Papers citing Machine learning for predicting thermodynamic properties of pure fluids and their mixtures
Molecular dynamics simulation and theoretical study on heat capacities of supercritical H2O/CO2 mixtures
Journal of Molecular Liquids · 2019
Xueming Yang, Yiyu Feng, Jianghao Jin, Yuanbin Liu, Bing Cao
Read original paper ↗ ·Thermodynamic models for H2O–CO2–H2 mixtures in near-critical and supercritical regions of water
International Journal of Hydrogen Energy · 2020
Yuanbin Liu, Bing Cao
Read original paper ↗ ·Molecular dynamics simulation study used in systems with supercritical water
Reviews in Chemical Engineering · 2020
Hui Jin, Weijing Ding, Bin Bai, Changqing Cao
Read original paper ↗ ·Viscosity Measurements of the H2–CO2, H2–CO2–CH4, and H2–H2O Mixtures and the H2–CO2–CH4–CO–H2O System at 280–924 K and 0.7–33.1 MPa with a Capillary Apparatus
Journal of Chemical & Engineering Data · 2020
思远 程, Fei Shang, Weigang Ma, Hui Jin, Naoya Sakoda, Xing Zhang, Liejin Guo
Read original paper ↗ ·Thermodynamic Calculations of the Critical Points of the H2–CO2–CH4–CO–H2O System
International Journal of Thermophysics · 2020
思远 程, Weigang Ma, Naoya Sakoda, Xing Zhang
Read original paper ↗ ·Machine learning approaches for structural and thermodynamic properties of a Lennard-Jones fluid
The Journal of Chemical Physics · 2020
Galen T. Craven, Nicholas Lubbers, Kipton M. Barros, Sergei Tretiak
Read original paper ↗ ·PVT Measurements of the H 2 –CO 2 –CH 4 –CO–H 2 O System at 740–939 K and 18.1–34.7 MPa with an Isochoric Apparatus and the Development of a Virial Equation of State
Journal of Chemical & Engineering Data · 2020
思远 程, Fei Shang, Weigang Ma, Hui Jin, Naoya Sakoda, Xing Zhang, Liejin Guo
Read original paper ↗ ·Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga2O3
The Journal of Chemical Physics · 2020
Yuanbin Liu, Jia‐Yue Yang, Gongming Xin, Linhua Liu, Gábor Cśanyi, Bing Cao
Read original paper ↗ ·A data-driven model for thermodynamic properties of a steam generator under cycling operation
Energy · 2020
Abhishek Navarkar, Veeraraghava Raju Hasti, Elihu Deneke, Jay P. Gore
Read original paper ↗ ·A machine learning model for predicting the minimum miscibility pressure of CO2 and crude oil system based on a support vector machine algorithm approach
Fuel · 2020
Hao Chen, Chao Zhang, Ninghong Jia, Ian Duncan, Shenglai Yang, Yongzhi Peter Yang
Read original paper ↗ ·A co-training style semi-supervised artificial neural network modeling and its application in thermal conductivity prediction of polymeric composites filled with BN sheets
Energy and AI · 2021
Yunmin Liang, Zhichun Liu, Wei Liu
Read original paper ↗ ·Predicting entropy and heat capacity of hydrocarbons using machine learning
Energy and AI · 2021
Mohammed Nasser Aldosari, Kiran K. Yalamanchi, Xin Gao, S. Mani Sarathy
Read original paper ↗ ·Machine learning approach for the prediction and optimization of thermal transport properties
Frontiers of Physics · 2021
Yulou Ouyang, Cuiqian Yu, Gang Feng Yan, Jie Chen
Read original paper ↗ ·Open Catalyst 2020 (OC20) Dataset and Community Challenges
ACS Catalysis · 2021
Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Rivière, Kevin Tran, Javier Heras‐Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon M. Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary W. Ulissi
Read original paper ↗ ·A Review of Experimental Researches on the Thermophysical Properties of Hydrogen-Containing Mixtures at High Temperatures and High Pressures
Journal of Chemical & Engineering Data · 2021
思远 程, Fengyi Li, Fei Shang, Weigang Ma, Hui Jin, Naoya Sakoda, Xing Zhang, Liejin Guo
Read original paper ↗ ·Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning
