Combining machine learning with physical knowledge in thermodynamic modeling of fluid mixtures
Thermophysical properties of fluid mixtures are important in many fields of science and
engineering. However, experimental data are scarce in this field, so prediction methods are …
engineering. However, experimental data are scarce in this field, so prediction methods are …
Alleviating monoterpene toxicity using a two‐phase extractive fermentation for the bioproduction of jet fuel mixtures in Saccharomyces cerevisiae
TCR Brennan, CD Turner, JO Krömer… - Biotechnology and …, 2012 - Wiley Online Library
Monoterpenes are a diverse class of compounds with applications as flavors and
fragrances, pharmaceuticals and more recently, jet fuels. Engineering biosynthetic pathways …
fragrances, pharmaceuticals and more recently, jet fuels. Engineering biosynthetic pathways …
Machine learning in thermodynamics: Prediction of activity coefficients by matrix completion
Activity coefficients, which are a measure of the nonideality of liquid mixtures, are a key
property in chemical engineering with relevance to modeling chemical and phase equilibria …
property in chemical engineering with relevance to modeling chemical and phase equilibria …
A comprehensive dataset on cytotoxicity of ionic liquids
LA Arakelyan, DM Arkhipova, MM Seitkalieva… - Scientific Data, 2024 - nature.com
Ionic liquids (ILs) are structurally tunable salts with applications ranging from chemical
synthesis to batteries, novel materials and medicine. Despite their potential, the toxicity of ILs …
synthesis to batteries, novel materials and medicine. Despite their potential, the toxicity of ILs …
Predicting activity coefficients at infinite dilution for varying temperatures by matrix completion
Activity coefficients describe the nonideality of liquid mixtures and are essential for
calculating equilibria. The activity coefficients at infinite dilution in binary mixtures are …
calculating equilibria. The activity coefficients at infinite dilution in binary mixtures are …
Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions
Predictive models of thermodynamic properties of mixtures are paramount in chemical
engineering and chemistry. Classical thermodynamic models are successful in generalizing …
engineering and chemistry. Classical thermodynamic models are successful in generalizing …
An Interpretable Solute–Solvent Interactive Attention Module Intensified Graph-Learning Architecture toward Enhancing the Prediction Accuracy of an Infinite Dilution …
D Wu, Z Zhu, J Zhang, H Wen, S Jin… - Industrial & Engineering …, 2024 - ACS Publications
The infinite dilution activity coefficient (γ∞) is a significant thermodynamic property for phase
equilibrium prediction. Herein, a solute–solvent interactive attention module is proposed to …
equilibrium prediction. Herein, a solute–solvent interactive attention module is proposed to …
Hybridizing physical and data-driven prediction methods for physicochemical properties
We present a generic way to hybridize physical and data-driven methods for predicting
physicochemical properties. The approach 'distills' the physical method's predictions into a …
physicochemical properties. The approach 'distills' the physical method's predictions into a …
KnowTD─ An Actionable Knowledge Representation System for Thermodynamics
We demonstrate that thermodynamic knowledge acquired by humans can be transferred to
computers so that the machine can use it to solve thermodynamic problems and produce …
computers so that the machine can use it to solve thermodynamic problems and produce …
Prediction of parameters of group contribution models of mixtures by matrix completion
Group contribution (GC) methods are widely used for predicting the thermodynamic
properties of mixtures by dividing components into structural groups. These structural groups …
properties of mixtures by dividing components into structural groups. These structural groups …