About this Research Topic
Developments in computational chemistry and materials science have led to a growing need to understand the phenomena determining the properties of molecules and solids. As such, accurate and efficient methods for solving basic quantum-mechanical equations must be developed. Density functional theory (DFT) has marked a decisive breakthrough in the applications of computational chemistry, with its real forte manifesting in its favorable performance ratio compared to electron-correlated wave function-based methods. Therefore, DFT has become a standard tool in Theoretical and Computational Chemistry.
However, the application of DFT is limited by its poor computational complexity (at least O(N3)). This cubic-scaling bottleneck limits its applications on hundreds of atoms. Thus, it is of great importance to apply and further develop acceleration algorithms that provide physically sound answers for large and complex molecules at a reasonable computational cost. An important variety of such approaches is represented by linear scaling techniques, that is, by methods where the computational cost scales linearly with the size of the system (O(N)). Linear-scaling DFT is thus an area of active research in computational chemistry.
Furthermore, excitations in molecules and solids are nowadays at the heart of fundamental and technological research projects. There is already a rich set of theoretical methods for excited-state calculations, such as TDDFT and GW/BSE. However, these methods are all subject to computational bottlenecks that are far more severe than those of ground-state calculations.
As such, the purpose of this Research Topic is to investigate new methods of overcoming traditional bottlenecks that impede the efficacy of DFT methods when applied to large molecules. Our scope also includes the recent emergence of Machine Learning (ML) as a powerful tool to accelerate chemical discovery by extracting knowledge and insight from data generated by experimental or DFT methods.
Given the above, we welcome submissions that address themes including, but not limited to:
• state-of-the-art developments on linear- and low-scaling DFT methods
• improvements in ML algorithms in computational chemistry
• the development of excited state calculations
The Topic Editors would like to acknowledge Dr. Honghui Shang (Institute of Computing Technology, Chinese Academy of Sciences) and Dr. Jie Liu (Hefei National Laboratory, China) for their contribution to the development of this Research Topic, as well as for the advisory role they will play in this article collection based on their expertise as developers of SIESTA and of Q-Chem, respectively.
However, the application of DFT is limited by its poor computational complexity (at least O(N3)). This cubic-scaling bottleneck limits its applications on hundreds of atoms. Thus, it is of great importance to apply and further develop acceleration algorithms that provide physically sound answers for large and complex molecules at a reasonable computational cost. An important variety of such approaches is represented by linear scaling techniques, that is, by methods where the computational cost scales linearly with the size of the system (O(N)). Linear-scaling DFT is thus an area of active research in computational chemistry.
Furthermore, excitations in molecules and solids are nowadays at the heart of fundamental and technological research projects. There is already a rich set of theoretical methods for excited-state calculations, such as TDDFT and GW/BSE. However, these methods are all subject to computational bottlenecks that are far more severe than those of ground-state calculations.
As such, the purpose of this Research Topic is to investigate new methods of overcoming traditional bottlenecks that impede the efficacy of DFT methods when applied to large molecules. Our scope also includes the recent emergence of Machine Learning (ML) as a powerful tool to accelerate chemical discovery by extracting knowledge and insight from data generated by experimental or DFT methods.
Given the above, we welcome submissions that address themes including, but not limited to:
• state-of-the-art developments on linear- and low-scaling DFT methods
• improvements in ML algorithms in computational chemistry
• the development of excited state calculations
The Topic Editors would like to acknowledge Dr. Honghui Shang (Institute of Computing Technology, Chinese Academy of Sciences) and Dr. Jie Liu (Hefei National Laboratory, China) for their contribution to the development of this Research Topic, as well as for the advisory role they will play in this article collection based on their expertise as developers of SIESTA and of Q-Chem, respectively.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
