Data-driven multi-objective molecular design of ionic liquid with high generation efficiency on small dataset. (August 2022) - Liu, Xiangyang; Chu, Jianchun; Zhang, Ziwen; He, Maogang
Graphical abstract: Highlights: A machine learning model for the multi-objective molecular design of ionic liquid is established. Particle swarm optimization helps variational autoencoder competent in molecule generation for small sparse dataset. The non-differentiable judgment criteria are proposed to greatly improve the generation efficiency of the model. The model is successfully applied to generate ionic liquids with high heat capacity and thermal conductivity. Abstract: Ionic liquids (ILs) are promising electrolytes or solvents for numerous applications owing to their unique properties. However, it is a challenge to design the ideal IL with the required properties. Variational autoencoders (VAEs) trained by significantly large datasets have shown good performance in drug discovery. However, low generation efficiency and small sparse datasets prevent their application on IL. In this work, we propose a high generation efficiency molecular design model for IL, which realizes multi-objective optimization on a small dataset. The model combines VAE, multilayer perceptron, and particle swarm optimization for property prediction and molecule optimization. The thermal conductivity and heat capacity of the ILs are chosen as a case to verify the advantages of our model. The results shows that by setting molecular validity judgments to optimization target, 98% output of our method are valid molecules. Besides, the heat capacity and thermal conductivity are improved by 39% and 15%, respectively. Our model improves the applicability to small sparse datasets and the generation efficiency of VAE-like generation model. By multi-objective design ILs for given properties, our model can provide guidance for the design and application of ILs.