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[Display omitted] • Efficient ground state determination methodology is introduced using machine learning and first-principles calculations. • Polar phase diagram of BaTiO 3 , a representative ferroelectric oxide, is accurately determined by the proposed methodology. • The proposed methodology is a viable way to study the phase transition of solid-state material, with minimizing cost. Machine learning emerges to accelerate first-principles calculations in materials discovery and property prediction, but developing high-accuracy prediction models requires training on large datasets of relaxed structures, which can be computationally expensive. Besides, the crystal structure prediction algorithms show promising aspects but often deviate from the ground state, leading to poor predictions. Using a crystal graph convolutional neural network, we propose an alternative and efficient method of finding the ground state by utilizing the total energy of random unrelaxed structures. The prediction model is trained on a small dataset of unrelaxed energy obtained from first-principles calculations and effectively predicts the total energy of random structures with an accuracy almost equivalent to first-principles calculations. The resulting energy landscape is then reconstructed in terms of polarity. This leads to establishing an energy landscape of a perovskite oxide in a cost-effective way, enabling a polar phase stability map of strained BaTiO 3 , a representative ferroelectric oxide. The proposed methodology efficiently allows to obtain the ground state by eliminating the computationally intensive procedure of structure optimization, thus accelerating the ground state search to map out the phase stability of strained perovskite materials in a practical manner. [ABSTRACT FROM AUTHOR]

• Third reaction step (ΔG OOH -ΔG O) proposed to define the catalytic activity for OER. • Role of Oxygen intermediate is identified. • Simple and general electronic descriptor is identified. • Predictive models are developed using machine learning algorithms. Descriptor based model can be efficient in identifying an optimal carbon-based catalyst for oxygen evolution reaction (OER). Here, we correlate the O-atom adsorption strength with the OER activity of graphene nanoribbon systems and define the energy parameters (ΔG O -ΔG OH) to identify the overpotential (ɳ). The π electron based descriptor can predict the catalytic activity of the graphene surfaces. Machine learning algorithms like Multiple Linear Regression, Random Forest Regression and Support Vector Regression (SVR) are trained on the data generated by density functional theory to predict the overpotential. An optimal active site for OER using proposed SVR model is identified with overpotential (0.29 V) and then validate through DFT calculations. To generalize the study, we used SVR model on N doped GNR to predict the site-specific activity towards OER. Such a combined approach can be extended to estimate the site-specific OER activity of different carbon catalysts at a dramatically reduced computational cost. [ABSTRACT FROM AUTHOR]