4.5 Review

Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Software Engineering

Attribute-based Explanation of Non-Linear Embeddings of High-Dimensional Data

Jan-Tobias Sohns et al.

Summary: This paper discusses the importance of embeddings of high-dimensional data and the difficulty in explaining them. By introducing Non-Linear Embeddings Surveyor (NoLiES) and a new augmentation strategy called rangesets, users are able to quickly observe the structure and detect outliers.

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS (2022)

Article Energy & Fuels

Modelling density of pure and binary mixtures of normal alkanes: Comparison of hybrid soft computing techniques, gene expression programming, and equations of state

Aria Shahabi-Ghahfarokhy et al.

Summary: In this study, robust artificial intelligence techniques were investigated to predict the density of normal alkanes. The models developed showed great match with experimental data, with LSSVM-GWO and RBFNN-CSA identified as the most accurate models for predicting the density of pure and binary mixtures of normal alkanes. The gene expression programming correlations also showed high accuracy compared to equations of state, making them suitable for practical density estimation of hydrocarbons.

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2022)

Article Engineering, Multidisciplinary

Flow field tomography with uncertainty quantification using a Bayesian physics-informed neural network

Joseph P. Molnar et al.

Summary: We introduce a new approach to flow field tomography using the Navier-Stokes and advection-diffusion equations for regularization. Through the use of physics-informed neural networks (PINNs), we are able to leverage the governing physics to improve the accuracy of flow field reconstructions from sparse line-of-sight integrated measurements. Our results demonstrate that PINNs outperform state-of-the-art algorithms in terms of accuracy, even when used for post-processing. However, high levels of noise can lead to semi-convergence, which we address with a Bayesian PINN that allows for uncertainty quantification and reveals the source of semi-convergence.

MEASUREMENT SCIENCE AND TECHNOLOGY (2022)

Article Chemistry, Physical

Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium

Ruiyang Li et al.

Summary: This study introduces a data-free deep learning scheme, physics-informed neural network (PINN), for solving the phonon Boltzmann transport equation (BTE) with arbitrary temperature gradients. Numerical experiments suggest that the proposed PINN can accurately predict phonon transport under arbitrary temperature gradients and shows great promise for thermal design.

NPJ COMPUTATIONAL MATERIALS (2022)

Article Engineering, Chemical

Prediction of infinite-dilution activity coefficients with neural collaborative filtering

Tian Tan et al.

Summary: Accurate prediction of infinite dilution activity coefficient (gamma(infinity)) is crucial for phase equilibria and process design. This study proposes a new method based on neural collaborative filtering (NCF) to fill in the gamma(infinity) matrix, and the experimental results show that it outperforms traditional models and previous machine learning models. The completed matrix can also be used for solvent screening and parameter extension.

AICHE JOURNAL (2022)

Article Engineering, Chemical

Prediction of Henry's law constants by matrix completion

Nicolas Hayer et al.

Summary: This study introduces a new machine learning approach called matrix completion methods (MCMs) for predicting Henry's law constants H-ij. The predictive performance of MCMs is similar to that of the Predictive Soave-Redlich-Kwong equation-of-state (PSRK-EoS), but with wider applicability. Additionally, a hybrid method combining MCMs and PSRK-EoS in a Bayesian framework achieves unprecedented performance in predicting H-ij.

AICHE JOURNAL (2022)

Article Astronomy & Astrophysics

Physics-informed neural networks for gravity field modeling of the Earth and Moon

John Martin et al.

Summary: This paper investigates the use of physics-informed neural networks (PINNs) to model the gravitational potential of the Earth and Moon, and finds that this method offers advantages in model compactness and computational efficiency compared to traditional analytic models.

CELESTIAL MECHANICS & DYNAMICAL ASTRONOMY (2022)

Article Thermodynamics

AI-PCSAFT approach: New high predictive method for estimating PC-SAFT pure component properties and phase equilibria parameters

A. Abdallah el Hadj et al.

Summary: In this study, a new approach combining Artificial Intelligence (AI) and PC-SAFT equation of state is proposed for estimating the solubility of solid drugs in supercritical carbon dioxide. The approach involves the optimization of a direct artificial neural network (ANN) for predicting phase equilibria, an ANN inverse for estimating pure components and physical properties, and the enhancement of the PC-SAFT equation of state. The new method is successfully applied to predict the solubility of 213 solid solutes in supercritical carbon dioxide with high accuracy.

