D3SC: CDS&E: Collaborative Research: Machine Learning Modeling for the Reactivity of Organic Contaminants in Engineered and Natural Environments
D3SC:CDS
基本信息
- 批准号:2105005
- 负责人:
- 金额:$ 25.28万
- 依托单位:
- 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:
- 关键词:
项目摘要
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。
项目成果
期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 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:
- 影响因子:0
- 作者:Kai Zhang;Huichun Zhang
- 通讯作者: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:
- 影响因子:11.4
- 作者:Yang, Hongrui;Huang, Kuan;Wang, Feier
- 通讯作者:Wang, Feier
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Huichun Zhang其他文献
Transition metal-free, iodide-mediated domino carbonylation–benzylation of benzyl chlorides with arylboronic acids under ambient pressure of carbon monoxide
一氧化碳环境压力下,无过渡金属、碘化物介导的多米诺羰基化-苄基氯与芳基硼酸的苄基化
- DOI:
10.1039/c6gc00017g - 发表时间:
2016-05 - 期刊:
- 影响因子:9.8
- 作者:
Xin Zhang;Huichun Zhang;Qian Zhao;Wei Han - 通讯作者:
Wei Han
AI and Big Data in Water Environments
- DOI:
10.1021/acsestwater.2c00203 - 发表时间:
2022-05 - 期刊:
- 影响因子:0
- 作者:
Huichun Zhang - 通讯作者:
Huichun Zhang
Comparative research on nonlinear growth curve models for describing growth of Arabidopsis thaliana rosette leaves
描述拟南芥莲座叶生长的非线性生长曲线模型的比较研究
- DOI:
- 发表时间:
- 期刊:
- 影响因子:2.6
- 作者:
Xiang Jiao;Huichun Zhang;Jiaqiang Zheng - 通讯作者:
Jiaqiang Zheng
Integrating Bioinformatics To Identify Potential Cytokines ALPL /TNAP In Children With Spastic Cerebral Palsy
整合生物信息学识别痉挛性脑瘫儿童中潜在的细胞因子 ALPL /TNAP
- DOI:
10.21203/rs.3.rs-1080264/v1 - 发表时间:
2021 - 期刊:
- 影响因子:3.6
- 作者:
Xiaokun Wang;Chao Gao;Hequan Zhong;Xian;Rui Qiao;Huichun Zhang;Dongmei Yang;Yang Gao;Bing Li - 通讯作者:
Bing Li
Evaluating the environmental impact of selected chemical de-icers
评估所选化学除冰剂的环境影响
- DOI:
10.1093/tse/tdz015 - 发表时间:
2019 - 期刊:
- 影响因子:2.2
- 作者:
Xiang Li;B. Aken;E. McKenzie;Huichun Zhang;Bechara E. Abboud;W. Davenport - 通讯作者:
W. Davenport
Huichun Zhang的其他文献
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{{ truncateString('Huichun Zhang', 18)}}的其他基金
Predictive Modeling of Multi-Solute Adsorption Equilibrium based on Adsorbed Solution Theories
基于吸附溶液理论的多溶质吸附平衡预测模型
- 批准号:
1804708 - 财政年份:2018
- 资助金额:
$ 25.28万 - 项目类别:
Standard Grant
Synthetic Manganese Oxides for Oxidative and Catalytic Removal of Contaminants of Emerging Concern
用于氧化和催化去除新兴污染物的合成锰氧化物
- 批准号:
1808406 - 财政年份:2018
- 资助金额:
$ 25.28万 - 项目类别:
Standard Grant
Reduction of Nitrogen-Oxygen Containing Contaminants (NOCs) in Aquatic Environments
减少水生环境中的氮氧污染物 (NOC)
- 批准号:
1762686 - 财政年份:2017
- 资助金额:
$ 25.28万 - 项目类别:
Standard Grant
Impact of Interactions between Metal Oxides to Redox Reactivity of Iron and Manganese Oxides
金属氧化物之间的相互作用对铁和锰氧化物氧化还原反应性的影响
- 批准号:
1762691 - 财政年份:2017
- 资助金额:
$ 25.28万 - 项目类别:
Standard Grant
Reduction of Nitrogen-Oxygen Containing Contaminants (NOCs) in Aquatic Environments
减少水生环境中的氮氧污染物 (NOC)
- 批准号:
1507981 - 财政年份:2015
- 资助金额:
$ 25.28万 - 项目类别:
Standard Grant
Impact of Interactions between Metal Oxides to Redox Reactivity of Iron and Manganese Oxides
金属氧化物之间的相互作用对铁和锰氧化物氧化还原反应性的影响
- 批准号:
1236517 - 财政年份:2012
- 资助金额:
$ 25.28万 - 项目类别:
Standard Grant
BRIGE: Redox Noninnocent Ligands - Application to the Reductive Transformation of Veterinary Pharmaceuticals Containing Carbon-Nitrogen Double Bonds
BRIGE:氧化还原非无害配体——在含碳氮双键兽药还原转化中的应用
- 批准号:
1125713 - 财政年份:2011
- 资助金额:
$ 25.28万 - 项目类别:
Standard Grant
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