CAREER: Decoding the Structure and Energy Landscape of Isostatic Glasses by Machine Learning and Enhanced Sampling

职业：通过机器学习和增强采样解码等静压玻璃的结构和能量景观

• 批准号: 1944510
• 负责人: Mathieu Bauchy
• 金额: $ 50万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944510&HistoricalAwards=false
• 关键词: CAREER; Decoding; Structure; Energy; Landscape

NON-TECHNICAL SUMMARYThis CAREER award supports research and education activities in the field of glass science. When cooled below their melting point, liquids usually turn into crystals. As an alternative route, liquids can bypass crystallization and enter a supercooled liquid state if quenched fast enough. At the glass transition temperature, liquids become so viscous that their flowing time eventually exceeds the observation time. At this point, since they are unable to flow, they freeze into solid glasses. Although virtually the entire periodic table can form a glass if quenched fast enough, the propensity for a liquid to crystallize or form a glass upon quenching, its glass-forming ability, depends on its composition. Deciphering how the atomic composition and structure of liquids govern their glass-forming ability has been at the very foundation of glass science and has remained largely unresolved thus far. To this end, the atomic structure of glasses can be described based on the concept of network topology, that is, the branch of mathematics that studies how the connectivity of nodes, in this case - the atoms, govern the properties of networks, the glass atomic structure. Interestingly, glasses exhibiting an optimal network topology, called isostatic, tend to feature unique properties, including maximum glass-forming ability. However, the nature of the linkages between network topology and glass-forming ability remains largely unknown.To address these questions, the PI seeks to decode the relationship between network topology and glass-forming ability, and so, interrogates the very nature of a glass and works toward understanding the origin of the anomalous properties featured by isostatic glasses. This will be accomplished by a combination of molecular dynamics simulations, enhanced sampling, and machine learning activities, which are closely integrated to inform and advance each other. This project will focus on chalcogenide glasses, which are the base material for 3D Xpoint phase-change random access memory. By storing 3 bits per cell instead of a single bit, this technology is widely believed to constitute the future of non-volatile memory.These research tasks are complemented and enabled by several educational activities. The PI will develop a series of cross-disciplinary courses to equip students with transversal knowledge, which is a key skillset to develop integrated solutions that globally address complex real-world challenges. The PI will also leverage this project to promote data-based adaptive learning, which offers a unique opportunity to move away from the "one size fits all" way of learning that does not reflect complex cognitive processes. To address the gender imbalance in engineering, the PI will develop an undergraduate research program targeted to female students, which aims to enhance their retention in graduate school.TECHNICAL SUMMARY This CAREER award supports research and education activities to advance fundamental understanding of structural glasses. Based on their network topology, structural glasses can be classified as flexible (underconstrained), stressed-rigid (overconstrained), and isostatic (rigid, but free of stress) when the number of topological interatomic constraints is lower, higher, or equal to the number of atomic degrees of freedom, respectively. Interestingly, optimally-constrained isostatic glasses tend to exhibit anomalous properties, including optimal glass-forming ability and low propensity for relaxation. Such isostatic glasses have been suggested to exist within an intermediate phase, wherein the glass self-organizes to exhibit a nearly-reversible glass transition. However, the existence and origin of the intermediate phase remain debated and no structural signature of this phase has been revealed thus far. Further, the origin of the anomalous properties of isostatic glasses remains elusive. To address these gaps in knowledge, the PI seeks to decode the relationship between network topology, propensity for relaxation, and glass-forming ability.To this end, the PI will adopt an integrated approach, wherein classical molecular dynamics, density functional theory, enhanced sampling, and machine learning activities mutually inform and advance each other. First, a machine-learned forcefield will be trained to simulate archetypical Ge-As-Se chalcogenide glasses. Second, this forcefield will be used to decode the relationship between topology and propensity for relaxation, and interrogate the existence of the intermediate phase. Third, a structural signature of self-organization will be sought using machine learning. Last, enhanced sampling techniques will be used to characterize the energy landscape of glasses with varying network topologies. By synergistically combining physics- and data-driven modeling techniques, this research will reveal how the relationship between network topology, propensity for relaxation, and glass-forming ability is encoded in the topography of the energy landscape. This study will also interrogate the existence, origin, and signatures of the intermediate phase and structural self-organization in chalcogenide glasses. This project also supports an extensive education plan. First, the PI will introduce a cross-disciplinary program connecting condensed matter, civil engineering, and computer science. Second, the machine learning techniques developed herein will be used to enable data-informed active learning in classrooms. Third, active collaborations with industrial partners will be leveraged to expose students to industrial environments and corporate cultures. Fourth, the PI will develop an undergraduate research program targeted to females, so as to enhance their retention in graduate school. Finally, the PI will collaborate with local high school teachers to develop demonstration modules, with activities involving virtual reality, structural truss kit set, and sugar glass crystallization to support material science outreach to K-12 students. Through these activities, the project will contribute to supporting glass science in the United States.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.

项目成果

Accessing a Forbidden Disordered State of a Zeolitic Imidazolate Framework with Higher Stiffness and Toughness through Irradiation

• DOI: 10.1021/acs.chemmater.2c01949
• 发表时间: 2022-09
• 期刊: Chemistry of Materials
• 影响因子: 8.6
• 作者: T. Du;S. S. Sørensen-S.;Qi Zhou;M. Bauchy;M. Smedskjaer

Mineral Dissolution under Electric Stimulation

• DOI: 10.1021/acs.jpcc.0c04823
• 发表时间: 2020-07
• 期刊: Journal of Physical Chemistry C
• 影响因子: 3.7
• 作者: Y. Hsiao;Xin Chen;Erika Callagon La Plante;Aditya Kumar;M. Bauchy;D. Simonetti;D. Jassby;J. Israelachvili;G. Sant

Deciphering a structural signature of glass dynamics by machine learning

• DOI: 10.1103/physrevb.106.214206
• 发表时间: 2022-12
• 期刊: Physical Review B
• 影响因子: 3.7
• 作者: Han Liu;M. Smedskjaer;M. Bauchy

Challenges and opportunities in atomistic simulations of glasses: a review

• DOI: 10.5802/crgeos.116
• 发表时间: 2022-05
• 期刊: Comptes Rendus. Géoscience
• 作者: Han Liu;Zhangji Zhao;Qi Zhou;Ruoxia Chen;Kai Yang;Zhe Wang;Longwen Tang;M. Bauchy

Analytical model of the network topology and rigidity of calcium aluminosilicate glasses

• DOI: 10.1111/jace.17781
• 发表时间: 2021-03
• 期刊: Journal of the American Ceramic Society
• 影响因子: 3.9
• 作者: Kai Yang;Yu-Song Hu;Zhou Li;N. Krishnan;M. Smedskjaer;C. Hoover;J. Mauro;G. Sant;M. Bauchy