BioCompoundML: A General Biofuel Property Screening Tool for Biological Molecules Using Random Forest Classifiers
- Sandia National Lab. (SNL-CA), Livermore, CA (United States); Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States)
- Sandia National Laboratories, Livermore, California 94551, United States
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sandia National Lab. (SNL-CA), Livermore, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Screening a large number of biologically derived molecules for potential fuel compounds without recourse to experimental testing is important in identifying understudied yet valuable molecules. Experimental testing, although a valuable standard for measuring fuel properties, has several major limitations, including the requirement of testably high quantities, considerable expense, and a large amount of time. This paper discusses the development of a general-purpose fuel property tool, using machine learning, whose outcome is to screen molecules for desirable fuel properties. BioCompoundML adopts a general methodology, requiring as input only a list of training compounds (with identifiers and measured values) and a list of testing compounds (with identifiers). For the training data, BioCompoundML collects open data from the National Center for Biotechnology Information, incorporates user-provided features, imputes missing values, performs feature reduction, builds a classifier, and clusters compounds. BioCompoundML then collects data for the testing compounds, predicts class membership, and determines whether compounds are found in the range of variability of the training data set. This tool is demonstrated using three different fuel properties: research octane number (RON), threshold soot index (TSI), and melting point (MP). We provide measures of its success with these properties using randomized train/test measurements: average accuracy is 88% in RON, 85% in TSI, and 94% in MP; average precision is 88% in RON, 88% in TSI, and 95% in MP; and average recall is 88% in RON, 82% in TSI, and 97% in MP. The receiver operator characteristics (area under the curve) were estimated at 0.88 in RON, 0.86 in TSI, and 0.87 in MP. We also measured the success of BioCompoundML by sending 16 compounds for direct RON determination. Finally, we provide a screen of 1977 hydrocarbons/oxygenates within the 8696 compounds in MetaCyc, identifying compounds with high predictive strength for high or low RON.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- AC36-08GO28308; AC04-94AL85000; 347AC36-99GO10337; AC02- 05CH11231
- OSTI ID:
- 1327434
- Alternate ID(s):
- OSTI ID: 1332664; OSTI ID: 1440943
- Report Number(s):
- NREL/JA-5400-67434
- Journal Information:
- Energy and Fuels, Journal Name: Energy and Fuels Vol. 30 Journal Issue: 10; ISSN 0887-0624
- Publisher:
- American Chemical Society (ACS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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