Emerging Research Fronts in Machine Learning for Studying Excited State Dynamics
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".
Deadline for manuscript submissions: 31 October 2024 | Viewed by 54
Special Issue Editors
Interests: machine learning; drug discovery; geometric deep learning; materials science; quantum chemistry
Special Issue Information
Dear Colleagues,
The study of excited-state dynamics is crucial for understanding phenomena such as photoabsorption, energy and charge transfers, photochemical reactions, etc. Traditional computational methods for studying excited-state dynamics face significant challenges in terms of computational cost and accuracy; thus, they are often limited to small systems with short dynamics. The emergence of machine learning is revolutionizing computational chemistry and materials science, providing novel methods to address these challenges in modeling excited-state dynamics. Machine learning models can learn from data to predict properties or study dynamics for functional material design. Prior knowledge can be encoded into these models to gain unprecedented accuracy, and novel model architectures can significantly reduce the computational cost. Overall, machine learning has opened new frontiers in the study of excited-state dynamics, enabling researchers to tackle problems which were previously intractable.
This Special Issue aims to cover recent advances in the development and application of machine learning in studying excited-state dynamics. The topic of interest include, but are not limited to, methods and/or applications in the following areas:
- Excited-state machine learning force fields;
- Excited-state property prediction with machine learning;
- Solar cell design with machine learning;
- Nonadiabatic excited-state dynamics with machine learning;
- Machine learning exciton dynamics;
- Machine learning for quantum dynamics.
Dr. Guoqing Zhou
Guest Editors
Dr. Wang Bipeng
Guest Editor Assistant
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
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machine learning
- excited-state dynamics
- quantum dynamics
- nonadiabatic excited-state dynamics
- charge transfer
- photochemistry
- photodynamics
