Collaborative Research: C1: Learning the Universal Free Energy Function

该奖项将材料科学和材料工程与数据科学结合起来，开发数据密集型方法来创建材料的相图或“路线图”。新材料的发现和设计需要有能力预测不同的化学元素如何根据温度结合成不同的化合物。具有重大技术相关性的一个例子是金属合金，它是由多种金属元素在高温下结合而形成的。在过去的一个世纪里，材料科学家已经测量了许多材料系统的这种化合物形成过程，但可用的数据仍然只代表了化学元素和温度所有可能组合的整个空间的一小部分。与此同时，机器学习和数据科学在发现新的模式和联系以及能够“填补”大型数据集中缺失的信息方面取得了长足的进步。研究团队将扩展和开发最先进的机器学习方法，应用于金属合金的数学模型和数据，以学习化学元素之间的新联系，并发现新的合金。如果成功，研究小组将开发出新的和改进的轻质结构合金和更长的寿命，更高功率密度的电池。所有开发的软件工具都将在整个资助期内公开实现，以加速此类开发。研究团队的方法是通过领域和数据科学家之间的密切合作，通过强大的“交叉训练”来培养下一代科学家和工程师，而数据科学家则采用融合的方法来解决科学和工程的挑战性问题。技术概述：该奖项汇集了材料科学和工程以及数据科学，以开发确定材料相图的数据密集型方法。新材料的设计和发现很大程度上依赖于相图，相图量化了在给定温度和化学成分下哪些相是稳定的，这是由不同相的自由能决定的。此外，许多平衡材料的性质是由自由能或自由能差导出的。大量的资源已经投入到许多材料系统相图的实验确定，但尽管这些努力，只有一小部分可能的材料的整个空间已经被探索。高通量计算方法增加了我们的知识，但是从易于计算的零温度结果推断到实验相关的有限温度结果是耗时的。虽然已经确定了一些定性的化学和结构趋势——元素周期表是最著名的例子——但利用这些趋势进行定量预测是困难的。同时，机器学习的重大发展扩大了可以用不确定性量化插值的非线性函数的范围，推进了降维领域，并揭示了数据中新的潜在模式。材料热力学的计算和实验开放数据集的不断扩展呈现出一个临界点，在这个临界点上，构建用于热力学外推的机器学习模型变得可行，并提供了超越高通量方法的重大进步。研究团队将开发一种新的热力学机器学习引擎，并将其用于相关条件下的材料建模，重点是：(1)轻质金属合金以预测新成分的相图，以及(2)扩展到天然氧化物热力学。pi将采用半监督学习、用于判别和生成学习的生成对抗网络框架以及包括不确定性量化在内的功能分位数学习的组合。如果成功，热力学机器学习引擎可以扩展到其他材料领域，包括高温合金，电池和燃料电池材料。它可以驱动未来的高通量计算和实验。该团队将与TRIPODS中心进行互动，进行传播、讨论和合作，因为它与领域科学和工程挑战驱动的数据科学建立了更深层次的联系。为全范围的材料空间开发一个准确的、可预测的、计算效率高的自由能函数是材料设计和发现的变革性创新。普遍自由能函数中固有的潜在维度降低允许发现化学元素和固相之间的新关系，超越现有的定性关系。不确定度量化可以识别未开发但有价值的化学和结构空间区域，为高通量计算和实验方法提供新的范例，以最佳地扩展我们对材料和化学关系的知识。数据科学创新将扩展基于高斯过程的建模范围，使机器学习与功能数据相结合，并将其与数据深度的最新进展相结合，推进生成对抗网络和相关的贝叶斯研究，用于不确定性量化的功能数据生成模型，并将分位数回归扩展到函数值响应。材料研究部、数学科学部、土木、机械和制造创新部以及先进网络基础设施办公室为该奖项提供资金。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。

NONTECHNICAL SUMMARYThis award brings materials science and materials engineering together with data science to develop data-intensive methods to create phase diagrams or "roadmaps" of materials. The discovery and design of new materials requires the ability to predict how different chemical elements can combine to make different compounds depending on the temperature. One example of great technological relevance are metallic alloys that form by combining multiple metallic elements at elevated temperatures. Over the past century, materials scientists have measured such compound-formation processes for many materials systems, but the available data still represents only a tiny fraction of the entire space of all possible combinations of chemical elements and temperatures. Meanwhile, machine-learning and data science have made great strides in discovering new patterns and connections, and being able to “fill in” missing information from large data sets. The research team will extend and develop state-of-the-art machine learning approaches to apply to mathematical models and data for metallic alloys to learn new connections between chemical elements and discover new alloys. If successful, the research team will enable the development of new and improved lightweight structural alloys and longer-lived, higher power density batteries. All of the developed software tools will have publicly available implementations throughout the funding period to accelerate such developments. The research team’s approach uses close collaboration between domain and data scientists with strong “cross-training” to develop the next generation of scientists and engineers, and data scientists enabling convergent approaches to the challenging problems of science and engineering. TECHNICAL SUMMARYThis award brings together materials science and engineering, and data science to develop data-intensive methods to determine materials phase diagrams. Design and discovery of new materials relies extensively on phase diagrams that quantify what phase(s) are stable at a given temperature