FLUID PHASE EQUILIBRIA (2022)

Article Thermodynamics

Prediction of CO2 solubility in potential blends of ionic liquids with Alkanolamines using statistical non-rigorous and ANN based modeling: A comprehensive simulation study for post combustion CO2 capture

Anirban Dey et al.

Summary: Statistical non-rigorous models are developed to predict CO2 solubility in different aqueous blends, which can estimate the liquid phase CO2 loading in different solvent formulations. The proposed models show good agreement with experimental data on CO2 solubility.

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER (2022)

Article Chemistry, Physical

A matrix completion algorithm for efficient calculation of quantum and variational effects in chemical reactions

Selin Bac et al.

Summary: This work examines the viability of matrix completion methods as cost-effective alternatives to full nuclear Hessians for calculating quantum and variational effects in chemical reactions. The study finds that the harmonic variety-based matrix completion algorithm demonstrates robustness in chemical reactions and accurately recovers key observables.

JOURNAL OF CHEMICAL PHYSICS (2022)

Article Computer Science, Interdisciplinary Applications

Physics constrained learning for data-driven inverse modeling from sparse observations

Kailai Xu et al.

Summary: This article presents a new approach that trains deep neural networks (DNNs) while numerically satisfying partial differential equation (PDE) constraints. The algorithm developed allows differentiation of both explicit and implicit numerical solvers in reverse-mode automatic differentiation. The approach demonstrates faster convergence and better stability in relatively stiff problems compared to the penalty method.

JOURNAL OF COMPUTATIONAL PHYSICS (2022)

Article Multidisciplinary Sciences

Modeling solubility of CO2-N2 gas mixtures in aqueous electrolyte systems using artificial intelligence techniques and equations of state

Reza Nakhaei-Kohani et al.

Summary: This study predicts the solubility of CO2-N-2 mixtures in water and brine solutions using six intelligent models and compares the results with four equations of state. The Random Forest model provides the best predictions, while the PC-SAFT model performs well in predicting the solubility of CO2, and the VPT EOS is the best for N-2 solubility. Sensitivity analysis shows that various factors affect the solubility of the gas mixture. The Leverage method confirms the reliability of the Random Forest approach for determining solubility.

SCIENTIFIC REPORTS (2022)

Article Engineering, Chemical

A neural recommender system for efficient adsorbent screening

Xiang Zhang et al.

Summary: A data-driven neural recommender system is developed for preliminary adsorbent screening by constructing a sparse matrix and applying the neural collaborative filtering method. The recommender system can impute the missing adsorption uptake data in the matrix and identify promising adsorbents for gas separation and storage.

CHEMICAL ENGINEERING SCIENCE (2022)

Article Chemistry, Multidisciplinary

Database for liquid phase diffusion coefficients at infinite dilution at 298 K and matrix completion methods for their prediction

Oliver Grossmann et al.

Summary: This study systematically consolidated experimental data on diffusion in binary liquid mixtures and developed new matrix completion methods for predicting diffusion coefficients. The hybrid MCMs based on SEGWE information outperformed other methods in predictive accuracy.

DIGITAL DISCOVERY (2022)

Article Chemistry, Multidisciplinary

Graph neural networks for the prediction of infinite dilution activity coefficients

Edgar Ivan Sanchez Medina et al.

Summary: In this study, graph neural networks (GNNs) were used to predict infinite dilution activity coefficients γij of organic systems. The proposed method showed competitive performance compared to traditional phenomenological/mechanistic methods, and improved prediction accuracy.

DIGITAL DISCOVERY (2022)

Article Chemistry, Multidisciplinary

Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions

Richard S. Graham et al.

Summary: Accurate potential energy surfaces (PES) are required for predicting thermophysical properties from molecular principles. This study presents a widely-applicable method that produces first-principles PES using Gaussian Processes (GP) as a machine learning technique. The method accurately interpolates three-body non-additive interaction data and does not require modification for different molecules. It produces highly accurate interpolation from fewer training points and enables more accurate ab initio calculations. The method is exemplified by computing the PES for CO2-Ar mixtures, which allows for accurate first-principles predictions of various thermophysical properties.

