Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences
Abstract
Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.
- Authors:
-
[1];
[2];
[3];
[4]
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.
- Department of Chemical and Biomolecular Engineering, University of Delaware,150 Academy Street, Colburn Laboratory Newark, DE 19716, USA.
- Department of Mathematics and Statistics, University of Massachusetts at Amherst, Amherst, MA 01003, USA.
- Department of Chemical and Biomolecular Engineering, University of Delaware,150 Academy Street, Colburn Laboratory Newark, DE 19716, USA., Catalysis Center for Energy Innovation, University of Delaware, 221 Academy Street, 250R, Newark, DE 19716, USA.
- Publication Date:
- Research Org.:
- RAPID Manufacturing Inst., Newark, DE (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office; US Air Force Office of Scientific Research (AFOSR); Defense Advanced Research Projects Agency (DARPA); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
- OSTI Identifier:
- 1675000
- Alternate Identifier(s):
- OSTI ID: 1768836
- Grant/Contract Number:
- EE0007888; AC02-05CH11231; FA-9550-18-1-0214; W911NF1520122
- Resource Type:
- Published Article
- Journal Name:
- Science Advances
- Additional Journal Information:
- Journal Name: Science Advances Journal Volume: 6 Journal Issue: 42; Journal ID: ISSN 2375-2548
- Publisher:
- AAAS
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Feng, Jinchao, Lansford, Joshua L., Katsoulakis, Markos A., and Vlachos, Dionisios G. Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences. United States: N. p., 2020.
Web. doi:10.1126/sciadv.abc3204.
Feng, Jinchao, Lansford, Joshua L., Katsoulakis, Markos A., & Vlachos, Dionisios G. Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences. United States. https://doi.org/10.1126/sciadv.abc3204
Feng, Jinchao, Lansford, Joshua L., Katsoulakis, Markos A., and Vlachos, Dionisios G. Wed .
"Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences". United States. https://doi.org/10.1126/sciadv.abc3204.
@article{osti_1675000,
title = {Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences},
author = {Feng, Jinchao and Lansford, Joshua L. and Katsoulakis, Markos A. and Vlachos, Dionisios G.},
abstractNote = {Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.},
doi = {10.1126/sciadv.abc3204},
journal = {Science Advances},
number = 42,
volume = 6,
place = {United States},
year = {Wed Oct 14 00:00:00 EDT 2020},
month = {Wed Oct 14 00:00:00 EDT 2020}
}
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