and chemical composition, which is determined by the free energy of different phases. Moreover, many equilibrium material properties are derived from free energies or free-energy differences. Extensive resources have been devoted to experimental determination of phase diagrams for many material systems, but despite these efforts only a tiny fraction of the entire space of possible materials has been explored. High-throughput computational approaches have added to our knowledge, but it is time-consuming to extrapolate from the easy-to-compute zero temperature results to experimentally relevant finite temperature results. While some qualitative chemical and structural trends have been identified—the periodic table being the most well-known example—leveraging this for quantitative predictions is difficult. Simultaneously, significant developments in machine learning have expanded the range of non-linear functions that can be interpolated with uncertainty quantification, advanced the field of dimensionality reduction, and revealed new underlying patterns in data. Continual expansion of computational and experimental open data sets of materials thermodynamics presents a tipping point where constructing machine-learned models for thermodynamic extrapolation becomes feasible, and offers a significant advance beyond high-throughput methods alone.The research team will develop a novel thermodynamic machine learning engine and demonstrate it for the modeling of materials at relevant conditions with a focus on: (1) lightweight metallic alloys to predict of phase diagrams at new compositions, and (2) extending to native oxide thermodynamics. The PIs will employ a combination of semi-supervised learning, a generative adversarial network framework for discriminative and generative learning, and functional quantile learning including uncertainty quantification. If successful, the thermodynamic machine learning engine can be expanded to other material spaces including high-temperature alloys, and battery and fuel cell materials. It can drive future high-throughput computation and experiment. The team will interact with TRIPODS centers for dissemination, discussions, and collaborations as it develops deeper connections with data science driven by the challenges of domain science and engineering.Developing an accurate, predictive, and computationally efficient free energy function for the full range of materials space is a transformative innovation for the design and discovery of materials. The underlying dimensionality reduction inherent in the universal free energy function permits the discovery of new relationships between chemical elements and solid phases, beyond existing qualitative relationships. Uncertainty quantification can identify unexplored but valuable regions of chemical and structure space to provide a new paradigm for high-throughput computation and experimental methods to optimally expand our knowledge of materials and chemical relationships. The data science innovations will extend the scope of Gaussian process-based modeling, enable machine learning with functional data and couple it with recent advances in data-depth, advance generative adversarial networks and related Bayesian studies for functional data generative models with uncertainty quantification, and extend quantile regression to function-valued responses.The Division of Materials Research, the Division of Mathematical Sciences, the Civil, Mechanical, and Manufacturing Innovation Division, and the Office of Advanced Cyberinfrastructure contribute funds to this award.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.