Revolutionizing layered double hydroxides for adsorption, catalysis and energy storage through artificial intelligence and machine learning: A critical review

Layered Double Hydroxides (LDHs) are a structurally flexible class of 2D materials that offer great potential for a wide range of applications. The use of artificial intelligence (AI) and machine learning (ML) is revolutionizing the scientific research on LDHs, by generating new data-driven insights that drastically reduced the design cycle time. This critical review intends to demonstrate how extensively AI and ML applications are transforming the science of LDHs. It provides a clear step-wise explanation of how different AI and ML techniques, including supervised, unsupervised, deep, and active learning are used to predict material properties and performance from composition, structure, and synthesis-related features. Together, these methods allow the design of novel LDH architectures, thus greatly speeding up the discovery of these materials. It also delivers a detailed report on the combinatorial methods, which reveal the essential crystal structure of LDHs via data mining through cross-validation of the actual XRD, and provide the evaluations of the changes in its energy density through Bayesian inference using Markov chain Monte Carlo simulations. Furthermore, it highlights the recent trends of AI/ML applications of LDHs on catalysis, adsorption, oxygen evolution reaction (OER), hydrogen evolution reaction (HER) and energy storage. Moreover, the future viewpoints have been outlined, underlining the potential of AI/ML incorporation with high-throughput experimentation, autonomous synthesis, and advanced characterization techniques for unleashing unimaginable capabilities of LDH material design and applications. It is anticipated that a synergistic approach would drastically reduce the material development time and empower the next-generation LDHs with tailored functionalities for a sustainable future. • Artificial intelligence and machine learning are transforming the design of LDHs. • Structure-property and composition are predicted through machine learning. • Chemistry of optimization LDHs is investigated using artificial intelligence. • Intercalation mechanisms with DFT-based electronic structures of LDHs are explored. • Machine learning boosts LDH efficiency in OER, HER, adsorption and energy storage. • Current trends and emerging challenges in this context are discussed.