CHEMICAL COMMUNICATIONS (2022)

Article Chemistry, Multidisciplinary

Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions

Fabian Jirasek et al.

Summary: This study combines machine learning and classical thermodynamic models to predict the thermodynamic properties of mixtures. By embedding machine learning methods into classical models, the predictive accuracy is significantly improved, and a complete set of parameters for all binary systems is obtained.

CHEMICAL SCIENCE (2022)

Review Physics, Fluids & Plasmas

Current Trends in Fluid Research in the Era of Artificial Intelligence: A Review

Filippos Sofos et al.

Summary: Computational methods in fluid research have progressed due to the incorporation of large amounts of data, and artificial intelligence and machine learning have played a significant role in fluid research by extracting information from data and converting it into knowledge. Non-linear, decision tree-based methods have shown remarkable performance in reproducing fluid properties.

FLUIDS (2022)

Article Engineering, Chemical

Neural recommender system for the activity coefficient prediction and UNIFAC model extension of ionic liquid-solute systems

Guzhong Chen et al.

Summary: A deep neural network based recommender system is proposed for predicting the infinite dilution activity coefficient in IL-solute systems, showing superior performance compared to existing models after being trained on a comprehensive experimental database.

AICHE JOURNAL (2021)

Article Chemistry, Physical

Prediction of the ideal-gas thermodynamic properties for water

Chao-Wen Wang et al.

Summary: The study introduces suitable analytical representations for the ideal-gas Gibbs free energy and entropy of water, which accurately predict values in agreement with experimental data. This novel approach provides a pathway for treating the anharmonic vibrations of water molecules.

JOURNAL OF MOLECULAR LIQUIDS (2021)

Article Thermodynamics

Machine Learning of Thermophysical Properties

Fabian Jirasek et al.

Summary: This article discusses the role of machine learning in research on thermophysical properties, providing an overview of developments and new directions in the field. By linking the perspectives of chemical engineers and computer scientists, the discussion highlights the importance of merging physical modeling with machine learning to create hybrid approaches for future advancements.

FLUID PHASE EQUILIBRIA (2021)

Article Engineering, Chemical

Predicting Activity Coefficients at Infinite Dilution for Varying Temperatures by Matrix Completion

Julie Damay et al.

Summary: Activity coefficients can describe the nonideality of liquid mixtures, with infinite dilution activity coefficients being particularly important in binary mixtures; organizing experimental data in a matrix and filling data gaps is crucial; new matrix completion methods show better performance in predicting activity coefficients at infinite dilution compared to current physical prediction methods.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2021)

Article Thermodynamics

Thermal performance of hybrid fly ash and copper nanofluid in various mixture ratios: Experimental investigation and application of a modern ensemble machine learning approach

Praveen Kanti et al.

Summary: The research aims to investigate the properties of copper and fly ash-copper nanoparticles suspended in water, with the highest thermal conductivity and viscosity values obtained at a mixture ratio of 20:80 for HNF. The study shows that HNF with a concentration of 1.0 vol% enhances heat transfer for all mixture ratios. Additionally, the Boosted Regression Tree model outperforms classical regression in predicting the thermo-physical properties of HNF.

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER (2021)

Article Computer Science, Artificial Intelligence

A Comprehensive Survey on Graph Neural Networks

Zonghan Wu et al.

Summary: This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. It discusses the taxonomy of GNNs, their applications, and summarizes open-source codes, benchmark data sets, and model evaluation. The article also proposes potential research directions in this rapidly growing field.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Chemistry, Physical

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

Joshua P. Allers et al.

Summary: The ability to predict transport properties of liquids using machine learning methods shows promising results in accurately forecasting diffusion properties of pure liquids. Artificial Neural Networks have been effectively used in this study to model diffusion of pure liquids, aiding in the design of materials and processes for various applications.

JOURNAL OF PHYSICAL CHEMISTRY B (2021)

Review Chemistry, Multidisciplinary

Machine Learning Force Fields

Oliver T. Unke et al.

