About: Predicting Diffusion Coefficients of Binary and Ternary Supercritical Water Mixtures via Machine and Transfer Learning with Deep Neural Network

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Predicting Diffusion Coefficients of Binary and Ternary Supercritical Water Mixtures via Machine and Transfer Learning with Deep Neural Network Goto Sponge NotDistinct Permalink

An Entity of Type : wikibase:Item, within Data Space : wikidata.demo.openlinksw.com associated with source document(s)

scientific article published on 25 February 2022

Attributes	Values
rdf:type	Item
description	article científic (ca) articol științific (ro) articolo scientifico (it) artigo científico (gl) artigo científico (pt) artigo científico (pt-br) artikull shkencor (sq) artículo científico (es) artículu científicu (ast) bilimsel makale (tr) scienca artikolo (eo) vedecký článok (sk) vetenskaplig artikel (sv) vitenskapelig artikkel (nb) vitskapeleg artikkel (nn) vědecký článek (cs) wetenschappelijk artikel (nl) wissenschaftlicher Artikel (de) научная статья (ru) научни чланак (sr) מאמר מדעי (he) artikel ilmiah (id) мақолаи илмӣ (tg-cyrl) article scientifique (fr) مقالة علمية (ar) বৈজ্ঞানিক নিবন্ধ (bn) maqolai ilmiy (tg-latn) наукова стаття, опублікована в лютому 2022 (uk) scientific article published on 25 February 2022 (en)
publication date	wds:Q111521258-74C88F3E-B755-401D-BF95-35BBBBD31BCD
publication date	2022-02-25 00:00:00Z (xsd:dateTime)
cites work	wds:Q111521258-137E0450-9C0C-4930-A0B8-200248A234F1 wds:Q111521258-1A79FD45-BCD3-4FBB-9CDD-8B7A92C2483E wds:Q111521258-2C4B25F2-5C41-4D0D-89B7-E65D5312AFAA wds:Q111521258-3AE288ED-2CD0-4F6C-AE03-0918420A3E0A wds:Q111521258-487481F1-1468-4FA6-8C9B-6E871C44DD18 wds:Q111521258-52DBCAEB-9957-4EFA-9933-D36E1B631D9C wds:Q111521258-555106D8-5298-49BD-A1AF-4FD2E785F659 wds:Q111521258-56926453-E8EB-49E3-B082-D5D9698350F3 wds:Q111521258-5C932C44-6A74-4277-B960-434762DFD724 wds:Q111521258-5DAB594A-3EDF-4661-B9F8-6CC09BF494EA wds:Q111521258-67624EB5-1219-44A9-9CBE-36B3AE2119AE wds:Q111521258-710E479B-CBF1-4308-80CC-E7E2C5CC1D75 wds:Q111521258-7F3BA47B-54A9-4E56-A45E-33E3B183DADE wds:Q111521258-871FC9EE-6CDA-4648-953F-C31F953DED91 wds:Q111521258-89DC192F-00BC-4B47-98E5-E2EAB9030645 wds:Q111521258-9AD96CCF-4A88-460C-BFFA-9E85D5FE0689 wds:Q111521258-BAF4E2AA-27FA-48A9-A349-5AFE7F3A8809 wds:Q111521258-C27899EF-B663-489F-A52C-CDB0B83A4FD1 wds:Q111521258-D81EC6D1-9ADA-498E-8992-52AA0D7EBDF7 wds:Q111521258-DEFAA252-B87E-4072-9BDC-29053F3D626A wds:Q111521258-E8B3FD9D-0ED2-445E-9FE2-75A137C052C1 wds:Q111521258-FFFDB833-4AB1-400F-A346-1FEFEBD7D6FD
cites work	Self-strengthening biphasic nanoparticle assemblies with intrinsic catch bonds PACKMOL: a package for building initial configurations for molecular dynamics simulations. System-Size Dependence of Diffusion Coefficients and Viscosities from Molecular Dynamics Simulations with Periodic Boundary Conditions A Survey on Transfer Learning Supercritical water oxidation: A technical review Fick Diffusion Coefficients in Ternary Liquid Systems from Equilibrium Molecular Dynamics Simulations The Darken Relation for Multicomponent Diffusion in Liquid Mixtures of Linear Alkanes: An Investigation Using Molecular Dynamics (MD) Simulations Atomically Detailed Modeling of Metal Organic Frameworks for Adsorption, Diffusion, and Separation of Noble Gas Mixtures Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning Predicting Materials Properties with Little Data Using Shotgun Transfer Learning Machine Learning-Based Upscaling of Finite-Size Molecular Dynamics Diffusion Simulations for Binary Fluids Atomistic molecular dynamics simulations of CO₂ diffusivity in H₂O for a wide range of temperatures and pressures Understanding the Role of Molecular Diffusion and Catalytic Selectivity in Liquid-Phase Beckmann Rearrangement A Transfer Learning Study of Gas Adsorption in Metal-Organic Frameworks Machine Learning Interatomic Potentials as Emerging Tools for Materials Science Catalytic gasification of phenol in supercritical water over bimetallic Co-Ni/AC catalyst. Thermodynamic properties and diffusion of water + methane binary mixtures Machine learning prediction of self-diffusion in Lennard-Jones fluids Leverage electron properties to predict phonon properties via transfer learning for semiconductors Ternary diffusivities by model-based analysis of Raman spectroscopy measurements Diffusion Coefficients from Molecular Dynamics Simulations in Binary and Ternary Mixtures Predicting Thermodynamic Properties of Alkanes by High-Throughput Force Field Simulation and Machine Learning
author name string	wds:Q111521258-8785AF51-B472-4228-8812-E99BFA683703
author name string	Xiao Zhao
rdfs:label	Predicting Diffusion Coefficients of Binary and Ternary Supercritical Water Mixtures via Machine and Transfer Learning with Deep Neural Network (en)
skos:prefLabel	Predicting Diffusion Coefficients of Binary and Ternary Supercritical Water Mixtures via Machine and Transfer Learning with Deep Neural Network (en)
name	Predicting Diffusion Coefficients of Binary and Ternary Supercritical Water Mixtures via Machine and Transfer Learning with Deep Neural Network (en)
author	wds:Q111521258-E32856CF-521F-4115-9240-EC4023FEF23A wds:Q111521258-E4424C66-1157-49B4-BDA6-24534A389C4D
author	Tengfei Luo Hui Jin
title	wds:Q111521258-13B0091E-863C-4FB4-A9A5-9051136E05F7
title	Predicting Diffusion Coefficients of Binary and Ternary Supercritical Water Mixtures via Machine and Transfer Learning with Deep Neural Network (en)
instance of	wds:Q111521258-61379678-5E75-4D51-9DC1-87207573F535
instance of	scholarly article
main subject	wds:Q111521258-706E324C-896D-4235-991F-32A478A84C38 wds:Q111521258-95F49A18-2AE3-48A6-899B-92C1E0C2F7ED
main subject	transfer learning deep neural network
published in	wds:Q111521258-A454E14A-7F8F-45DF-A6B3-E0A5DF31D242
published in	Industrial & Engineering Chemistry Research
DOI	wds:Q111521258-C744A503-BEC1-48F2-A293-084C24F73665
DOI	http://dx.doi.org/10.1021/ACS.IECR.2C00017
DOI	10.1021/ACS.IECR.2C00017
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q111521258

Faceted Search & Find service v1.16.117 as of May 05 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3239 as of May 5 2024, on Linux (x86_64-centos_6-linux-gnu), Single-Server Edition (378 GB total memory, 190 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software