Machine Learning and Computational Nanochemistry for Predictive Adsorption Studies

The integration of machine learning (ML) with computational nanochemistry is significantly advancing predictive adsorption studies by enabling faster, more precise, and scalable modeling of adsorption processes at the nanoscale. This review outlines recent developments in ML techniques including regression models, deep neural networks, and hybrid physics-informed approaches and their combination with computational methods such as density functional theory (DFT), molecular dynamics (MD), and Monte Carlo simulations. Critical steps in the workflow, including data collection, feature engineering, model training, and validation, are examined in detail, along with challenges such as limited data availability, issues with model interpretability, and the transferability of models across diverse material systems. Emerging innovations like graph neural networks and active learning are discussed as promising approaches to overcome these challenges. Applications spanning gas storage, catalysis, environmental remediation, and drug delivery showcase the broad impact of these integrated strategies. This comprehensive review aims to assist researchers in leveraging the strengths of ML and computational nanochemistry to accelerate adsorption research and guide materials design.