ITR - (ASE) - (sim+dmc): Parallel Data Mining for Nanoscale Kinetic Monte Carlo Simulation Models

ITR - (ASE) - (sim dmc)：纳米级动力学蒙特卡罗模拟模型的并行数据挖掘

• 批准号: 0840389
• 负责人: Talat Rahman
• 金额: $ 28.05万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-11-27 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0840389&HistoricalAwards=false
• 关键词: ITR; ASE; sim+dmc; Parallel; Data

Intellectual Merit:Advances in computational science and technology have made the theoreticalmodeling of materials processes and properties viable, desirable, and a strong supplement to experimental work. Since the understanding and manipulation of the macroscopic properties of materials relies on information obtained at the microscopic level, one of the challenges in ASE is in developing the framework for a seamless, multiscale study of materials properties and related phenomena. The kinetic Monte Carlo method is one such technique which is suitable for simulations over a large range of length and time scales and which has the potential to connect atomistic details with macroscopic observations.The standard method is, however, handicapped because of the requirement of prior knowledge of theunderlying atomic mechanisms and their energetics. Typically only a few processes involving singleatom motion are provided as input to kinetic Monte Carlo simulations, thereby neglecting the role ofcollective atomic motion, vacancy creation, and complex atomic processes, as well as biasing the timeevolution of the system. In the proposed research in ASE with technical focus in sim and dmc, we planto overcome these limitations through the inclusion of unique and innovative pattern recognition schemesalong with automated procedures for the calculation of system energetics on the fly. This procedure willallow the development of an extensive database of possible atomic events. The database so collected willserve as input for further analysis and processing using machine learning and data mining for thedevelopment of efficient, robust, and accurate mapping functions which will be extensively tested throughsimulations of a variety of phenomena in epitaxial growth and validated through comparison with relevantexperimental data. The resulting mapping functions will serve to vastly increase the accuracy and speedof simulations.Broader Impact:Our goal of creating accurate and efficient computational algorithms for the simulationof phenomena such as thin-film growth will be a significant achievement in the technical focus areas ofsim and dmc, because of the innovative methodologies resulting from cross-disciplinary approaches. Thesuccessful implementation of the algorithms for computer design of materials, however, will be abreakthrough in ASE, as it will enable the development of technologically important materials with muchreduced cost and much greater control.The work will also provide us opportunities for educational and outreach activities with broad national, international and societal impact. Apart from the education and training of our graduate and undergraduate students in ITR, we will propose to work with the K-12 community in this endeavor. We intend to do so through the integration of research and education. Our team will collectively incorporate products of the research into courses on computational methods in physics, on data mining, on machine learning, and on adaptive parallelization techniques. A module for instructional and outreach purposes will also be developed. Two high school teachers will be recruited to spend summer sessions at KSU. Regular outreach activities with K-12 teachers and students will help broaden the pool of individuals in IT and nanoscale science literate individuals. Existing international collaborations of the PI with Prof. Alatalo, Finland, Dr. Trushin, Russia, and Dr. Durukanoglu, Turkey will help extend the outcomes of the proposed work internationally.

智力优点：计算科学和技术的进步使得材料过程和性能的理论建模变得可行、理想，并且是对实验工作的有力补充。由于对材料宏观特性的理解和操纵依赖于在微观层面获得的信息，因此 ASE 的挑战之一是开发对材料特性和相关现象进行无缝、多尺度研究的框架。动力学蒙特卡罗方法就是这样一种技术，它适用于大范围的长度和时间尺度上的模拟，并且具有将原子细节与宏观观察联系起来的潜力。然而，由于需要先验知识，标准方法存在缺陷。基本原子机制及其能量学的知识。通常，仅提供少数涉及单原子运动的过程作为动力学蒙特卡罗模拟的输入，从而忽略了集体原子运动、空位产生和复杂原子过程的作用，并且使系统的时间演化产生偏差。在 ASE 提出的以 sim 和 dmc 为技术重点的研究中，我们计划通过纳入独特和创新的模式识别方案以及用于动态计算系统能量的自动化程序来克服这些限制。该过程将允许开发可能的原子事件的广泛数据库。如此收集的数据库将作为使用机器学习和数据挖掘进行进一步分析和处理的输入，以开发高效、稳健和准确的映射函数，这些函数将通过模拟外延生长的各种现象进行广泛测试，并通过与相关实验数据的比较进行验证。由此产生的映射函数将大大提高模拟的准确性和速度。更广泛的影响：我们的目标是为薄膜生长等现象的模拟创建准确且高效的计算算法，这将是 sim 和 dmc 技术重点领域的重大成就，因为跨学科方法产生的创新方法。然而，材料计算机设计算法的成功实施将成为 ASE 的一项突破，因为它将能够以大大降低的成本和更大的控制力来开发技术上重要的材料。这项工作还将为我们提供教育和推广活动的机会广泛的国家、国际和社会影响。除了 ITR 研究生和本科生的教育和培训之外，我们还将建议与 K-12 社区共同努力。我们打算通过研究和教育的结合来实现这一目标。我们的团队将把研究成果共同纳入物理学计算方法、数据挖掘、机器学习和自适应并行化技术的课程中。还将开发一个用于教学和外展目的的模块。两名高中教师将被招募到堪萨斯州立大学进行暑期课程。与 K-12 教师和学生定期开展的外展活动将有助于扩大具有 IT 和纳米科学素养的人员群体。 PI 与芬兰 Alatalo 教授、俄罗斯 Trushin 博士和土耳其 Durukanoglu 博士的现有国际合作将有助于在国际上推广拟议工作的成果。