Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

We present the first hard-constraint neural network model for predicting activity coefficients
(HANNA), a thermodynamic mixture property that is the basis for many applications in …

Graph-structured data are pervasive in the real-world such as social networks, molecular
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …

The accuracy of thermochemical prediction methods is strongly dependent on the size of the
set of training data. Group additivity is an interpretable modeling strategy that can be …

Data-driven materials science has realized a new paradigm by integrating materials domain
knowledge and machine-learning (ML) techniques. However, ML-based research has often …

The ability to evaluate pure compound properties of various molecular species is an
important prerequisite for process simulation in general and in particular for computer-aided …

Rapid and accurate prediction of basic physicochemical parameters of molecules will
greatly accelerate the target-orientated design of novel reactions and materials but has been …

Accurate vapor pressure prediction is crucial for various applications, but obtaining precise
measurements for certain compounds is resource-and labor-intensive. This challenge is …

In the present work, a multi-angle approach is adopted to develop two ML-QSPR models for
the prediction of the enthalpy of formation and the entropy of molecules, in their ideal gas …

This article investigates the applicability domain (AD) of machine learning (ML) models
trained on high-dimensional data, for the prediction of the ideal gas enthalpy of formation …