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Gibbs free energy is a fundamental physical property for understanding the stability and synthesizability of materials under various thermodynamic conditions, but its accessibility and availability are still limited. In this study, we used 9880 phonon databases to construct a machine learning model to predict approximately 40 000 Inorganic Crystalline Solid Database (ICSD) materials, whose free energy information has not been fully explored. To improve the prediction accuracy, a sampling strategy was implemented by including structures with low accuracy metrics, leading to R2 and mean absolute error values of 0.99 and 18.7 kJ/mol, respectively, in the testing set. Uncertainty analysis was followed for unexplored ICSD materials by obtaining the standard deviation in predictions from 10 surrogate models with different samplings in the training set. Based on this, an optimization process was conducted: density functional calculations were performed for 50 structures with high uncertainty and the training database was updated; this loop was repeated 15 times. This demonstrates the reduction and saturation in the uncertainty, confirming that the constructed model can provide a comprehensive map of the Gibbs free energy for inorganic solid materials. This can accelerate the material screening process by providing information on thermodynamic stability.

Li-ion solid-state electrolytes (SSEs) have great potential, but their commercialization is limited due to interfacial contact stability issues and the formation and growth of dendrites. In this study, a machine learning regression algorithm was implemented to screen for mechanically superior SSEs among 17,619 candidates. Elasticity information (14,238 structures) was imported from an available database, and their machine learning descriptors were constructed using physiochemical and structural properties. A surrogate model for predicting the shear and bulk moduli exhibited R2 values of 0.819 and 0.863, respectively. The constructed model was applied to predict the elastic properties of potential SSEs, and first-principles calculations were conducted for validation. Furthermore, the application of an active learning process, which reduced the prediction uncertainty, was clearly demonstrated to improve the R2 score from approximately 0.6-0.8 by adding only 32-63% of new data sets depending on the type of modulus. We believe that the current model and additional data sets can accelerate the process of finding optimal SSEs to satisfy the mechanical conditions being sought.

Li-ion solid-state electrolytes (SSEs) have great potential, but their commercialization is limited due to interfacial contact stability issues and the formation and growth of dendrites. In this study, a machine learning regression algorithm was implemented to screen for mechanically superior SSEs among 17,619 candidates. Elasticity information (14,238 structures) was imported from an available database, and their machine learning descriptors were constructed using physiochemical and structural properties. A surrogate model for predicting the shear and bulk moduli exhibited

Automated metaphor detection is a challenging task to identify metaphorical expressions of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we propose a novel metaphor detection model, namely metaphor-aware late interaction over BERT (MelBERT). Our model not only leverages contextualized word representation but also benefits from linguistic metaphor identification theories to distinguish between the contextual and literal meaning of words. Our empirical results demonstrate that MelBERT outperforms several strong baselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi., In Proceedings of 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics. 11 pages

Na-ion solid-state electrolytes (Na-SSE) exhibit high potential for electrical energy storage owing to their high energy densities and low manufacturing cost. However, their mechanical properties critical to maintain structural stability at the interface are still insufficiently understood. In this study, a machine learning based regression model was developed for predicting the mechanical properties of Na-SSEs. As a training set, 12,361 materials were obtained from a well-known materials database (Materials Project) and were represented with their respective chemical and structural descriptors. The developed surrogate model exhibited a remarkable accuracy (R2 score) of 0.72 and 0.87, with a mean absolute error of 11.8 GPa and 15.3 GPa for the shear and bulk modulus, respectively. This model was then applied to predict the mechanical properties of 2,432 Na-SSEs, the properties of which have been validated with first principles calculations. Finally, the optimization process was performed to develop an ideal materials screening platform by adding the new minimized dataset, wherein the prediction uncertainty is reduced. We believe that the platform proposed in this study can accelerate the search for Na-SSEs with ideal mechanical properties at minimum cost., Comment: Submitted and Under Review