EAGER: Collaborative Research: MATDAT18 Type-I: Development of a machine learning framework to optimize ReaxFF force field parameters

EAGER：协作研究：MATDAT18 Type-I：开发机器学习框架以优化 ReaxFF 力场参数

• 批准号: 1842952
• 负责人: Tirthankar Dasgupta
• 金额: $ 14万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1842952&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; MATDAT18; Type

NONTECHNICAL SUMMARIES.This award supports continued collaboration of materials researchers with data scientists kindled at the MATDAT18 Datathon event. Recent advancements in technological devices, such as smart phones, batteries, and solar cells, are consequences of the discovery and application of novel materials. Computer simulations of systems of atoms could be insightful in predicting and discovering new materials. Simulations based on quantum mechanics are computationally expensive and prohibitive for all but for systems of a few atoms. Simulations involving a much larger number of atoms can be done using molecular dynamics which utilizes models for the interactions between atoms. ReaxFF is one such interaction model which can also describe chemical bonding. Currently, more than a thousand academic groups and companies are using ReaxFF to model systems of atoms. It takes many parameters to fully specify a ReaxFF model. These parameters control the interactions between atoms and must be individually optimized for different types of materials. Due to the prohibitively large number of possible combinations of parameters, this optimization process is time consuming and complex, and consequently limits the applicability of ReaxFF. A procedure that can produce optimum parameter sets within a reasonable time will facilitate novel material research by accelerating the investigation of underlying physics and chemistry on the scale of atoms. Recent developments in machine learning are promising in terms of solving such high dimensional global optimization problems. The goal of this study is to develop a procedure that will enable fast and high-quality force field development using machine learning models and make this procedure accessible to all current and future ReaxFF users.The results of this project can also be applied to other large-scale multi-objective optimization problems and can have impacts on many scientific disciplines that involve large and complex data. The developed machine learning code and optimization procedure will be shared with researchers through the Materials Computation Center at Penn State University and GitHub. Some outreach programs will be conducted for educating the next generation of materials scientists, data scientists and statisticians. The research teams will create diverse environments in their laboratories in terms of race, gender and national origin. The research will also provide an excellent opportunity to recruit students from underrepresented groups to participate in projects at the interface between materials science, data science, and statistics and is highly relevant to societal needs.TECHNICAL SUMMARYThis award supports continued collaboration between a materials researcher and a data scientist kindled at the MATDAT18 Datathon event. ReaxFF is a commonly used reactive force field method, capable of simulating bond formation and dissociation in large atomistic systems. In order to reveal the physics behind these systems accurately by using the ReaxFF simulations, the force field parameters must be optimized for each different materials system, and the high-dimensional force field parameter landscape should be explored thoroughly during optimization. However, the large number of existing parameters limit the optimization stage of the force field development, as the conventional optimization approaches become time-consuming. This challenge can be resolved by the development of an efficient optimization framework. In this project, an efficient sequential optimization framework will be developed, including a "minimum energy" sequential search and a novel "divide-and-conquer" strategy for efficient Gaussian process modeling. This study will make ReaxFF force field development more practical, which will enable fast access to physics and chemistry in a wide range of material systems to enhance novel material design. This project can serve is an example of how rigorous statistical/machine learning methods can be used to tackle important problems in materials science and engineering. The project may be transformative, as it can empower the atomistic-scale understanding of materials systems by using novel techniques in data science and machine learning. The developed iterative optimization procedure will be combined under Python programming language to facilitate implementation to commercial molecular dynamics packages. From a statistical point of view, the idea of divide-and-conquer and design-based subsample aggregation to reduce computational complexity of Gaussian process modeling is innovative. It can open a new path in statistics/data science with big data settings and can lead to advances in machine learning and optimization. The sequential optimization framework constructed for high-dimensional problems may open new avenues for studying problems with massive and complex input structure and energize both theoretical and applied research in statistics and machine learning.The award is jointly funded through the Division of Materials Research and the Division of Mathematical Sciences in the Mathematical and Physical Sciences Directorate.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。该奖项支持材料研究人员与数据科学家在MATDAT 18 Datathon活动中的持续合作。智能手机、电池和太阳能电池等技术设备的最新进展是新材料的发现和应用的结果。原子系统的计算机模拟在预测和发现新材料方面可能很有见地。基于量子力学的模拟在计算上是昂贵的，除了几个原子的系统之外，对所有的系统都是禁止的。涉及大量原子的模拟可以使用分子动力学来完成，该分子动力学利用原子之间相互作用的模型。ReaxFF是一个这样的相互作用模型，它也可以描述化学键。目前，超过1000个学术团体和公司正在使用ReaxFF来模拟原子系统。它需要很多参数来完全指定一个ReaxFF模型。这些参数控制原子之间的相互作用，必须针对不同类型的材料进行单独优化。由于参数的可能组合的数量非常多，这种优化过程既耗时又复杂，因此限制了ReaxFF的适用性。一个可以在合理的时间内产生最佳参数集的程序将通过加速对原子尺度上的基础物理和化学的研究来促进新材料的研究。机器学习的最新发展在解决这种高维全局优化问题方面很有前途。本研究的目标是开发一个程序，使使用机器学习模型的快速和高质量的力场开发，并使该程序可供所有当前和未来的ReaxFF用户使用。该项目的结果也可以应用于其他大规模多目标优化问题，并可能对涉及大量复杂数据的许多科学学科产生影响。开发的机器学习代码和优化程序将通过宾夕法尼亚州立大学的材料计算中心和GitHub与研究人员共享。一些推广计划将用于教育下一代材料科学家，数据科学家和统计学家。研究团队将在实验室中创造不同种族、性别和民族的环境。该研究还将提供一个绝佳的机会，招募来自代表性不足的群体的学生参与材料科学，数据科学和统计学之间的接口项目，并与社会需求高度相关。技术总结该奖项支持材料研究人员和数据科学家之间的持续合作，在MATDAT 18 Datasheet事件中点燃。ReaxFF是一种常用的反应力场方法，能够模拟大型原子系统中的键形成和解离。为了通过使用ReaxFF模拟准确地揭示这些系统背后的物理，必须针对每个不同的材料系统优化力场参数，并且在优化过程中应彻底探索高维力场参数景观。然而，大量的现有参数限制了力场开发的优化阶段，因为传统的优化方法变得耗时。这一挑战可以通过开发一个有效的优化框架来解决。在这个项目中，将开发一个有效的顺序优化框架，包括一个“最小能量”的顺序搜索和一个新的“分而治之”的策略，有效的高斯过程建模。这项研究将使ReaxFF力场的开发更加实用，这将使人们能够快速访问各种材料系统中的物理和化学，以增强新型材料的设计。 该项目可以作为严格的统计/机器学习方法如何用于解决材料科学和工程中的重要问题的一个例子。该项目可能是变革性的，因为它可以通过使用数据科学和机器学习中的新技术来增强对材料系统的原子级理解。开发的迭代优化程序将结合在Python编程语言，以促进商业分子动力学软件包的实施。从统计的角度来看，分而治之的思想和设计为基础的子样本聚合，以减少高斯过程建模的计算复杂度是创新的。它可以通过大数据设置在统计/数据科学中开辟一条新的道路，并可以导致机器学习和优化的进步。本文提出了一种基于序贯优化框架的高精度优化方法，三维问题可能为研究具有大量复杂输入结构的问题开辟新的途径，并激励统计和机器学习的理论和应用研究。该奖项由数学和物理科学理事会的材料研究部和数学科学部共同资助。该奖项反映了NSF的法定使命，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，认为值得支持。