Collaborative Research: C1: Learning the Universal Free Energy Function
合作研究:C1:学习通用自由能函数
基本信息
- 批准号:1939956
- 负责人:
- 金额:$ 39.95万
- 依托单位:
- 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 2024-02-29
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
非技术总结该奖项将材料科学和材料工程与数据科学结合在一起,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的化学元素如何结合在一起,根据温度产生不同的化合物。一个与技术密切相关的例子是由多种金属元素在高温下结合而成的金属合金。在过去的一个世纪里,材料科学家测量了许多材料系统的这种化合物形成过程,但现有数据仍然只代表所有可能的化学元素和温度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方面取得了长足的进步,并能够从大数据集中“填补”缺失的信息。研究团队将扩展和开发最先进的机器学习方法,将其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,并发现新的合金。如果成功,研究团队将能够开发新的和改进的轻质结构合金和寿命更长、功率密度更高的电池。所有已开发的软件工具都将在整个供资期间公开提供实施方案,以加速此类开发。研究团队的方法利用领域和数据科学家之间的密切合作,通过强大的“交叉培训”来培养下一代科学家和工程师,以及数据科学家,使他们能够以集中的方法解决科学和工程方面的挑战性问题。技术总结该奖项汇集了材料科学和工程学以及数据科学,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广泛依赖于相图,这些相图量化了什么相(S)在给定的温度和化学组成下是稳定的,这是由不同相的自由能决定的。此外,许多平衡物质的性质是由自由能或自由能差得出的。许多材料体系的相图的实验测定已经投入了大量的资源,但尽管有这些努力,可能的材料只探索了整个空间的一小部分。高通量计算方法增加了我们的知识,但从易于计算的零温度结果外推到实验上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--但利用这一点进行定量预测是困难的。与此同时,机器学习的重大发展扩大了可以用不确定性量化进行内插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。材料热力学计算和实验开放数据集的持续扩展提供了一个转折点,在这里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。研究小组将开发一种新的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,重点放在:(1)预测新成分下的相图的轻质金属合金,以及(2)扩展到自然氧化物热力学。PIS将结合使用半监督学习、用于鉴别性和生成性学习的生成性对抗网络框架,以及包括不确定性量化的功能分位数学习。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、电池和燃料电池材料。它可以推动未来的高通量计算和实验。在领域科学和工程挑战的推动下,该团队将与三脚架中心互动,以传播、讨论和协作,并与数据科学建立更深层次的联系。为所有材料空间开发准确、可预测和计算高效的自由能函数是材料设计和发现的革命性创新。普适自由能函数所固有的降维原理使人们能够发现化学元素和固相之间的新关系,而不仅仅是现有的定性关系。不确定性量化可以识别化学和结构空间中尚未探索但有价值的区域,为高通量计算和实验方法提供一种新的范式,以最佳地扩展我们对材料和化学关系的知识。数据科学的创新将扩展基于高斯过程的建模的范围,使机器学习与函数数据相结合,并将其与数据深度方面的最新进展相结合,推进生成性对抗网络和相关的贝叶斯研究,以进行不确定性量化的函数数据生成模型,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、土木工程、机械和制造创新部以及高级网络基础设施办公室为该奖项提供资金。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Bayesian framework for studying climate anomalies and social conflicts
- DOI:10.1002/env.2778
- 发表时间:2022-11-21
- 期刊:
- 影响因子:1.7
- 作者:Mukherjee,Ujjal Kumar;Bagozzi,Benjamin E.;Chatterjee,Snigdhansu
- 通讯作者:Chatterjee,Snigdhansu
A dependent multimodel approach to climate prediction with Gaussian processes
利用高斯过程进行气候预测的相关多模型方法
- DOI:10.1017/eds.2022.24
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Thompson, Marten;Braverman, Amy;Chatterjee, Snigdhansu
- 通讯作者:Chatterjee, Snigdhansu
On weighted multivariate sign functions
关于加权多元符号函数
- DOI:10.1016/j.jmva.2022.105013
- 发表时间:2022
- 期刊:
- 影响因子:1.6
- 作者:Majumdar, Subhabrata;Chatterjee, Snigdhansu
- 通讯作者:Chatterjee, Snigdhansu
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Snigdhansu Chatterjee其他文献
Fast and General Model Selection using Data Depth and Resampling
使用数据深度和重采样进行快速通用模型选择
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
S. Majumdar;Snigdhansu Chatterjee - 通讯作者:
Snigdhansu Chatterjee
Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks
使用相关网络挖掘时间序列数据中的新型多元关系
- DOI:
10.1109/tkde.2019.2911681 - 发表时间:
2018 - 期刊:
- 影响因子:8.9
- 作者:
Saurabh Agrawal;M. Steinbach;Daniel Boley;Snigdhansu Chatterjee;G. Atluri;A. T. Dang;S. Liess;Vipin Kumar - 通讯作者:
Vipin Kumar
A Bootstrap Test Using Maximum Likelihood Ratio Statistics to Check the Similarity of Two 3-Dimensionally Oriented Data Samples
- DOI:
10.1023/a:1021776814497 - 发表时间:
1998-04-01 - 期刊:
- 影响因子:3.600
- 作者:
Sojen Joy;Snigdhansu Chatterjee - 通讯作者:
Snigdhansu Chatterjee
Approximate Bayesian Computation for Physical Inverse Modeling
物理逆建模的近似贝叶斯计算
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Neel Chatterjee;Somya Sharma;S. Swisher;Snigdhansu Chatterjee - 通讯作者:
Snigdhansu Chatterjee
Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty
计算数据科学对极端气候和不确定性的可行见解
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
A. Ganguly;E. Kodra;Snigdhansu Chatterjee;A. Banerjee;H. Najm - 通讯作者:
H. Najm
Snigdhansu Chatterjee的其他文献
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{{ truncateString('Snigdhansu Chatterjee', 18)}}的其他基金
Collaborative Research: Machine Learning methods for multi-disciplinary multi-scales problems
协作研究:多学科多尺度问题的机器学习方法
- 批准号:
1939916 - 财政年份:2020
- 资助金额:
$ 39.95万 - 项目类别:
Continuing Grant
ATD: Collaborative Research: Multivariate Quantiles for Rapid Spatio-Temporal Threat Detection
ATD:协作研究:用于快速时空威胁检测的多元分位数
- 批准号:
1737918 - 财政年份:2017
- 资助金额:
$ 39.95万 - 项目类别:
Standard Grant
On Conditional Statistical Procedures for Simultaneous Model Selection, Inference, and Prediction in Complex Climate Systems
复杂气候系统中同时模型选择、推理和预测的条件统计程序
- 批准号:
1622483 - 财政年份:2016
- 资助金额:
$ 39.95万 - 项目类别:
Continuing Grant
Collaborative Research: Computation-driven small area inference with applications
协作研究:计算驱动的小区域推理与应用
- 批准号:
0851705 - 财政年份:2009
- 资助金额:
$ 39.95万 - 项目类别:
Standard Grant
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Cell Research
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- 批准号:10774081
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- 项目类别:面上项目
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