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NSF化学司的环境化学科学计划的支持下，凯斯·张大学（Case Western Reserve University）的Huichun Zhang教授和伊利诺伊大学Urbana University of Illinois Urbana的Dong Wang将开发机器学习模型，以预测工程（水）和自然（水）和自然（土壤）环境中成千上万的有机污染物（OCS）的反应性。为了评估和减轻与这大量OC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很容易地提供其反应性的合理估计。但是，现有模型在很大程度上依赖于常规的统计方法。它们具有多个限制，例如涉及的OC的较小数量和狭窄的范围以及分子特性的冗长计算。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。获得的机器学习模型将有助于确定关注的OC并优化治疗过程。此外，环境数据科学将作为试点量表的新教育轨道发展。具有潜水员背景的研究生，本科和高中生将从事跨学科研究，包括建模和实验性工作。该项目还计划在6至12年级的OCS上进行OCS的实践活动，而代表性不足的大学生则计划。这项研究将系统地开发全面，准确的机器学习模型，以预测成千上万的OC在高级氧化过程中的反应性（AOP），在工程的吸附剂上增加吸附，吸附到土壤和沉积物中以及生物降解。这项研究的目标是1）挖掘文献和可用数据库，以获取AOPS（AD）吸附和生物降解中最大的污染反应性数据集； 2）实验量化所选OC在AOPS（AD）吸附和生物降解中的反应性； 3）基于上述两个目标的数据，开发了感知信心的机器学习模型，以实现OC的反应性； 4）解释获得的模型，以使其可信赖并定义其适用性域。 OCS将通过新的化学表示，包括分子指纹，分子图像以及与分子描述符的不同组合进行建模。在（AD）吸附模型中包括（AD）吸附性能将是将模型适用性扩展到潜水员（AD）吸附结构和特性的主要步骤。正确解释和修改所获得的模型并计算模型置信范围将使获得的模型可信赖。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估被认为是宝贵的支持。