arXiv (Cornell University) · 2021
Rodolfo S. M. Freitas, Ágatha P. F. Lima, Cheng Chen, Fernando Alves Rochinha, Daniel Mira, Xi Jiang
Read original paper ↗ ·Aviation Turbine Fuel Thermal Conductivity: A Predictive Approach Using Entropy Scaling-Guided Machine Learning with Experimental Validation
ACS Omega · 2021
William A. Malatesta, Bao Yang
Read original paper ↗ ·Multiscale Graph Attention Neural Networks for Mapping Materials and Molecules beyond Short-Range Interatomic Correlations
Research Square · 2021
Yuanbin Liu, Xin Liu, Bing Cao
Read original paper ↗ ·Modeling the Solubility of Sulfur in Sour Gas Mixtures Using Improved Support Vector Machine Methods
ACS Omega · 2021
Yu-Chen Wang, Zhengshan Luo, Yiqiong Gao, Yulei Kong
Read original paper ↗ ·Multi support vector models to estimate solubility of Busulfan drug in supercritical carbon dioxide
Journal of Molecular Liquids · 2022
Zhiyu Zhao, Peng Liu, Yijie Li, Shuai Zhang, Lan Guo, Sami Mansour Ghazali, A.S. El-Shafay
Read original paper ↗ ·Predicting Diffusion Coefficients of Binary and Ternary Supercritical Water Mixtures via Machine and Transfer Learning with Deep Neural Network
Industrial & Engineering Chemistry Research · 2022
Xiao Feng Zhao, Tengfei Luo, Hui Jin
Read original paper ↗ ·MolNet‐3D: Deep Learning of Molecular Representations and Properties from 3D Topography
Advanced Theory and Simulations · 2022
Yuanbin Liu, Weixiang Hong, Bing Cao
Read original paper ↗ ·Current Trends in Fluid Research in the Era of Artificial Intelligence: A Review
Fluids · 2022
Filippos D. Sofos, Christos Stavrogiannis, Kalliopi K. Exarchou-Kouveli, Daniel Akabua, George Charilas, Theodoros E. Karakasidis
Read original paper ↗ ·The calibration for many-body dissipative particle dynamics by using back-propagation neural networks
Molecular Simulation · 2022
Xin Yu, Jiayi Zhao, Shuo Chen, Diangui Huang, Kaixuan Zhang, Damin Cao
Read original paper ↗ ·Machine learning of consistent thermodynamic models using automatic differentiation
Physical review. E · 2022
David Rosenberger, Kipton M. Barros, Timothy Clark Germann, Nicholas Lubbers
Read original paper ↗ ·Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks
The Journal of Physical Chemistry B · 2022
Joshua P. Allers, Jane Keth, Todd Michael Alam
Read original paper ↗ ·Enhancing thermal transport in multilayer structures: A molecular dynamics study on Lennard-Jones solids
Frontiers of Physics · 2022
Cuiqian Yu, Yulou Ouyang, Jie Chen
Read original paper ↗ ·Machine learning-based optimization of air-cooled heat sinks
Thermal Science and Engineering Progress · 2022
Mohammad Reza Shaeri, Soroush Sarabi, Andoniaina M. Randriambololona, Ameneh Shadlo
Read original paper ↗ ·Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization
Molecules · 2022
Mohammed A Alqarni, Nader Ibrahim Namazi, Sameer Alshehri, Ibrahim Ahmed Naguib, Amal M. Alsubaiyel, Kumar Venkatesan, Eman Mohamed Elmokadem, Mahboubeh Pishnamazi, Mohammed A. S. Abourehab
Read original paper ↗ ·Dielectric constant prediction of pure organic liquids and their mixtures with water based on interpretable machine learning
Fluid Phase Equilibria · 2022
Jiandong Deng, Guozhu Jia
Read original paper ↗ ·A molecular dynamics study of evaporation of multicomponent stationary and moving fuel droplets in multicomponent ambient gases under supercritical conditions
Energy · 2022
Yifei Gong, Xiao Ma, Kai Luo, Hongming Xu, Shijin Shuai
Read original paper ↗ ·Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models
Fuel · 2022
Rodolfo S. M. Freitas, Ágatha P. F. Lima, Cheng Chen, Fernando Alves Rochinha, Daniel Mira, Xi Jiang
Read original paper ↗ ·Predicting gas production by supercritical water gasification of coal using machine learning
