DMREF: High Throughput Design of Metallic Glasses with Physically Motivated Descriptors

DMREF：具有物理激励描述符的金属玻璃的高通量设计

• 批准号: 1728933
• 负责人: Dane Morgan
• 金额: $ 120万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1728933&HistoricalAwards=false
• 关键词: DMREF; Throughput; Design; Metallic; Glasses

Non-technical Description: Silica-based glasses are familiar to most of us from our experiences with everything from windows to wineglasses. However, when cooled quickly enough, some metal alloys can also form a glassy state. Metallic glasses have attractive properties such as high elastic modulus, excellent strength, good biocompatibility, and the ability to be processed like plastics. Applications include packaging, arterial stents, water purification, and Micro-Electro-Mechanical Systems gears and springs. The slowest cooling rate that still forms a glass is called the critical cooling rate. Although many metals can form a glass, only a rare set of alloys have slow enough critical cooling rate that they can form a significant bulk volume of glassy material, and these alloys are said to have good glass forming ability. Despite the importance of these materials and years of research, there are still no rigorous, consistent, and quantitative rules to predict the actual glass forming ability of a metallic alloy system. To solve this problem, this project will develop an extensible materials informatics framework for predicting the glass-forming ability of metal alloys, and then apply that framework to develop aluminum- and magnesium-based alloys with improved glass forming ability.Technical description: In order to discover new aluminum- and magnesium-based bulk metallic glasses with superior glass-forming ability, the team will execute a dual-loop iterative materials design approach. A rapid materials design loop will provide high-throughput materials discovery by integrating experimental and simulated data with machine learning methods. An unprecedented body of experimental data on glass forming ability and basic mechanical properties will be generated by combinatorial 3D printing synthesis, followed by rapid optical, microscopy, thermal, and nanomechanical characterization. A similarly unique database of liquid and glass thermodynamic, kinetic, and structural properties will be determined by automated, high-throughput ab initio molecular dynamics. Machine-learning methods, trained on the data and physically motivated descriptors from existing experiments and the ab initio molecular dynamics simulation, will search a space of up to hundreds of thousands of potential alloys for the most promising candidates, which will then be synthesized, characterized and used to refine the models. Slower descriptor design loop studies will study select alloys in detail with fluctuation electron microscopy and extensive simulations to develop improved descriptors, which will then be incorporated into the rapid materials design loop and further validated by their predictive ability. This work will produce the first set of large-scale databases with both true measures of glass forming ability and extensive thermophysical data from simulations, and integrate them to generate physical descriptor driven machine-learning models for iterative new metallic glass search and discovery. The PIs also plan to release the Materials Simulation Toolkit - Machine Learning (MASTML) as open source and build a user community around the language by ensuring that interested researchers are able to contribute to the MASTML codebase. This will allow a wider growth of the project. This aspect is of special interest to the software cluster in the Office of Advanced Cyberinfrastructure, which has provided co-funding for this award.

非技术性描述：我们大多数人都熟悉硅基玻璃，从窗户到酒杯。然而，当冷却足够快时，一些金属合金也可以形成玻璃态。金属玻璃具有高弹性模量、优异的强度、良好的生物相容性和像塑料一样加工的能力等吸引人的特性。应用包括包装、动脉支架、水净化和微机电系统齿轮和弹簧。仍然形成玻璃的最慢冷却速率称为临界冷却速率。虽然许多金属可以形成玻璃，但只有少数合金具有足够慢的临界冷却速率，使得它们可以形成大量的玻璃状材料，并且这些合金据说具有良好的玻璃形成能力。尽管这些材料的重要性和多年的研究，仍然没有严格的，一致的，定量的规则来预测金属合金系统的实际玻璃形成能力。为了解决这一问题，本项目将开发一个可扩展的材料信息学框架，用于预测金属合金的玻璃形成能力，然后将该框架应用于开发具有改善的玻璃形成能力的铝基和镁基合金。为了发现具有上级玻璃形成能力的新型铝基和镁基块体金属玻璃，该团队将执行双循环迭代材料设计方法。快速材料设计循环将通过将实验和模拟数据与机器学习方法相结合来提供高通量材料发现。将通过组合3D打印合成，然后进行快速光学、显微镜、热和纳米机械表征，生成有关玻璃形成能力和基本机械性能的前所未有的实验数据。一个类似的独特的数据库的液体和玻璃的热力学，动力学和结构特性将被确定的自动化，高通量从头算分子动力学。根据现有实验和从头算分子动力学模拟的数据和物理动机描述符进行训练的机器学习方法将在多达数十万种潜在合金的空间中搜索最有希望的候选者，然后将其合成，表征并用于优化模型。慢速描述符设计循环研究将使用波动电子显微镜和广泛的模拟详细研究选定的合金，以开发改进的描述符，然后将其纳入快速材料设计循环，并通过其预测能力进一步验证。这项工作将产生第一组大规模数据库，其中既有玻璃形成能力的真实测量值，又有来自模拟的大量热物理数据，并将它们整合起来，生成物理描述符驱动的机器学习模型，用于迭代新的金属玻璃搜索和发现。PI还计划将材料模拟工具包-机器学习（MASTML）作为开源发布，并通过确保感兴趣的研究人员能够为MASTML代码库做出贡献，围绕该语言建立一个用户社区。这将使该项目得到更广泛的发展。高级网络基础设施办公室的软件集群对此特别感兴趣，该办公室为该奖项提供了共同资助。

项目成果

Molecular simulation-derived features for machine learning predictions of metal glass forming ability

• DOI: 10.1016/j.commatsci.2021.110728
• 发表时间: 2021-08
• 作者: Ben Afflerbach;L. Schultz;J. Perepezko;P. Voyles;I. Szlufarska;D. Morgan

Experimental validation and microstructure characterization of topology optimized, additively manufactured SS316L components

• DOI: 10.1016/j.msea.2020.139050
• 发表时间: 2020-03-03
• 期刊: MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
• 影响因子: 6.4
• 作者: Rankouhi, B.;Bertsch, K. M.;Suresh, K.
• 通讯作者: Suresh, K.

A dimensionless number for high-throughput design of multi-principal element alloys in directed energy deposition

定向能量沉积多主元合金高通量设计的无量纲数

• DOI: 10.1063/5.0069384
• 发表时间: 2021
• 期刊: Applied Physics Letters
• 影响因子: 4
• 作者: Islam, Zahabul;Nelaturu, Phalgun;Thoma, Dan J.
• 通讯作者: Thoma, Dan J.

Molecular dynamic characteristic temperatures for predicting metallic glass forming ability

• DOI: 10.1016/j.commatsci.2021.110877
• 发表时间: 2021-09
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: L. Schultz;Ben Afflerbach;I. Szlufarska;D. Morgan

StructOpt: A modular materials structure optimization suite incorporating experimental data and simulated energies

StructOpt：结合实验数据和模拟能量的模块化材料结构优化套件

• DOI: 10.1016/j.commatsci.2018.12.052
• 发表时间: 2019
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Maldonis, Jason J.;Xu, Zhongnan;Song, Zhewen;Yu, Min;Mayeshiba, Tam;Morgan, Dane;Voyles, Paul M.
• 通讯作者: Voyles, Paul M.