Summary: The use of machine learning in computational chemistry has led to significant advancements, particularly in the development of machine learning-based force fields to bridge the gap between accuracy and efficiency. The key concept is to learn the statistical relations between chemical structure and potential energy, without preconceived notions of fixed bonds. Challenges remain for the next generation of machine learning force fields.

CHEMICAL REVIEWS (2021)

Review Thermodynamics

Application of Artificial Intelligence-based predictive methods in Ionic liquid studies: A review

Falola Yusuf et al.

Summary: Comprehensive experimental investigation and accurate predictive models are crucial for understanding the dynamics in Ionic liquid (IL) properties. Machine learning models, including conventional ones like Artificial Neural Networks and hybrid ones like random forest and gradient boosting, can be used to predict the thermo-physical properties of ILs. The study found that system parameters and critical properties play a key role in depicting the phase behavior of ILs.

FLUID PHASE EQUILIBRIA (2021)

Review Chemistry, Applied

Data Science in Chemical Engineering: Applications to Molecular Science

Chowdhury Ashraf et al.

Summary: Chemical engineering is undergoing rapid transformation with the tools of data science, particularly with the growing influence of artificial intelligence applications. Molecular data science, which focuses on molecular discovery and property optimization at the atomic scale, plays a key role in this evolution.

ANNUAL REVIEW OF CHEMICAL AND BIOMOLECULAR ENGINEERING, VOL 12, 2021 (2021)

Article Green & Sustainable Science & Technology

Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems

George Truc et al.

Summary: This study evaluates the equations of state for predicting thermodynamic properties, comparing Peng-Robinson and Soave-Redlich-Kwong, and utilizing machine learning methods such as artificial neural networks. The combination of machine learning with equations of state leads to lower predictive errors and provides valuable insight for dealing with engineering problems.

SUSTAINABILITY (2021)

Article Chemistry, Physical

Representation of Vapor-Liquid Equilibria Properties for Binary Mixtures Containing R1234ze(E) using Machine Learning Models

Biao Li et al.

Summary: This study investigates the applicability of four machine learning models for representing VLE in binary mixtures containing R1234ze(E), with SVR being the most accurate model. Compared to thermodynamic models, the SVR model provides more accurate descriptions of experimental pressure and vapor-phase mole fraction.

JOURNAL OF PHASE EQUILIBRIA AND DIFFUSION (2021)

Article Chemistry, Multidisciplinary

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

Prathana Nimmanterdwong et al.

Summary: The study successfully predicted the impact of various absorbents on the solubility of hydrogen sulfide using artificial neural networks, and found that the number of hidden neurons and prediction algorithms have an effect on the prediction results. The experimental results showed that the LM-ANN model performed best in terms of prediction performance.

ACS OMEGA (2021)

Review Chemistry, Multidisciplinary

Machine Learning for Chemical Reactions

Markus Meuwly

Summary: Machine learning techniques have a long history in the field of chemical reactions, being able to address complex problems involving both computation and experiments. These techniques can develop models consistent with experimental knowledge, handle problems intractable to conventional approaches, and simulate reactive networks in combustion.

CHEMICAL REVIEWS (2021)

Article Mathematics, Applied

PHYSICS-INFORMED NEURAL NETWORKS WITH HARD CONSTRAINTS FOR INVERSE DESIGN\ast

Lu Lu et al.

Summary: Inverse design, such as topology optimization, is widely used in engineering for achieving targeted properties by optimizing designed geometries. The proposed physics-informed neural networks with hard constraints (hPINNs) can effectively solve topology optimization problems without the need for a large dataset, demonstrating smoother design outcomes compared to conventional methods.

SIAM JOURNAL ON SCIENTIFIC COMPUTING (2021)

Review Computer Science, Artificial Intelligence

Traceability for Trustworthy AI: A Review of Models and Tools

Marcal Mora-Cantallops et al.

Summary: Traceability is considered a key requirement for trustworthy artificial intelligence, involving the need to maintain a complete account of the provenance of data, processes, and artifacts. However, a common approach and shared semantics are currently lacking in AI traceability tools, with some tools either not fully mature or falling into obsolescence, compromising research reproducibility.

BIG DATA AND COGNITIVE COMPUTING (2021)

Review Chemistry, Multidisciplinary

A review on machine learning algorithms for the ionic liquid chemical space

Spyridon Koutsoukos et al.

