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

地质聚合物是无机和非结晶结构材料，可以通过化学活化从天然土壤中获得。它们作为添加剂具有很大的潜力，可以减少建筑中的水泥消耗，从而有助于减少水泥制造过程中的温室气体排放。它们还促进了当地土壤资源用于传统和基于3D打印的建筑。对于人类太空探索来说，地质聚合物也可以从水资源有限的月球和火星土壤中形成，因此是空间基础设施的绝佳候选者，如着陆垫和避难所。然而，由于不同的本地地质聚合物的广泛的化学性质和特性，目前将地质聚合物加工成期望的结构仍然远远落后于它们的实验室规模性能。该奖项结合了实验，微观模拟和机器学习方法，使科学家和工程师能够有效地设计和控制地质聚合物的性能和性能。该团队将与空军研究实验室合作，教育和培训未来的材料研究人员，使其具备跨实验、模拟和数据驱动算法的多工具技能。地质聚合物是无定形和多孔的固体基质，当铝硅酸盐源（通常来自粘土）与碱金属氢氧化物或碱金属硅酸盐溶液反应时，会形成凝胶，产生类似陶瓷的结构和力学性能。地质聚合物凝胶的多尺度孔隙形态和材料强度范围使其成为理想的通用和潜在的智能粘合剂。然而，阻碍这些可持续材料广泛采用的主要挑战是控制属性开发和加工的复杂性，因为显著的化学变化性使其设计周期变得困难和经验性。需要人工智能方法来弥合对少数材料的深刻基本理解与需要在有限的实验努力下可持续地处理地球和其他星球上的广泛材料资源之间的差距。该团队将构建一个数据驱动的平台，通过集成的多尺度建模和实验来提供信息，以加速地质聚合物加工路线的设计，使其成为理想的结构。PI将共同开发流变学信息神经网络，该网络使用负载和流动条件下地质聚合物系统的多尺度和多组分动力学。为此，他们计划对从原子到宏观尺度的实验和模拟进行全面的调查。该奖项反映了NSF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。

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.