ERI - Simulation methods for competitive adsorption in Bronsted acidic zeolites

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。用于催化剂和吸附剂的合成沸石的生产是一个价值数十亿美元的产业。沸石是一种具有分子级孔隙的材料，可用于石油炼制、汽车尾气处理和生物质处理等催化应用。沸石经常催化水中的化学反应，就像从生物质中生产可再生燃料和化学品一样。水分子与沸石催化剂的相互作用是非常复杂的，但了解这些系统的行为是非常重要的，这样沸石就可以根据应用优化性能。不幸的是，现有的计算建模工具难以准确有效地预测复杂的化学混合物如何与沸石催化活性位点相互作用。该项目将开发模拟技术，使现有方法得到数量级的改进，从而产生关于水如何与沸石催化剂活性位点相互作用的基本知识。符号回归是一种机器学习工具，用于识别代表给定数据集的方程，也将用于描述酸和碱之间的基本相互作用。以社区为基础的学习和推广活动计划让高中生参与数学、科学和机器学习的符号回归。该项目将推进蒙特卡罗模拟方法，使酸性沸石中水吸附的有效采样成为可能。具体目标包括：1)引入蒙特卡罗移动，直接采样沸石中水团簇的质子化状态；2)利用文献力场模拟水和烷烃的竞争吸附；3)通过使用符号回归学习量子化学计算方程，生成酸碱相互作用的原子间电位。预期的结果将为具有活性位点的多孔材料的竞争性化学吸附模拟奠定基础，并发现简单表征酸碱相互作用势能表面的新方程。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2)The production of synthetic zeolites for use as catalysts and adsorbents is a multi-billion-dollar industry. Zeolites are materials having molecule-scale pores that enable catalytic applications in petroleum refining, automotive emissions treatment, and biomass processing. Zeolites frequently catalyze chemical reactions in water, as is the case when producing renewable fuels and chemicals from biomass. The interactions of water molecules with the zeolite catalysts are incredibly complex, but it is important to understand how these systems behave so that zeolites can be optimized for application-based performance. Unfortunately, existing computational modeling tools struggle to both accurately and efficiently predict how complex chemical mixtures interact with zeolite catalytic active sites. This project will develop simulation techniques that will enable orders-of-magnitude improvements over current approaches, thereby generating fundamental knowledge of how water interacts with zeolite catalyst active sites. Symbolic regression, a machine learning tool for identifying equations that represent a given dataset, will also be explored for describing fundamental interactions between acids and bases. Community-based learning and outreach activities are planned to engage high school students in symbolic regression for math, science, and machine learning. This project will advance Monte Carlo simulation methods to enable efficient sampling of water adsorption in acidic zeolites. Specific aims include 1) introducing Monte Carlo moves for directly sampling protonation states of water clusters in zeolites, 2) demonstrating simulations of competitive adsorption of water and alkanes using literature force fields, and 3) generating interatomic potentials for acid-base interactions by using symbolic regression to learn equations from quantum chemistry calculations. The anticipated outcomes will lay the groundwork for simulations of competitive chemisorption in porous materials with active sites, as well as discover new equations for simply characterizing potential energy surfaces of acid-base interactions.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.