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

在NSF化学部环境化学科学项目的支持下，凯斯西储大学的Huichun Zhang教授和伊利诺伊大学香槟分校的Dong Wang教授将开发机器学习模型，以预测工程（水）和自然（土壤和沉积物）环境中数千种有机污染物（OC）的反应性。为了评估和减轻与大量OC相关的风险，需要准确的预测模型来提供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。它们具有多个局限性，例如所涉及的OC数量少且范围窄，以及分子性质的冗长计算。该项目将采用先进的机器学习算法来预测污染物的反应性。所获得的机器学习模型将有助于识别关注的OC并优化处理过程。此外，环境数据科学将作为一个新的教育轨道在试点规模发展。具有不同背景的研究生，本科生和高中生将从事跨学科研究，包括建模和实验工作。该项目还计划为6-12年级的女孩和代表性不足的大学生开展关于组织委员会的实践活动。 该研究将系统地开发全面准确的机器学习模型，用于预测高级氧化过程（AOP）中数千种OC的反应性，吸附到工程吸附剂上，吸附到土壤和沉积物上以及生物降解。本研究的目标是：1）挖掘文献和可用数据库，以获得最大的AOPs中污染物反应性、（ad）吸附和生物降解的数据集; 2）实验量化AOPs中选定的OCs的反应性、（ad）吸附和生物降解; 3）基于上述两个目标的数据，开发用于OCs反应性的置信度感知机器学习模型;（4）对所得到的模型进行解释，使其可信，并定义其适用范围。OC将通过新的化学表示法建模，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。在吸附模型中加入吸附剂的性质是将模型适用性扩展到不同吸附剂结构和性质的重要一步。正确地解释和修改所获得的模型和计算模型的置信界限将使所获得的模型trustable.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.