Reminder- Machine Learning for Electronic Structure

Submit by March 31, 2026 View in Browser SUBMIT AN ARTICLE MANAGE MY ALERTS ABOUT THE JOURNAL The Journal of Chemical Physics Call for Papers Special Topic: Machine Learning for Electronic Structure Machine learning has become a powerful tool to replace or accelerate electronic-structure calculations, for example by predicting interatomic potentials with high accuracy and low cost. Increasingly, ML is also integrated into modern quantum chemistry and electronic-structure theory, enhancing algorithms that describe the quantum behavior of electrons in molecules and materials. This Special Issue highlights three complementary themes:   Learning the ingredients of electronic-structure calculations: Contributions predicting electron densities, Hamiltonian elements, or other effective one- and two-body operators from atomic configurations, while exploiting symmetries and uncertainty quantification, are encouraged.   ML-accelerated many-body methods: Studies embedding ML models into wave-function theories—from coupled-cluster and multireference to variational and diffusion Monte Carlo—aiming to reduce scaling, extend reach, or improve accuracy.   Data-driven density functionals: Manuscripts developing and testing exchange–correlation functionals, kinetic-energy functionals, or orbital-free models with supervised, unsupervised, or reinforcement learning are welcome. Topics covered include, but are not limited to: • Machine learning (ML) • Electronic-structure calculations • Interatomic potentials • Quantum chemistry • Electronic-structure theory • Quantum mechanical behavior Guest Editors Michele Pavanello, Rutgers University Frank Noe, Microsoft Research AI for Science Julia Westermayr, University of Leipzig Submission Deadline: March 31, 2026 LEARN MORE > Tell Us What You Think! Did you like this email? Yes > No >  Follow us on social media   Copyright © 2025 AIP Publishing. All rights reserved. 1305 Walt Whitman Rd., Melville, NY 11747 You are receiving this email because you have opted-in to receive alerts from us. To guarantee delivery of this email please add journals@aip-info.org to your address book and safe senders list. If you no longer wish to receive emails from us then please unsubscribe or amend your settings. Privacy Policy