The lack of accurate methods for predicting the viscosity of solvent materials, especially those with complex interactions, remains unresolved. Deep eutectic solvents (DESs), an emerging class of green solvents, have a severe lack of viscosity data, resulting in their application still staying at the stage of random trial and error, and it is difficult for them to be implemented on an industrial scale. In this work, we demonstrate the successful prediction of the viscosity of DESs based on the transition state theory-inspired neural network (TSTiNet). The TSTiNet adopts multilayer perceptron (MLP) for the transition state theory-inspired equation (TSTiEq) parameters calculation and verification using the most comprehensive DESs viscosity data set to date. For the energy parameters of the TSTiEq, the constant assumption and the fast iteration with the help of MLP can allow TSTiNet to achieve the best performance (the average absolute relative deviation on the test set of 6.84% and R 2 of 0.9805). Compared with the traditional machine learning methods, the TSTiNet has better generalization ability and dramatically reduces the maximum relative deviation of prediction under the constraints of the thermodynamic formulation. It requires only the structural information on DESs and is the most accurate and reliable model available for DESs viscosity prediction.

Deep eutectic solvents (DESs), a novel category of sustainable solvents, are expected to achieve the design of the chemical processes without utilizing or generating harmful chemicals. In this work, based on the mathematical model inspired by the transition state theory, the group contribution method is used to accurately predict the viscosity of DESs. The model is constrained by Eyring rate theory and hard sphere free volume theory. A dataset of 2229 experimental viscosity data points of 183 DESs from literature is used to determine the model parameters and subsequently verify the model. The rules introduced by this model are simple and easy to follow. The results show that the proposed model is capable to predict the viscosity of DESs with very high accuracy, using only temperature and composition as inputs. The average absolute relative deviations (AARDs) of the model are 8.12% and 8.64% over the training and test sets, respectively, and the maximum ARD is 34.63%. Therefore, the as-proposed model can be considered a highly reliable tool for predicting the viscosity of DESs when experimental data are absent. It will provide useful guidance for the synthesis of DESs with specific viscosity to meet different application requirements and promote their industrial-scale implementation.

Molecular representation learning is a crucial task to accelerate drug discovery and materials design. Graph neural networks (GNNs) have emerged as a promising approach to tackle this task. However, most of them do not fully consider the intramolecular interactions, i.e. bond stretching, angle bending, torsion, and nonbonded interactions, which are critical for determining molecular property. Recently, a growing number of 3D-aware GNNs have been proposed to cope with the issue, while these models usually need large datasets and accurate spatial information. In this work, we aim to design a GNN which is less dependent on the quantity and quality of datasets. To this end, we propose a force field-inspired neural network (FFiNet), which can include all the interactions by incorporating the functional form of the potential energy of molecules. Experiments show that FFiNet achieves state-of-the-art performance on various molecular property datasets including both small molecules and large protein–ligand complexes, even on those datasets which are relatively small and without accurate spatial information. Moreover, the visualization for FFiNet indicates that it automatically learns the relationship between property and structure, which can promote an in-depth understanding of molecular structure.

In this work, based on mathematical model inspired by transition state theory, the group contribution (GC) method is used to predict the viscosity of DESs. The model is constrained by Eyring rate theory and hard sphere free volume theory. A dataset of 2229 experimental measurements of the viscosity of 183 DESs from literature is used for determining the model parameters and subsequent verification of the model. The rules introduced by this model are simple and easy to understand. The results show that the proposed model is able to predict the DESs viscosity with very high accuracy, i.e., with an average absolute relative deviation of 8.12% over the training set and 8.64% over the test set, using only temperature and composition as inputs. The maximum absolute relative deviation is 34.63%. Therefore, the as-proposed model can be considered a highly reliable tool for predicting DESs viscosity when experimental data are absent.

Machine learning has great potential in predicting chemical information with greater precision than traditional methods. Graph neural networks (GNNs) have become increasingly popular in recent years, as they can automatically learn the features of the molecule from the graph, significantly reducing the time needed to find and build molecular descriptors. However, the application of machine learning to energetic materials property prediction is still in the initial stage due to insufficient data. In this work, we first curated a dataset of 12,072 compounds containing CHON elements, which are traditionally regarded as main composition elements of energetic materials, from the Cambridge Structural Database, then we implemented a refinement to our force field-inspired neural network (FFiNet), through the adoption of a Transformer encoder, resulting in force field-inspired Transformer network (FFiTrNet). After the improvement, our model outperforms other machine learning-based and GNNs-based models and shows its powerful predictive capabilities especially for high-density materials. Our model also shows its capability in predicting the crystal density of potential energetic materials dataset (i.e. Huang & Massa dataset), which will be helpful in practical high-throughput screening of energetic materials.

Molecular representation learning is an essential component of many molecule-oriented tasks, such as molecular property prediction and molecule generation. In recent years, graph neural networks (GNNs) have shown great promise in this area, representing a molecule as a graph composed of nodes and edges. There are increasing studies showing that coarse-grained or multiview molecular graphs are important for molecular representation learning. Most of their models, however, are too complex and lack flexibility in learning different granular information for different tasks. Here, we proposed a flexible and simple graph transformation layer (i.e., LineEvo), a plug-and-use module for GNNs, which enables molecular representation learning from multiple perspectives. The LineEvo layer transforms fine-grained molecular graphs into coarse-grained ones based on the line graph transformation strategy. Especially, it treats the edges as nodes and generates the new connected edges, atom features, and atom positions. By stacking LineEvo layers, GNNs can learn multilevel information, from atom-level to triple-atoms level and coarser level. Experimental results show that the LineEvo layers can improve the performance of traditional GNNs on molecular property prediction benchmarks on average by 7%. Additionally, we show that the LineEvo layers can help GNNs have more expressive power than the Weisfeiler-Lehman graph isomorphism test.