Summary: Ionic liquids have diverse properties and applications, with machine learning algorithms playing a key role in predicting these properties. Continuously optimizing training datasets and models can enhance the accuracy and efficiency of predictions.

CHEMICAL SCIENCE (2021)

Article Chemistry, Multidisciplinary

Prediction of Henry's law constants of CO2 in imidazole ionic liquids using machine learning methods based on empirical descriptors

Ting Wu et al.

Summary: In this study, 160 experimental data points of Henry's law constant of CO2 in 32 imidazole ionic liquids were collected, and intuitive and explanatory descriptors related to HLC were suggested based on the 2D structural features of the ILs. The significant effect of temperature on HLC was also highlighted, and three machine learning methods were used to construct models for fast prediction of HLC. The results showed that using Multi-layer Perceptron to build the model was satisfactory compared to Random Forest and Multiple Linear Regression methods.

CHEMICAL PAPERS (2021)

Article Engineering, Chemical

QSPR study of the Henry's law constant for heterogeneous compounds

Pablo R. Duchowicz et al.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2020)

Review Computer Science, Hardware & Architecture

Techniques for Interpretable Machine Learning

Mengnan Du et al.

COMMUNICATIONS OF THE ACM (2020)

Article Chemistry, Physical

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion

Fabian Jirasek et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

Article Green & Sustainable Science & Technology

Precise prediction of biogas thermodynamic properties by using ANN algorithm

Mahmood Farzaneh-Gord et al.

RENEWABLE ENERGY (2020)

Article Chemistry, Medicinal

Learning Coarse-Grained Potentials for Binary Fluids

Peiyuan Gao et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

Review Chemistry, Multidisciplinary

The Role of Machine Learning in the Understanding and Design of Materials

Seyed Mohamad Moosavi et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2020)

Article Multidisciplinary Sciences

Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

Samuel Boobier et al.

NATURE COMMUNICATIONS (2020)

Article Chemistry, Multidisciplinary

Hybridizing physical and data-driven prediction methods for physicochemical properties

Fabian Jirasek et al.

CHEMICAL COMMUNICATIONS (2020)

Review Chemistry, Multidisciplinary

Machine learning the ropes: principles, applications and directions in synthetic chemistry

Felix Strieth-Kalthoff et al.

CHEMICAL SOCIETY REVIEWS (2020)

Review Chemistry, Multidisciplinary

QSAR without borders

Eugene N. Muratov et al.

CHEMICAL SOCIETY REVIEWS (2020)

Article Computer Science, Interdisciplinary Applications

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

M. Raissi et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2019)

Review Chemistry, Medicinal

Deep Learning in Chemistry

Adam C. Mater et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Review Computer Science, Information Systems

Machine Learning Interpretability: A Survey on Methods and Metrics

Diogo Carvalho et al.

ELECTRONICS (2019)

Article Chemistry, Multidisciplinary

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Volker L. Deringer et al.

ADVANCED MATERIALS (2019)

Review Chemistry, Physical

Recent advances and applications of machine learning in solid-state materials science

Jonathan Schmidt et al.

NPJ COMPUTATIONAL MATERIALS (2019)

Article Chemistry, Physical

Predicting the volumetric properties of pure and mixture of amino acid-based ionic liquids

M. Taghizadehfard et al.

JOURNAL OF MOLECULAR LIQUIDS (2019)

Article Engineering, Chemical

The promise of artificial intelligence in chemical engineering: Is it here, finally?

Venkat Venkatasubramanian

AICHE JOURNAL (2019)

Editorial Material Robotics

XAI-Explainable artificial intelligence

David Gunning et al.

SCIENCE ROBOTICS (2019)

Review Materials Science, Multidisciplinary

Machine learning in materials science

Jing Wei et al.

INFOMAT (2019)

Editorial Material Biochemistry & Molecular Biology

The FAIR guiding principles for data stewardship: fair enough?

Martin Boeckhout et al.

EUROPEAN JOURNAL OF HUMAN GENETICS (2018)

Article Chemistry, Multidisciplinary

Toward an intelligent approach for predicting surface tension of binary mixtures containing ionic liquids

Reza Soleimani et al.

