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

地聚合物是一种无机和非晶体结构材料，可以通过化学活化从天然土壤中获得。它们作为添加剂在减少水泥在建筑中的消耗方面具有很大的潜力，从而可以帮助减少水泥制造过程中的温室气体排放。他们还推动采用当地土壤资源进行传统和基于3D打印的建筑。地聚合物对人类的太空探索很重要，它也可以从月球和火星土壤中形成，水分有限，因此是太空基础设施（如着陆垫和避难所）的绝佳候选者。然而，目前将地聚合物加工成理想结构仍远远落后于其实验室规模性能，因为不同的天然地聚合物的化学性质和特性范围广泛。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聚合物的特性和性能。在与空军研究实验室的合作下，该团队将教育和培训未来材料研究人员，使其具备跨实验、模拟和数据驱动算法的多工具技能。地聚合物是一种无定形的多孔固体基质，当铝硅酸盐源（通常来自粘土）与氢氧化碱或碱硅酸盐溶液反应时，形成凝胶，产生类似陶瓷的结构和力学。地聚合物凝胶的多尺度孔隙形态和材料强度使其成为理想的多用途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房地产开发和加工的复杂性，考虑到显著的化学变化，使其设计周期变得困难和经验。需要人工智能方法来弥合对少数材料的深刻基本理解与对地球和其他行星上广泛材料资源的可持续处理的需求之间的差距，并且需要有限的实验努力。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，以加速地聚合物加工路线的设计，使其成为理想的结构。pi将共同开发流变信息神经网络，利用负载和流动条件下地聚合物系统的多尺度和多组分动力学。为此，他们计划对从原子尺度到宏观尺度的实验和模拟进行全面的调查。该奖项反映了美国国家科学基金会的法定使命，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.