Journal
SCIENCE ADVANCES
Volume 6, Issue 42, Pages -Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.abc3204
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Funding
- NSF TRIPODS [CISE-1934846]
- Air Force Office of Scientific Research (AFOSR) [FA-9550-18-1-0214]
- Defense Advanced Research Projects Agency (DARPA) EQUiPS program [W911NF1520122]
- RAPID Manufacturing Institute - Department of Energy (DOE) Advanced Manufacturing Office (AMO) [DE-EE0007888-9.5]
- State of Delaware
- National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility [DE-AC02-05CH11231]
- The 2019 to 2020 Blue Waters Graduate Fellowship
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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.
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