Machine Learning meets Quantum Chemistry

We are excited to announce the Call for Papers for the upcoming Special Issue on "Machine Learning meets Quantum Chemistry: accelerating simulations of reactivity, photophysical, and photochemical properties". This collection aims to showcase work from both the machine learning and quantum chemistry communities to explore innovative approaches in accelerating simulations and advancing our understanding and predictive ability with respect to chemical reactivity, photophysical, and photochemical properties.

Key Topics:

● Integration and application of machine learning techniques within quantum chemistry methodologies, with accelerated simulations

● Predictive modelling of chemical reactivity, photophysical and photochemical properties

● Building/Design of quantum chemical datasets for machine learning applications

● Deep-learning wavefunctions for molecules

● Hybrid ML/MM embedding for molecules in complex environments

● Novel approaches for data-driven discovery in quantum chemistry

● Challenges and opportunities at the intersection of machine learning and quantum chemistry

We invite submissions of original research papers addressing the collection theme. Authors are encouraged to submit high-quality papers describing their latest findings, methodologies, and applications. Submissions will undergo a rigorous peer-review process to ensure the quality and relevance of accepted papers.

Collections and special issues follow the standard peer review policy.