Collaborative Research: DMREF: Rheostructurally-informed Neural Networks for geopolymer material design

合作研究：DMREF：用于地质聚合物材料设计的流变结构信息神经网络

• 批准号: 2118944
• 负责人: Norman Wagner
• 金额: $ 51.37万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118944&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Rheostructurally; informed

Geopolymers are inorganic and non-crystalline structural materials that can be obtained from natural soils via a chemical activation. They have great potential as additives to reduce cement consumption in construction and thus can help reducing green-house gas emissions of cement manufacturing. They also promote the adoption of local soil resources for traditional and 3D printing-based construction. Important for human space exploration, geopolymers can be also formed from lunar and Martian soils with limited water, and thus are excellent candidates for space infrastructure such as landing pads and shelters. However, at present processing of geopolymers into desirable structures remains far behind their laboratory scale performance, due to the wide range of chemistries and characteristics of different indigenous geopolymers. This award combines experiments, microscopic simulations, and machine learning approaches that will enable scientists and engineers to effectively design and control geopolymers properties and performances. In collaboration with the Air Force Research Laboratory, the team will educate and train future materials researchers with multi-tool skills that span experiments, simulations, and data-driven algorithms.Geopolymers are amorphous and porous solid matrices that develop as gels when an alumino-silicate source (typically from clays) reacts with an alkali hydroxide or alkali silicate solution, yielding ceramic-like structures and mechanics. The range of multiscale pore morphologies and material strengths of geopolymer gels makes them ideally versatile and potentially smart binders. However, the primary challenge hindering wide adoption of these sustainable materials is the complexity of controlling property development and processing, given the significant chemical variability that makes their design cycle difficult and empirical. Artificial intelligence approaches are required to bridge the gap between the deep fundamental understanding of a few materials and the need for sustainable processing of a wide range of material resources on earth and other planets with limited experimentation efforts. The team will construct a data-driven platform informed by integrated multiscale modeling and experiments, in order to accelerate design of processing routes for geopolymers into desirable structures. The PIs will work together to develop rheology-informed neural networks that use the multi-scale and multi-component dynamics of geopolymeric systems under load and in flowing conditions. To do so, they have planned a comprehensive interrogation of experiments and simulations that hierarchically span from the atomistic to macroscale.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.

地球聚合物是无机和非晶体结构材料，可以通过化学激活从天然土壤中获得。它们具有减少建筑水泥消耗的添加剂的巨大潜力，因此可以帮助减少水泥制造的绿色房屋气体排放。他们还促进了基于传统和3D印刷建筑的当地土壤资源的采用。对于人类太空探索很重要，也可以由有限的水的月球和火星土壤形成地球聚合物，因此是空间基础设施（例如着陆垫和庇护所）的出色候选者。但是，由于不同土著地球聚合物的化学和特征广泛，目前将地质聚合物加工为理想的结构远远落后于实验室规模的性能。该奖项结合了实验，微观模拟和机器学习方法，这些方法将使科学家和工程师能够有效地设计和控制地球聚合物的属性和性能。 In collaboration with the Air Force Research Laboratory, the team will educate and train future materials researchers with multi-tool skills that span experiments, simulations, and data-driven algorithms.Geopolymers are amorphous and porous solid matrices that develop as gels when an alumino-silicate source (typically from clays) reacts with an alkali hydroxide or alkali silicate solution, yielding ceramic-like structures and力学。地球聚合物凝胶的多尺度孔形态和物质优势的范围使它们成为理想的通用性和潜在的智能粘合剂。但是，鉴于显着的化学变异性使其设计周期变得困难和经验，因此阻碍这些可持续材料的广泛采用的主要挑战是控制财产开发和加工的复杂性。需要人工智能方法来弥合对少数材料的深层理解与在有限的实验工作中对地球和其他行星进行可持续处理的广泛物质资源的需求之间的差距。该团队将通过集成的多尺度建模和实验来构建一个数据驱动的平台，以加速地理聚合物的处理路线的设计。 PI将共同开发流变知识的神经网络，这些神经网络在负载和流动条件下使用地球聚合系统的多尺度和多组分动力学。为此，他们计划对实验和模拟进行全面的询问，这些实验和模拟从原子体到宏观的层次。该奖项反映了NSF的法定任务，并认为通过基金会的智力优点和更广泛的影响来通过评估来获得支持。