CAREER: Reaction-Diffusion Kinetics with Tensor Networks

职业：张量网络的反应扩散动力学

• 批准号: 2239867
• 负责人: Todd Gingrich
• 金额: $ 65万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239867&HistoricalAwards=false
• 关键词: CAREER; Reaction; Diffusion; Kinetics; Tensor

With support from the Chemical Theory, Models, and Computational Methods Program in the Division of Chemistry Todd Gingrich of Northwestern University will develop and assess algorithms for simulating reaction-diffusion kinetics. Many self-assembly and signal transduction processes in both biological and synthetic systems are implemented through the reaction and diffusion of molecules. For example, proteins collude together in signaling cascades to detect stimuli and transduce that detection into a desired response. While there has been great progress both in understanding natural systems and in engineering synthetic ones, chemical reaction networks (CRNs) continue to pose a challenge, particularly when accounting for random fluctuations characteristic to the molecular scale. Currently, repeated noisy simulations can be combined to assess how fast-timescale events (individual reactions and diffusive steps) build together to generate slow-timescale responses. Dr. Gingrich and his research group will utilize a mathematical construction called a tensor network (TN) to pursue computational approaches that yield novel methods to analyze CRNs without the need for many repeated simulations. The work will result in disseminated computer code, which will also form the basis for two educational web-based simulation modules about fluctuations in chemical kinetics.Connecting microscopic interactions with emergent macroscopic phenomena is a central challenge of statistical mechanics. In chemical kinetics, this challenge manifests in the nonequilibrium spatio-temporal patterns which emerge from a balance between chemical reactions and diffusion. Subtle changes in the kinetic interactions between species in CRNs can sensitively impact the patterns that emerge, e.g., oscillatory chemical clocks, Turing patterns, and cellular signal transduction. The Gingrich team will develop, characterize, optimize, and ultimately apply a new computational method to study the sensitivity of patterns to the microscopic kinetic model. The research program will combine analytical techniques known as the Doi-Peliti (DP) framework with mature computational methods for TN many-body problems to offer an alternative perspective to kinetics problems which are routinely attacked through Monte Carlo sampling alone. An accompanying educational plan will use web simulations to teach aspects of stochastic kinetics, with one module targeting undergraduates and another targeting graduate students.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.

在化学系化学理论、模型和计算方法计划的支持下，西北大学的托德·金里奇将开发和评估模拟反应扩散动力学的算法。生物和合成系统中的许多自组装和信号转导过程都是通过分子的反应和扩散来实现的。例如，蛋白质在信号级联中串连在一起，以检测刺激并将检测转化为所需的反应。虽然在理解自然系统和工程合成系统方面都取得了很大的进展，但化学反应网络(CRN)仍然构成了一个挑战，特别是在考虑分子尺度的随机波动时。目前，可以结合重复的噪声模拟来评估快时间尺度事件(个别反应和扩散步骤)如何聚集在一起产生慢时间尺度反应。金里奇博士和他的研究小组将利用一种名为张量网络(TN)的数学结构来寻求计算方法，从而产生新的方法来分析CRN，而不需要许多重复的模拟。这项工作将产生传播的计算机代码，这也将形成两个基于网络的关于化学动力学波动的教育模拟模块的基础。将微观相互作用与新出现的宏观现象联系起来是统计力学的一个中心挑战。在化学动力学中，这一挑战表现在化学反应和扩散之间的平衡产生的非平衡时空模式中。CRN中物种间动力学相互作用的细微变化可以敏感地影响出现的模式，例如振荡化学钟、图灵模式和细胞信号转导。金里奇团队将开发、表征、优化并最终应用一种新的计算方法来研究模式对微观动力学模型的敏感性。该研究计划将把被称为Doi-Peliti(DP)框架的分析技术与TN多体问题的成熟计算方法相结合，为通常仅通过蒙特卡罗采样来攻击的动力学问题提供另一种视角。随附的教育计划将使用网络模拟教授随机动力学的各个方面，一个模块针对本科生，另一个针对研究生。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。