effort to lay the groundwork for an AI-Enabled Materials Discovery, Design, and Synthesis (AIMS) Institute. Technical Description: Realizing the AIMS vision requires synergistic advances across multiple areas of AI, including: (i) knowledge representation frameworks for encoding, communicating, and reasoning with models or abstractions of scientific domains, scientific artifacts, e.g. data, experiments, hypotheses, models; (ii) planning for optimizing scientific studies, experiments, etc.; (iii) machine learning and causal inference methods that can provide explanations of their results in the context of available knowledge, and recommend experiments to validate the predictions using the available experimental techniques); and (iv) algorithmic abstractions of AI-enabled human-machine, AI-enabled human-human, and machine-machine collaborations in science. Addressing this AI grand challenge would unify many of the sub-fields of AI, yield fundamental advances across multiple areas of AI, and AI mediated human-machine systems that support collaborative team science. The AI advances would go hand-in-hand with use-inspired research driven by some of the most pressing challenges in materials discovery, design, and synthesis, yielding scientific insights into the relationships between materials structure and their properties, as well as new ways of rapidly optimizing material properties for specific applications. Thus, AIMS will catalyze and establish interdisciplinary and transdisciplinary collaborations that transcend institutional and organizational boundaries. It will prepare the next generation AI workforce by training a diverse cadre of individuals, including women and underrepresented minorities, students as well as working professionals, in diverse training environments (academia, industry, national labs) and diverse career paths. AIMS will produce AI advances and technologies that yield not only transformative advances in materials design, discovery and synthesis, but also provide organizing frameworks, infrastructure, collaborative human-AI systems and tools, and best practices to dramatically accelerate scientific discovery, but also enable new modes of discovery across diverse scientific domains. Towards this end, the planning project will organize workshops and idea labs to further develop the vision, initiate interdisciplinary research at the interface between AI and Material Science, identify the AIMS infrastructure needs, develop education and outreach plans, establish synergistic partnerships, and develop the requisite organizational structure and processes for realizing the AIMS vision.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.

非技术描述：科学进步越来越多地通过使用计算来检查自然现象的能力来实现。“大数据”的出现和机器学习的进步大大加速了科学中的一些关键步骤，例如，数据采集和模型拟合。然而，科学过程的其他关键要素，例如，生成假设，设计，优先考虑和执行实验，整合数据，模型和模拟，推断和构建解释，协调科学论点以及跨学科交流，在很大程度上没有受到人工智能（AI）进步的影响。通过有效解决这些瓶颈来加速科学进步，可能会达到几个数量级，这对人工智能来说是一个巨大的挑战。材料发现、设计和合成为解决科学发现所带来的人工智能重大挑战提供了一个极好的试验平台：从能源技术（电池、太阳能电池、能量收集技术）到传感器、人工器官和计算技术（例如，量子计算机）远远超出了传统材料设计和合成的能力，需要数年到数十年的努力。该项目汇集了一个跨学科的研究人员团队，他们在人工智能和材料科学方面具有互补的专业知识，以启动规划工作，为人工智能材料发现，设计和合成（AIMS）研究所奠定基础。技术说明：实现AIMS愿景需要人工智能多个领域的协同进步，包括：（i）知识表示框架，用于编码，通信和推理，科学领域的模型或抽象，科学工件，例如数据，实验，假设，模型;（ii）规划优化科学研究，实验等; (iii)机器学习和因果推理方法，可以在可用知识的背景下提供对其结果的解释，并推荐使用可用实验技术验证预测的实验）;以及（iv）人工智能支持的人机，人工智能支持的人机和机器-机器科学合作的算法抽象。解决这一人工智能的重大挑战将统一人工智能的许多子领域，在人工智能的多个领域取得根本性的进步，以及人工智能介导的支持协作团队科学的人机系统。人工智能的进步将与材料发现、设计和合成中一些最紧迫的挑战所驱动的使用启发式研究齐头并进，从而对材料结构与性能之间的关系产生科学见解，以及为特定应用快速优化材料性能的新方法。因此，AIMS将促进和建立跨学科和跨学科的合作，超越机构和组织的界限。它将通过在不同的培训环境（学术界，工业界，国家实验室）和不同的职业道路中培训多样化的个人干部，包括妇女和代表性不足的少数民族，学生以及工作专业人员，为下一代人工智能劳动力做好准备。AIMS将产生人工智能的进步和技术，不仅在材料设计、发现和合成方面产生变革性的进步，而且还提供组织框架、基础设施、协作式人工智能系统和工具以及最佳实践，以大大加速科学发现，同时还能在不同的科学领域实现新的发现模式。为此，该规划项目将组织研讨会和创意实验室，以进一步发展愿景，在人工智能和材料科学之间的接口启动跨学科研究，确定AIMS基础设施需求，制定教育和推广计划，建立协同合作伙伴关系，该奖项反映了NSF的法定使命，并被认为是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，