EAGER: ADAPT: Optimizing Chemical Reaction Networks With AI

西北大学的托德·金里奇得到了美国国家科学基金会数学和物理科学局、人工智能项目和化学部颁发的奖项的支持，该奖项旨在开发和评估用于优化反应扩散化学的人工智能算法。例如，反应和扩散的结合使生命系统能够调节基本的过程，例如，信号如何在大脑中处理和传播。生物系统不是通过流经计算机芯片和电线的电子发挥作用，而是通过分子相互转化(反应)和分子在空间中扩散来发挥作用。然而，目前还不清楚如何最好地混合必要的反应和扩散，以实现预期的功能。设计反应扩散化学的一个重要障碍是单个反应和扩散事件的发生具有一定的随机性，而计算模拟必须在存在噪声随机波动的情况下优化化学。金里奇博士和他的研究小组正在开发新的算法，利用一种名为张量网络的数学结构，有效地对噪音进行平均，从而寻求减少噪音的计算方法。作为另一个例子，可以产生一些化学反应，其作用就像一个钟，分子在低浓度和高浓度之间振荡。正在开发的方法是人工智能工具，它将确定调节反应扩散化学以调节振荡的策略。这些技术进步建立在i张量软件库的基础上，将公开和自由地传播。这项研究将与拉德布大学人工智能(AI)专业的Tal Kachman团队合作完成。该项目旨在开发识别速率常数的AI算法，以通过基于梯度的搜索来优化目标函数，其中梯度衡量由于基本反应速率的微小变化而产生的改进(例如，在化学振荡器中)。核心技术挑战是计算这些梯度，这是一个需要准确和有效的数值解的问题，用于异常高维空间中的化学动力学。一种天真的方法利用确定性的、耦合的微分方程组来描述质量-作用动力学，但众所周知，紧急化学反应网络现象只被结合了化学动力学的随机性质的算法捕捉到。像吉莱斯皮算法这样的算法生成化学动力学的随机实现，并通过对许多噪声轨迹进行平均来实现精度。通过利用Doi-Peliti形式主义与量子动力学的类比，金里奇博士和他的研究小组正在开发和分析一种替代方法，该方法使用张量网络来有效地平均所有可能的轨迹。这一奖项反映了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。

Todd Gingrich of Northwestern University is supported by an award from the by an award from the NSF Directorate of Mathematical and Physical Sciences, Artificial Intelligence Program, and the Division of Chemistry, to develop and assess AI algorithms for optimizing reaction-diffusion chemistry. As an example, the combination of reaction and diffusion enables living systems to regulate essential processes, e.g., how signals are processed and propagated in the brain. Rather than functioning with electrons flowing through computer chips and wires, biological systems perform functions by molecular interconversion (reactions) and molecular diffusion through space. It is not, however, clear how to best mix the necessary reactions and diffusion to achieve a desired function. A significant barrier to designing reaction-diffusion chemistry is that the individual reactions and diffusion events occur with some randomness, and computational simulations must optimize the chemistry in the presence of noisy stochastic fluctuations. Dr. Gingrich and his research group are pursuing computational approaches to mitigate the noise by developing new algorithms that utilize a mathematical construction called a tensor network, to effectively average over the noise. As another example, some chemical reactions can be generated that act as a clock with a molecule oscillating between low and high concentration. The methods being developed are AI tools that would identify strategies to modulate the reaction-diffusion chemistry to regulate the oscillations. Those technical advances, built upon the iTensor software library, will be openly and freely disseminated. The research will be done in collaboration with the group of Tal Kachman, specializing in artificial intelligence (AI) at Radboud University (NL).This project aims to develop AI algorithms that identify rate constants to optimize an objective function by gradient-based search, where the gradients measure improvements (e.g., in a chemical oscillator) due to small changes in the elementary reaction rates. The core technical challenge is to compute those gradients, a problem that demands accurate and efficient numerical solutions, for chemical kinetics in exceptionally high-dimensional space. A naïve approach utilizes deterministic, coupled differential equations for the mass-action kinetics, but it is well-known that emergent chemical reaction network phenomena are only captured by algorithms that incorporate the stochastic nature of chemical kinetics. Algorithms like the Gillespie algorithm generate stochastic realizations of chemical kinetics and achieve accuracy by averaging over many noisy trajectories. By utilizing the Doi-Peliti formalism’s analogies with quantum dynamics, Dr. Gingrich and his research group are developing and analyzing an alternative method that uses tensor networks to effectively average over all possible trajectories.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.