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

D3SC：CDS

• 批准号: 2105005
• 负责人: Huichun Zhang
• 金额: $ 25.28万
• 依托单位: Case Western Reserve University
• 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=2105005&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，凯斯西储大学的张慧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，以预测工程（水）和自然（土壤和沉积物）环境中数千种有机污染物（OCs）的反应性。为了评估和减轻与大量OCs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和环境中随时提供其反应性的合理估计。然而，现有的模型严重依赖于传统的统计方法。它们有许多局限性，如涉及的oc数量少，范围窄，分子性质的计算时间长。该项目将采用先进的机器学习算法来预测污染物的反应。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提供OCs实践活动。本研究将系统地开发全面而准确的机器学习模型，用于预测数千种OCs在高级氧化过程（AOPs）中的反应性，在工程吸附剂上的吸附，在土壤和沉积物上的吸附以及生物降解。本研究的目标是1)挖掘文献和现有数据库，以获得AOPs中污染物反应性、（和）吸附和生物降解的最大数据集；2)实验量化所选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，开发OCs反应性的自信感知机器学习模型；4)对得到的模型进行解释，使其可信，并定义其适用范围。OCs将通过新的化学表征来建模，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）吸附模型中加入（ad）吸附剂的性质将是扩大模型适用于不同（ad）吸附剂结构和性质的重要一步。对得到的模型进行正确的解释和修改，计算模型置信区间，可以使得到的模型具有可信度。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。

项目成果

Abiotic Reduction of Organic and Inorganic Compounds by Fe(II)-Associated Reductants: Comprehensive Data Sets and Machine Learning Modeling

Fe(II) 相关还原剂对有机和无机化合物的非生物还原：综合数据集和机器学习建模

• DOI: 10.1021/acs.est.2c09724
• 发表时间: 2023
• 期刊: Environmental Science & Technology
• 影响因子: 11.4
• 作者: Gao, Yidan;Zhong, Shifa;Zhang, Kai;Zhang, Huichun
• 通讯作者: Zhang, Huichun

Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds

• DOI: 10.1021/acsestwater.2c00193
• 发表时间: 2022-07
• 期刊: ACS ES&T Water
• 作者: Kai Zhang;Huichun Zhang

Predicting Heavy Metal Adsorption on Soil with Machine Learning and Mapping Global Distribution of Soil Adsorption Capacities

• DOI: 10.1021/acs.est.1c02479
• 发表时间: 2021-10-07
• 期刊: ENVIRONMENTAL SCIENCE & TECHNOLOGY
• 影响因子: 11.4
• 作者: Yang, Hongrui;Huang, Kuan;Wang, Feier
• 通讯作者: Wang, Feier