KOREAN JOURNAL OF CHEMICAL ENGINEERING (2018)

Article Computer Science, Artificial Intelligence

A comparative evaluation of outlier detection algorithms: Experiments and analyses

Remi Domingues et al.

PATTERN RECOGNITION (2018)

Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

Article Multidisciplinary Sciences

Deep reinforcement learning for de novo drug design

Mariya Popova et al.

SCIENCE ADVANCES (2018)

Article Engineering, Mechanical

Precise calculation of natural gas sound speed using neural networks: An application in flow meter calibration

Mahmood Farzaneh-Gord et al.

FLOW MEASUREMENT AND INSTRUMENTATION (2018)

Review Chemistry, Multidisciplinary

Deep learning for computational chemistry

Garrett B. Goh et al.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2017)

Review Statistics & Probability

Variational Inference: A Review for Statisticians

David M. Blei et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2017)

Article Thermodynamics

Further Development of Modified UNIFAC (Dortmund): Revision and Extension 6

Dana Constantinescu et al.

JOURNAL OF CHEMICAL AND ENGINEERING DATA (2016)

Article Biochemistry & Molecular Biology

Molecular graph convolutions: moving beyond fingerprints

Steven Kearnes et al.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2016)

Article Multidisciplinary Sciences

Comment: The FAIR Guiding Principles for scientific data management and stewardship

Mark D. Wilkinson et al.

SCIENTIFIC DATA (2016)

Article Automation & Control Systems

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Saeed Yousefinejad et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2015)

Review Multidisciplinary Sciences

Advances in natural language processing

Julia Hirschberg et al.

SCIENCE (2015)

Review Multidisciplinary Sciences

Machine learning: Trends, perspectives, and prospects

M. I. Jordan et al.

SCIENCE (2015)

Article Chemistry, Multidisciplinary

InChI, the IUPAC International Chemical Identifier

Stephen R. Heller et al.

JOURNAL OF CHEMINFORMATICS (2015)

Article Thermodynamics

Generalized binary interaction parameters for the Peng-Robinson equation of state

Agelia M. Abudour et al.

FLUID PHASE EQUILIBRIA (2014)

Article Chemistry, Multidisciplinary

Quantitative Interpretation of Diffusion-Ordered NMR Spectra: Can We Rationalize Small Molecule Diffusion Coefficients?

Robert Evans et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2013)

Review Chemistry, Multidisciplinary

Quantitative Structure-Property Relationship Modeling of Diverse Materials Properties

Tu Le et al.

CHEMICAL REVIEWS (2012)

Article Energy & Fuels

Prediction of crude oil viscosity curve using artificial intelligence techniques

M. A. Al-Marhoun et al.

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2012)

Article Chemistry, Physical

Thermodynamic prediction of vapor-liquid equilibrium of supercritical CO2 or CHF3 + ionic liquids

Victor H. Alvarez et al.

JOURNAL OF SUPERCRITICAL FLUIDS (2012)

Review Chemistry, Physical

Potential Energy Surfaces Fitted by Artificial Neural Networks

Chris M. Handley et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2010)

Review Chemistry, Applied

COSMO-RS: An Alternative to Simulation for Calculating Thermodynamic Properties of Liquid Mixtures

Andreas Klamt et al.

ANNUAL REVIEW OF CHEMICAL AND BIOMOLECULAR ENGINEERING, VOL 1 (2010)

Article Computer Science, Theory & Methods

Exact Matrix Completion via Convex Optimization

Emmanuel J. Candes et al.

FOUNDATIONS OF COMPUTATIONAL MATHEMATICS (2009)

Article Engineering, Chemical

Group contribution prediction of surface charge density profiles for COSMO-RS(OI)

Tiancheng Mu et al.

AICHE JOURNAL (2007)

Article Engineering, Chemical

Sigma-profile database for using COSMO-based thermodynamic methods

Eric Mullins et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2006)

Article Energy & Fuels

Performance comparison of CFCs with their substitutes using artificial neural network

E Arcaklioglu

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2004)

Review Environmental Sciences

Quantitative structure-property relationships for prediction of boiling point, vapor pressure, and melting point

JC Dearden

ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY (2003)

Review Biochemical Research Methods

Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening

L Xue et al.

COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING (2000)