Fuel · 2022
Shanke Liu, Yan Yang, Lijun Yu, Feihuan Zhu, Yu Cao, Xinyi Liu, Anqi Yao, Yaping Cao
Read original paper ↗ ·A machine learning approach for the surrogate modeling of uncertain distributed process engineering models
Process Safety and Environmental Protection · 2022
Dante Mora‐Mariano, Antonio Flores‐Tlacuahuac
Read original paper ↗ ·PREDICTING DRY GAS SEALS RELIABILITY WITH MACHINE LEARNING TECHNIQUES DEVELOPED FROM SCARCE DATA
Venue not supplied by source · 2022
MATHEUS HOFFMANN BRITO
Read original paper ↗ ·Insights into modelling and evaluation of thermodynamic and transport properties of refrigerants using machine-learning methods
Energy · 2022
Abolfazl Sajadi Noushabadi, Ebrahim Nemati Lay, Amir Dashti, Amir Hossein Mohammadi, Abdoulmohammad Gholamzadeh Chofreh, Feybi Ariani Goni, Jiří Jaromír Klemeš
Read original paper ↗ ·Application of data mining in gas injection methods
Elsevier eBooks · 2022
Fahimeh Hadavimoghaddam, Vahideh Shirvani Harandi, Masoud Mostajeran, Reza Zabihi
Read original paper ↗ ·Estimation of Breakdown Voltage of Epoxy/SiO2 Nanocomposites Insulators Using Machine Learning Algorithm
International Middle East Power Systems Conference (MEPCON) · 2022
Eslam Eid, L.S. Nasrat, A. H. Shaheen
Read original paper ↗ ·Neuro-fuzzy Approach based Prediction of Thermodynamic Properties of Steam
Venue not supplied by source · 2022
Ashwani Kharola, Rahul, Vishwjeet Choudhary, Varun Pokhriyal
Read original paper ↗ ·A Diversified End-to-End Machine Learning Approach for Thermo-Fluids’ Properties Prediction
SSRN Electronic Journal · 2023
Mahmoud Samawal, Mona Mohamed Mousa
Read original paper ↗ ·Machine Learning Applications in Chemical Kinetics and Thermochemistry
Challenges and advances in computational chemistry and physics · 2023
Lung-Yi Chen, Yi‐Pei Li
Read original paper ↗ ·SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients
Fluid Phase Equilibria · 2023
Benedikt Winter, Clemens Winter, Timm Esper, Johannes Schilling, André Bardow
Read original paper ↗ ·Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation
Energy · 2023
Shanke Liu, Yan Yang, Lijun Yu, Yu Cao, Xinyi Liu, Anqi Yao, Yaping Cao
Read original paper ↗ ·Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures
Annual Review of Chemical and Biomolecular Engineering · 2023
Fabian Jirasek, Hans Hasse
Read original paper ↗ ·Assessment of machine learning algorithms for predicting autoignition and ignition delay time in microscale supercritical water oxidation process
Fuel · 2023
Deewakar Sharma, Carole Lecoutre, Fabien Palencia, Olivier Nguyen, Arnaud Erriguible, Samuel Marre
Read original paper ↗ ·Acceleration of Numerical Simulations with Deep Learning : Application to Thermodynamic Equilibrium Calculations
Venue not supplied by source · 2023
Jingang Qu
Read original paper ↗ ·Applications of machine learning in supercritical fluids research
The Journal of Supercritical Fluids · 2023
Lucien Roach, Gian‐Marco Rignanese, Arnaud Erriguible, Cyril Aymonier
Read original paper ↗ ·Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy
Journal of Chemical Information and Modeling · 2023
José Ferraz-Caetano, Filipe Teixeira, M. Natália D. S. Cordeiro
Read original paper ↗ ·Data-driven methods for diffusivity prediction in nuclear fuels
Computational Materials Science · 2023
Galen T. Craven, Renai Chen, M. Cooper, Christopher Matthews, Jason T. Rizk, Walter Malone, Landon Johnson, Tammie R. Gibson, David A. Andersson
Read original paper ↗ ·A data-driven model to determine the infiltration characteristics of air curtains at building entrances
Physics of Fluids · 2023
Linye Song, Cong Zhang, Jing Hua, Kaijun Li, Wei Xu, Xinghui Zhang, Caohui Duan
Read original paper ↗ ·