Catalysis

World Wide Web

Extension (predicate logic)

Biology

Chromatography

Accounting

Partition coefficient

Data mining

Computer science

Business

Physical chemistry

Matrix (chemical analysis)

Thermodynamics

Physics

Organic chemistry

Collaborative filtering

Variance (accounting)

Activity coefficient

Aqueous solution

Artificial intelligence

Chemistry

Programming language

Ionic liquid

Recommender system

Artificial neural network

Biological system

UNIFAC

Dilution

Machine learning

AIChE Journal

For the ionic liquid (IL)‐solute systems of broad interest, a deep neural network based recommender system (RS) for predicting the infinite dilution activity coefficient ( γ ∞ ) is proposed and applied for a large extension of the UNIFAC model. In the RS, neural network entity embeddings are employed for mapping each IL and solute, and neural collaborative filtering is utilized to handle the nonlinearities of IL‐solute interactions. A comprehensive experimental γ ∞ database covering 215 ILs and 112 solutes (totally 41,553 data points) is established for training the RS, where the cross‐validation and test are performed based on a rigorous dataset split by IL‐solute combinations. The obtained RS shows superior performance than the state‐of‐the‐art γ ∞ prediction models and is thus taken to complete the solute‐in‐IL γ ∞ matrix. Based on the completed γ ∞ database, a large extension of the UNIFAC‐IL model is finally presented, filling all the parameters between involved groups.

Neural recommender system for the activity coefficient prediction and<scp>UNIFAC</scp>model extension of ionic<scp>liquid‐solute</scp>systems

Neural recommender system for the activity coefficient prediction andUNIFACmodel extension of ionicliquid‐solutesystems