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

非技术描述：科学进步越来越多地被通过使用计算来检查自然现象的能力所推动。“大数据”的出现和机器学习的进步极大地加速了科学中的一些关键步骤，例如数据获取和模型拟合。然而，科学过程的其他关键要素，如生成假设、设计、区分优先顺序和执行实验、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，在很大程度上仍未受到人工智能(AI)进展的影响。通过有效解决这些瓶颈来加速科学进步，可能会提高几个数量级，这对人工智能来说是一个巨大的挑战。材料发现、设计和合成为应对科学发现带来的人工智能重大挑战提供了一个极好的试验台：从能源技术(电池、太阳能电池、能量收集技术)到传感器、人造器官和计算技术(例如量子计算机)等应用对新材料的需求远远超过了传统材料设计和合成的能力，并且需要数年到数十年的努力。该项目汇集了一个在人工智能和材料科学方面具有互补专业知识的跨学科研究团队，以启动一项规划工作，为建立一个支持人工智能的材料发现、设计和合成(AIMS)研究所奠定基础。技术描述：实现AIMS愿景需要在人工智能的多个领域取得协同进展，包括：(1)用于编码、交流和推理的知识表示框架，用于利用科学领域的模型或抽象，科学人工制品，例如数据、实验、假设、模型；(2)优化科学研究、实验等的规划；(3)机器学习和因果推理方法，能够在现有知识的背景下提供对其结果的解释，并建议使用现有的实验技术来验证预测)；以及(Iv)科学中人工智能使能的人-机器、人工智能使能的人-人和机器-机器协作的算法抽象。应对这一巨大的人工智能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产生根本性的进展，以及支持协作团队科学的人工智能中介的人机系统。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推动的使用启发研究齐头并进，产生对材料结构及其性能之间关系的科学见解，以及为特定应用快速优化材料性能的新方法。因此，AIMS将促进和建立超越机构和组织边界的跨学科和跨学科合作。它将通过在不同的培训环境(学术界、行业、国家实验室)和不同的职业道路中培训不同的个人干部，包括妇女和代表性不足的少数族裔、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、协作的人类-人工智能系统和工具以及最佳实践，以显著加快科学发现，而且还使新的发现模式能够跨越不同的科学领域。为此，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启动跨学科研究，确定目标基础设施需求，制定教育和外展计划，建立协同合作伙伴关系，并制定实现目标愿景所需的组织结构和程序。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。

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.