An atomistic MD simulation method has been developed to study the electroosmotic drag in the hydrated perfluorosulfonic acid polymer. The transport characteristics of the hydroniums and water molecules are evaluated from their velocity distribution functions with an electric field applied. It is shown that the microstructure of the hydrated perfluorosulfonic acid polymer is not perturbed significantly by the electric field up to 2 V/microm, and the velocity distribution functions obey the peak shifted Maxwell velocity distribution functions. The evaluated peak shifting velocities are only about 1% of the average thermal motion. The hydronium flow and water flow are evaluated from the average transport velocities or the peak shifting velocities. The electroosmotic drag coefficients from the MD simulations are in good correspondence with the experimental values. It is also shown that the electroosmotic drag coefficient has no or weak temperature dependence.

Molecular simulation studies of the microstructure and of the proton transport properties of phosphoric acid solvated Nafion membrane are carried out. The ab initio calculations show that the phosphoric acid is a good solvent to promote the proton ionization of the sulfonic acid group, and only two phosphoric acid molecules are necessary for the dissociation of one sulfonic acid group. A mechanism of proton hopping between phosphoric acid and protonated phosphoric acid cation in the hydrophilic subphase is also elucidated by ab initio calculations. The molecular dynamics simulations, conducted at a phosphoric acid concentration of 25.4% (wt) which is slightly lower than that of phosphoric acid swollen Nafion, show that the phosphoric acid exists in subphases and that it cannot develop into a continuous subphase. Thus, proton-hopping pathways are interrupted, and the conductivity is expected to be lower than that for pure phosphoric acid. The molecular dynamics simulations, conducted at a phosphoric acid concentration of 45.1% (wt) which corresponds to an unstable state, show that the hydrophobic poly(tetrafluoroethylene) backbones trend to gather together forming hydrophobic clusters and that the phosphoric acid forms a continuous subphase with the sulfonic acid groups located at the hydrophobic/hydrophilic interface. Thus, proton-hopping pathways can develop uninterruptedly like the pure phosphoric acid, and high conductivity is expected. The molecular dynamics study also shows that the hydrogen-bonding characteristics of phosphoric acid and sulfonate anion are similar regardless of the factor that the former can move freely while the latter is attached to Nafion backbone.

This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction from publications, materials database construction, high-throughput computations and experiments from the data source level; modeling algorithms for small data and imbalanced learning from the algorithm level; active learning and transfer learning from the machine learning strategy level. Finally, the future directions for small data machine learning in materials science were proposed.

The specific surface area (SSA) of ABO3-type perovskite is one of the important properties associated with photocatalytic ability. In this work, data mining methods were used to explore the relationship between the SSA (in the range of 1–60 m2 g–1) of perovskite and its features, including chemical compositions and technical parameters. The genetic algorithm–support vector regression method was used to screen the main features for modeling. The correlation coefficient (R) between the predicted and experimental SSAs reached as high as 0.986 for the training data set and 0.935 for leave-one-out cross-validation. ABO3-type perovskites with higher SSA can be screened out using the Online Computation Platform for Materials Data Mining (OCPMDM) developed in our laboratory. Further, an online web server has been developed to share the model for the prediction of SSA of ABO3-type perovskite, which is accessible at .

Materials design is the most important and fundamental work on the background of materials genome initiative for global competitiveness proposed by the National Science and Technology Council of America. As far as the methodologies of materials design, besides the thermodynamic and kinetic methods combing databases, both deductive approaches so-called the first principle methods and inductive approaches based on data mining methods are gaining great progress because of their successful applications in materials design. In this paper, support vector machine (SVM), including support vector classification (SVC) and support vector regression (SVR) based on the statistical learning theory (SLT) proposed by Vapnik, is introduced as a relatively new data mining method to meet the different tasks of materials design in our lab. The advantage of using SVM for materials design is discussed based on the applications in the formability of perovskite or BaNiO 3 structure, the prediction of energy gaps of binary compounds, the prediction of sintered cold modulus of sialon-corundum castable, the optimization of electric resistances of VPTC semiconductors and the thickness control of In 2 O 3 semiconductor film preparation. The results presented indicate that SVM is an effective modeling tool for the small sizes of sample sets with great potential applications in materials design.