D3SC: CDS&E: Collaborative Research: Machine Learning Modeling for the Reactivity of Organic Contaminants in Engineered and Natural Environments

在美国国家科学基金会化学部环境化学科学项目的支持下，凯斯西部储备大学的张惠春教授和伊利诺伊大学香槟分校的王栋教授将开发机器学习模型，以预测数千种有机污染物(OCS)在工程(水)和自然(土壤和沉积物)环境中的反应活性。为了评估和减轻与这些大量的OCS相关的风险，需要准确的预测模型来随时提供对其反应性的合理估计，无论是在重要的水处理过程中还是在环境中。然而，现有的模型严重依赖于传统的统计方法。它们具有多重限制，如所涉及的OCS数量少、范围窄，以及对分子性质的冗长计算。该项目将使用先进的机器学习算法来预测污染物的反应性。获得的机器学习模型将有助于识别关注的OCS并优化治疗过程。此外，环境数据科学将被发展为试点规模的新的教育轨道。不同背景的研究生、本科生和高中生将从事跨学科研究，包括建模和实验工作。该项目还计划在OCS上为6-12年级的女孩和代表性不足的大学生开展实践活动。这项研究将系统地开发全面和准确的机器学习模型，以预测数千个OCS在高级氧化过程(AOPS)、工程吸附剂上的吸附、土壤和沉积物上的吸附以及生物降解中的反应性。这项研究的目标是1)挖掘文献和现有的数据库，以获得AOPS中污染物反应性的最大数据集，(Ad)吸附和生物降解；2)通过实验量化选定的OCS在AOPS中的反应性，(Ad)吸附和生物降解；3)基于上述两个目标的数据，为OCS的反应性开发置信度感知的机器学习模型；以及4)解释所获得的模型，使其可信并确定其适用范围。OCS将由新的化学表示来建模，包括分子指纹、分子图像以及它们与分子描述符的不同组合。在(Ad)吸附模型中包含(Ad)吸附剂特性将是将模型的适用性扩展到各种(Ad)吸附剂结构和性质的重要一步。对获得的模型进行适当的解释和修改，并计算模型的置信限，将使获得的模型可信。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。

With support from the Environmental Chemical Sciences Program of the NSF Division of Chemistry, Professors Huichun Zhang of Case Western Reserve University and Dong Wang of University of Illinois Urbana—Champaign will develop machine learning models to predict the reactivity of thousands of organic contaminants (OCs) in engineered (water) and natural (soil and sediment) environments. To assess and mitigate risks associated with this vast number of OCs, accurate predictive models are needed to readily provide reasonable estimates of their reactivity, both during important water treatment processes and in the environment. However, existing models rely heavily on conventional statistical methods. They have multiple limitations such as small numbers and narrow scopes of OCs involved and lengthy calculations of molecular properties. The project will employ advanced machine learning algorithms to predict contaminant reactivities. The obtained machine learning models will help identify OCs of concern and optimize the treatment processes. In addition, environmental data science will be developed as a new educational track at the pilot scale. Graduate, undergraduate and high school students with diverse backgrounds will be engaged in interdisciplinary research, including modeling and experimental work. The project also plans hands-on activities on OCs for girls in grade 6-12 and underrepresented college students. This study will systematically develop comprehensive and accurate machine learning models for predicting the reactivity of thousands of OCs in advanced oxidation processes (AOPs), adsorption onto engineered adsorbents, sorption onto soils and sediments, and biodegradation. The objectives of this research are to 1) mine the literature and available databases to obtain the largest datasets of contaminant reactivity in AOPs, (ad)sorption and biodegradation; 2) experimentally quantify the reactivity of selected OCs in AOPs, (ad)sorption and biodegradation; 3) develop confidence-aware machine learning models for the reactivity of OCs based on the data from the above two objectives; and 4) interpret the obtained models to make them trustable and define their applicability domains. OCs will be modeled by new chemical representations including molecular fingerprints, molecular images, and different combinations of them with molecular descriptors. Including (ad)sorbent properties in the (ad)sorption models will be a major step to expand the model applicability to diverse (ad)sorbent structures and properties. Properly interpreting and modifying the obtained models and calculating model confidence bounds will make the obtained models trustable.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.