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

D3SC：CDS

• 批准号: 2105032
• 负责人: Dong Wang
• 金额: $ 15万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105032&HistoricalAwards=false
• 关键词: D3SC; CDS& Collaborative; Research; Machine

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.

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，凯斯西储大学的张惠春教授和伊利诺伊大学香槟分校的王东教授将开发机器学习模型来预测数千种有机污染物（OC）的反应性在工程（水）和自然（土壤和沉积物）环境中，为了评估和减轻与大量有机污染物相关的风险，需要准确的预测模型，以便在重要的水处理过程中轻松提供对其反应性的合理估计。然而，现有模型严重依赖传统的统计方法，例如涉及的有机物数量少、范围窄，以及分子特性的计算冗长等。获得的机器学习模型将有助于识别相关的OC并优化处理流程。此外，环境数据科学将作为试点规模开发，以吸引不同背景的研究生、本科生和高中生参与。跨学科研究，包括该项目还计划为 6-12 年级的女生和代表性不足的大学生开展 OC 实践活动，这项研究将不可避免地开发出全面而准确的机器学习模型，用于预测高级氧化过程中数千个 OC 的反应性。 （AOP）、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 本研究的目标是 1）挖掘文献和可用数据库以获得最大的数据集。 AOP 中的污染物反应性、（吸附）吸附和生物降解；2）通过实验量化 AOP 中选定的 OC 的反应性、（吸附）吸附和生物降解；3）根据以下数据开发 OC 反应性的可信机器学习模型上述两个目标；4) 解释所获得的模型，使其可信并定义其适用范围，将通过新的化学表示（包括分子指纹、分子）来建模。图像，以及它们与分子描述符的不同组合。在（吸附）吸附模型中包含（吸附）吸附剂特性将是扩展模型适用于各种（吸附）吸附剂结构和特性的重要一步。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。