Computational Nanoscience in the Quantum and AI Era: Integrating Biomolecular and Materials Modeling

The past decade has witnessed remarkable advances in computational power, algorithms, and theoretical methods, enabling accurate modeling and prediction of nanoscale materials. Among these, ab initio approaches—particularly Density Functional Theory (DFT) and related first-principles techniques—have become indispensable for understanding matter with atomic-scale precision. They have profoundly impacted nanoscience by revealing the structural, electronic, optical, thermodynamic, and mechanical properties of nanostructures, guiding the design of next-generation materials and devices. Applications span surface and interface science, quantum transport, catalysis, and spintronics. The reach of ab initio methods now extends to biomolecules and hybrid nanostructures, offering insights into biomolecular stability and bio–nano interactions. Coupling with molecular dynamics (MD) captures finite-temperature and time-dependent processes, while advanced algorithms, tensor methods, and machine learning acceleration reduce computational barriers. Together, these advances establish ab initio methods as a cornerstone of modern computational nanoscience, complementing and guiding experimental effort.

This Research Topic aims to highlight the most recent advances in the application of ab initio methods, particularly Density Functional Theory (DFT) and related first-principles techniques, to the rapidly evolving field of computational nanoscience. These approaches have become indispensable for describing nanoscale systems with atomic-level precision, enabling the reliable prediction of their structural, electronic, optical, thermodynamic, and mechanical properties.

This Research Topic seeks to showcase research articles, methodological developments, review papers, and combined theoretical–experimental studies that demonstrate how ab initio approaches advance our understanding of nanomaterials and nanoscale systems. Studies that integrate computational predictions with experimental observations to validate models and reveal new nanoscale insights are strongly encouraged.

We welcome submissions that combine ab initio methods with molecular dynamics (MD), multiscale simulations, or machine learning acceleration, and those that bridge theory with spectroscopic, microscopic, or diffraction-based experiments. Integrated approaches that capture both equilibrium properties and dynamic processes across length and time scales are especially encouraged. Studies that test predictions using techniques such as TEM, XPS, Raman, or optical absorption are also strongly encouraged, ensuring theory both predicts and explains real‑world measurements.

Ultimately, this Research Topic strives to provide a platform that unites theoretical innovation, computational efficiency, and experimental validation, reflecting the state-of-the-art in ab initio nanoscience and guiding the discovery of next-generation materials. We aim to foster interdisciplinary research that unites theory, computation, and experiment, showcasing how ab initio methods contribute to new insights, accelerated discovery, and the design of next-generation nanomaterials.