ArticleScientific reports2020
Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 47 citations in OpenAlex.
- Colloid Transport in Bicontinuous Nanoporous Media.Langmuir : the ACS journal of surfaces and colloids · 2024Article
- Recent Trends in Polymeric Foams and Porous Structures for Electromagnetic Interference Shielding Applications.Polymers · 2024Review
- Predictive scale-bridging simulations through active learning.Scientific reports · 2023Article
- A Dataset of 3D Structural and Simulated Transport Properties of Complex Porous Media.Scientific data · 2022Article
- Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art.International journal of molecular sciences · 2022Review
- Multiscale Modeling of Broadband Perfect Absorbers Based on Gold Metallic Molecules.ACS omega · 2022Article
- Nanoscale slip length prediction with machine learning tools.Scientific reports · 2021Article
- A review on slip boundary conditions at the nanoscale: recent development and applications.Beilstein journal of nanotechnology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 2 countries.
Funding
Abstract
Fine-scale models that represent first-principles physics are challenging to represent at larger scales of interest in many application areas. In nanoporous media such as tight-shale formations, where the typical pore size is less than 50 nm, confinement effects play a significant role in how fluids behave. At these scales, fluids are under confinement, affecting key properties such as density, viscosity, adsorption, etc. Pore-scale Lattice Boltzmann Methods (LBM) can simulate flow in complex pore structures relevant to predicting hydrocarbon production, but must be corrected to account for confinement effects. Molecular dynamics (MD) can model confinement effects but is computationally expensive in comparison. The hurdle to bridging MD with LBM is the computational expense of MD simulations needed to perform this correction. Here, we build a Machine Learning (ML) surrogate model that captures adsorption effects across a wide range of parameter space and bridges the MD and LBM scales using a relatively small number of MD calculations. The model computes upscaled adsorption parameters across varying density, temperature, and pore width. The ML model is 7 orders of magnitude faster than brute force MD. This workflow is agnostic to the physical system and could be generalized to further scale-bridging applications.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.