Collaborative Research: C1: Learning the Universal Free Energy Function

合作研究：C1：学习通用自由能函数

• 批准号: 1940290
• 负责人: Alexander Urban
• 金额: $ 48.32万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940290&HistoricalAwards=false
• 关键词: Collaborative; Research; C1; Learning; Universal

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.

非技术摘要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能够预测不同化学元素如何结合来制造。具有重要技术意义的一个例子是在高温下结合多种金属元素形成的金属合金，在过去的一个世纪中，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，但现有的数据还不够。仍然只代表与此同时，机器学习和数据科学在发现新的模式和联系以及能够“填充”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，以了解化学元素之间的新联系并发现新合金。如果成功，研究团队将实现开发。新型和改进的轻质结构合金和所有开发的软件工具都将在整个资助期间公开实施，以加速此类开发，该研究团队的方法利用领域科学家和数据科学家之间的密切合作以及强大的“交叉培训”来开发。下一代科学家和工程师以及数据科学家能够采用聚合方法来解决科学和工程领域的挑战性问题。该奖项汇集了材料科学和工程以及数据科学，以开发数据密集型方法来确定材料相图。发现新材料主要依靠相图来量化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，许多平衡材料特性都源自自由能或自由能。人们投入了大量的资源来实验确定许多材料系统的相图，但尽管做出了这些努力，但高通量计算方法只探索了整个可能材料空间的一小部分，但它增加了我们的知识。从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），但利用它进行定量预测仍然很困难。机器学习扩大了可通过不确定性量化进行插值的非线性函数的范围，推进了降维领域，并揭示了材料热力学计算和实验开放数据集的不断扩展。构建用于热力学外推的机器学习模型变得可行的转折点，并提供超越单独高通量方法的重大进步。研究团队将开发一种新型热力学机器学习引擎，并演示它在相关条件下对材料进行建模重点关注：(1) 轻质金属合金来预测新成分的相图，(2) 扩展到原生氧化物热力学。PI 将采用半监督学习的组合，这是一种生成对抗网络框架。判别性和生成性学习以及包括不确定性量化在内的功能分位数学习如果成功，热力学机器学习引擎可以扩展到其他材料领域，包括高温合金以及电池和燃料电池材料。该团队将与 TRIPODS 中心进行互动，以进行传播、讨论和协作，因为它与领域科学和工程挑战所驱动的数据科学建立了更深入的联系。开发准确、可预测且计算高效的自由能函数。全面的材料空间是材料设计和发现的革命性创新，通用自由能函数固有的基本降维允许发现化学元素和固相之间的新关系，超越现有的不确定性量化关系。可以识别未探索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的范围，使能够机器学习与功能数据，并将其与数据深度、先进生成对抗网络和相关贝叶斯研究相结合，用于具有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功能值响应。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。该奖项是 NSF 的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Constructing and Compressing Global Moment Descriptors from Local Atomic Environments

从局部原子环境构建和压缩全局矩描述符

• 发表时间: 2023-04
• 期刊: Workshop on "Machine Learning for Materials" ICLR 2023
• 作者: Gharakhanyan, Vahe;Aalto, Max Shirokawa;Alsoulah, Aminah;Artrith, Nongnuch;Urban, Alexander
• 通讯作者: Urban, Alexander

Data-driven approach to parameterize SCAN+U for an accurate description of 3d transition metal oxide thermochemistry

用于参数化 SCAN U 的数据驱动方法，以准确描述 3d 过渡金属氧化物热化学

• DOI: 10.1103/physrevmaterials.6.035003
• 发表时间: 2022-03
• 期刊: Physical Review Materials
• 影响因子: 3.4
• 作者: Artrith, Nongnuch;Garrido Torres, José Antonio;Urban, Alexander;Hybertsen, Mark S.
• 通讯作者: Hybertsen, Mark S.

Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures

通过机器学习增强零开尔文量子力学以预测高温下的化学反应

• DOI: 10.1038/s41467-021-27154-2
• 发表时间: 2021-12-01
• 期刊: Nature Communications
• 影响因子: 16.6
• 作者: Garrido Torres JA;Gharakhanyan V;Artrith N;Eegholm TH;Urban A
• 通讯作者: Urban A