Modeling Nanoconfinement Effects Using Active Learning

Santos, Javier; Mehana, Mohamed Zakaria Saad; Hao, Wu; Prodanovic, Masa; Pyrcz, Michael; Kang, Qinjun; Viswanathan, Hari S.

doi:10.1021/acs.jpcc.0c07427

Title: Modeling Nanoconfinement Effects Using Active Learning

Journal Article · Fri Sep 18 00:00:00 EDT 2020 · Journal of Physical Chemistry. C

DOI:https://doi.org/10.1021/acs.jpcc.0c07427· OSTI ID:1835763

Santos, Javier ^[1];

^[2]; Hao, Wu ^[3]; Prodanovic, Masa ^[1]; Pyrcz, Michael ^[1];

^[2];

^[2]

Univ. of Texas, Austin, TX (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

Predicting the spatial configuration of gas in nanopores of is relevant in applications such as fluid flow forecasting and hydrocarbon reserves estimation. For example, shale reservoirs have suffered from computationally intractable multiscale problems, since fluid properties such as viscosity, density, and adsorption must be calculated by using expensive molecular dynamics (MD) simulations within each nanopore, whereas flow through these connected nanopores must be simulated at the micrometer scale. We utilize machine learning techniques to quickly and accurately model nanoscale confinement effects as an important step toward bridging the nano and micro scales. Our workflow is based on building and training physics-based deep-neural-networks models by learning from a database of MD calculations. The model accounts for the adsorption phenomenon by predicting the statistical distribution of gas inside nanopores. Because large databases of MD calculations are expensive to create, we investigate active learning (AL) as a data set construction strategy. In this workflow, new data are selected based on the model uncertainty via the query-by-committee approach. We show that our workflow obtains accurate models that generalize to real scanning electron microscopy geometries with 1/10th of the number of MD calculations required vs random data set generation. Our method enables the possibility of modeling nanoconfinement effects at the mesoscale, where complex connected sets of nanopores affect flow.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1835763

Report Number(s):: LA-UR-20-22427

Journal Information:: Journal of Physical Chemistry. C, Vol. 124, Issue 40; ISSN 1932-7447

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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