| 项目编号: | 1705592
|
| 项目名称: | SusChEM: Machine learning blueprints for greener chelants |
| 作者: | John Keith
|
| 承担单位: | University of Pittsburgh
|
| 批准年: | 2017
|
| 开始日期: | 2017-08-01
|
| 结束日期: | 2020-07-31
|
| 资助金额: | 299999
|
| 资助来源: | US-NSF
|
| 项目类别: | Standard Grant
|
| 国家: | US
|
| 语种: | 英语
|
| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
|
| 英文关键词: | project
; machine learning method
; quantum chemistry-based machine learning
; chelant/metal complex property
; novel chelant structure
; promising chelant structure
; overall chelant stability constant
; novel chelant
; state-of-the-art machine
; chelant/metal complex
; hypothetical chelant structure
|
| 英文摘要: | 1705592 (Keith). Chelating agents have recently been identified as a key category of chemical products that are ripe for greener design. It is hypothesized that identifying better alternatives will require far broader explorations of chemical compound space than what is possible with conventional trial and error experimentation. In this project, accurate quantum chemistry calculations will be used to train state-of-the-art machine learning methods that will allow prediction of structures of greener chelating agents.
The machine learning method that will be developed promises a novel route to rapidly predict properties of chelant/metal complexes, not only with higher accuracy but with six orders of magnitude less computational time than conventional predictive quantum chemistry methods (e.g. Kohn-Sham Density Functional Theory). With this computational tool, it will be possible to rapidly screen through about 100,000 hypothetical chelant structures to predict those that would bind strongly to different metal ions. The project will also screen these complexes to see which have high propensities to degrade in reasonable timeframes, and which have low probabilities of being toxic. The top candidates from this novel screening approach will then be experimentally synthesized and tested. This will validate if quantum chemistry-based machine learning would be a transformative tool for environmental sustainability and green chemical design by being a more predictive supplement and/or alternative to conventional QSAR models. Four basic scientific questions will be addressed by the project. First, state-of-the-art computational quantum chemistry will be used to develop a quantitative understanding of which chelant/metal complex properties (bond energies, pKas, etc.) best correlate with overall chelant stability constants in aqueous solutions. Second, machine learning methods will be developed to be used to drive in silico searches for non-traditional molecular structures that would be able to bind as strong (or stronger) to different metal ions as EDTA. Third, additional computational screening procedures will then be used to find which of these promising chelant structures are unlikely to be non-toxic and nonpersistent in nature. Finally, it will be experimentally validated which of the novel chelants identified via computation would be industrially synthesized and economically viable for widespread use. If successful, this research effort would signify a paradigm shift for computer-aided design of greener chelants used in detergents, treatments of heavy metal poisoning, metal extraction for soil treatments, waste remediation, sequestering normally occurring radioactive materials from hydraulic fracturing sites, and water purification. This project will lay important foundational work that is needed to introduce new state-of-the-art computational modeling tools with greater predictive capacity than widely used QSAR models. All developed computer programs and accompanying tutorials for how to use the programs will be made freely available on the website of the PI. The educational component of this project will develop a computer game, "Chelate-it", which will allow students to quantify different fundamental chemical bonding concepts involved in chelation and then use that knowledge to design novel chelant structures on their own. The computer game will be tested in a summer school program at the University of Pittsburgh for underrepresented 10th grade students. The capacity for the computer game to educate students about chemical bonding, environmental sustainability engineering, and research will be assessed. |
| 资源类型: | 项目
|
| 标识符: | http://119.78.100.158/handle/2HF3EXSE/89668
|
| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
|
|
There are no files associated with this item.
|
| Recommended Citation: | |
John Keith. SusChEM: Machine learning blueprints for greener chelants. 2017-01-01.
|
|
|