NSF Convergence Accelerator Track D: A Community Resource for Innovation in Polymer Technology (CRIPT)

聚合物材料，从服装和个人防护设备到建筑材料和食品包装，是满足我们对食物、住所、健康和交通的基本需求的基本材料。然而，为下一代产品开发新的聚合物需要数十年的时间，我们必须加快步伐才能保持竞争力。为了加快这一进程，该项目正在开发CRIPT，这是一个聚合物数据生态系统，由基于Web的应用程序和云数据库组成，使聚合物科学家能够轻松地查找、归档复杂的聚合物数据并与其交互。人工智能驱动的化学工具和CRIPT内的数据驱动的工作流程将使聚合物材料的开发时间减少一个数量级，对每年销售的近6000亿美元聚合物的生产商和买家都产生革命性的影响。目前，在现有聚合物中搜索是一项艰巨的任务，因为聚合物数据是以小而不同的集合存在的，这使得导航成为一个复杂的过程，结合了不同数据格式的协调和元数据的协调，这两者目前都需要专家干预。CRIPT提供基于新的聚合物专用数据模型的云数据库，该模型同时提供跨聚合物科学和工程的不同领域的互操作性，同时保留允许领域专家跨多个独立记录关联信息的关键元数据。还提供了一系列受化学启发的人工智能创新，包括基于化学的查询语言、保留数据时间结构的基于图形的模式、自动数据验证的算法、用于数据摄取的人工智能协作工具以及将机器集成到数据生态系统中，以增加公平原则、对数据的信任和系统的易用性。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。

Polymer materials, ranging from clothing and personal protective equipment to construction materials and food packaging, are fundamental to providing for our basic needs for food, shelter, health, and transportation. However, developing new polymers for next-generation products takes decades, and we must move faster to remain competitive. To accelerate this process, this project is developing CRIPT, a polymer data ecosystem consisting of a web-based application and cloud database that allow polymer scientists to easily find, archive, and interact with complex polymer data. AI-driven chemistry tools and data-driven workflows within CRIPT will reduce the development time for polymer materials by an order of magnitude, creating a transformative impact on both the producers and buyers of the nearly $600 billion of polymers sold each year.Currently, searching among existing polymers is a daunting task because polymer data exists as small, disparate sets, making the navigation a complex process combining the harmonization of different data formats and the reconciliation of metadata, both of which currently require expert intervention. CRIPT offers a cloud database based on a new polymer-specific data model that simultaneously provides interoperability across different domains of polymer science and engineering, while retaining critical metadata that allows domain experts to correlate information across many independent records. A series of chemically-inspired AI innovations, including a chemistry-based query language, a graph-based schema preserving temporal structure in data, algorithms for automatic data validation, AI-human cooperative tools for data ingestion, and the integration of machines into the data ecosystem are also provided to add FAIR principles, trust in data, and ease of use to the system.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.