Machine Learning Advances in Chemical Physics

From molecular simulations to data-driven discovery, explore recent machine learning research in chemical physics View in Browser SUBMIT AN ARTICLE MANAGE MY ALERTS OUR JOURNALS Chemical Physics Portfolio Advancing Chemical Physics Through Machine Learning The Journal of Chemical Physics (JCP) and Chemical Physics Reviews both offer a publication pathway for machine learning research in chemical physics--pairing original advances with a broader field-level perspective.  JCP publishes original research on machine learning methods and applications across molecular and materials modeling, electronic structure theory, molecular dynamics, and data-driven simulations, emphasizing technical innovation and new physical insight. Complementing this, Chemical Physics Reviews provides authoritative reviews and articles that synthesize progress in machine learning, including emerging algorithms, benchmarking, interpretability, and the broader impact of AI-driven approaches. Together, JCP and Chemical Physics Reviews support the machine learning community by showcasing both new research and the evolving context shaping the field of chemical physics. From molecular modeling and materials discovery to electronic structure and reaction dynamics, these articles showcase how machine learning is advancing insight and expanding possibilities for chemical physicists. Explore the machine learning articles below from both journals. MACHINE LEARNING ARTICLES FROM THE JOURNAL OF CHEMICAL PHYSICS SCINE—Software for chemical interaction networks Thomas Weymuth, Jan P. Unsleber, Paul L. Türtscher, Miguel Steiner, et al. READ MORE > Quantum chemical package Jaguar: A survey of recent developments and unique features Yixiang Cao, Ty Balduf, Michael D. Beachy, M. Chandler Bennett, et al. READ MORE > i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations Yair Litman, Venkat Kapil, Yotam M. Y. Feldman, Davide Tisi, et al. READ MORE > The Amsterdam Modeling Suite Evert Jan Baerends, Nestor F. Aguirre, Nick D. Austin, Jochen Autschbach, et al. READ MORE > Modern semiempirical electronic structure methods Pavlo O. Dral, Ben Hourahine, Stefan Grimme READ MORE > Quantum computing for chemistry and physics applications from a Monte Carlo perspective Guglielmo Mazzola READ MORE > Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials Amir Omranpour, Pablo Montero De Hijes, Jörg Behler, Christoph Dellago READ MORE > Application of graph neural network in computational heterogeneous catalysis Zihao Jiao, Ya Liu, Ziyun Wang READ MORE > Molecular hypergraph neural networks Junwu Chen, Philippe Schwaller READ MORE > Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks Pablo Montero de Hijes, Christoph Dellago, Ryosuke Jinnouchi, Bernhard Schmiedmayer, et al. READ MORE > MACHINE LEARNING ARTICLES FROM CHEMICAL PHYSICS REVIEWS Applications of machine learning in surfaces and interfaces Shaofeng Xu, Jingyuan Wu, Ying Guo, Qing Zhang, et al. READ MORE > Kernel regression methods for prediction of materials properties: Recent developments Ye Min Thant, Taishiro Wakamiya, Methawee Nukunudompanich, Keisuke Kameda, et al. READ MORE > Role of artificial intelligence in the design and discovery of next-generation battery electrolytes Manikantan R. Nair, Tribeni Roy READ MORE > Rational electrocatalyst design for selective nitrate reduction to ammonia Zhaodong Niu, Guoxiong Wang READ MORE > Self-assembly of architected macromolecules: Bridging a gap between experiments and simulations Ji Woong Yu, Changsu Yoo, Suchan Cho, Myungeun Seo, et al. READ MORE > Advances in modeling complex materials: The rise of neuroevolution potentials Penghua Ying, Cheng Qian, Rui Zhao, Yanzhou Wang, et al. READ MORE > Machine learning prediction of materials properties from chemical composition: Status and prospects Mohammad Alghadeer, Nufida D. Aisyah, Mahmoud Hezam, Saad M. Alqahtani, et al. READ MORE > Transcend the boundaries: Machine learning for designing polymeric membrane materials for gas separation Jiaxin Xu, Agboola Suleiman, Gang Liu, Renzheng Zhang, et al. READ MORE > Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems Soohaeng Yoo Willow, Amir Hajibabaei, Miran Ha, David ChangMo Yang, et al. READ MORE > Aqueous solution chemistry in silico and the role of data-driven approaches Debarshi Banerjee, Khatereh Azizi, Colin K. Egan, Edward Danquah Donkor, et al. READ MORE > EXPLORE MORE IN CHEMICAL PHYSICS  In our extensive collection of journals, conference proceedings, and books that span chemical physics and its interactions with other disciplines, we'll provide you with the best home for your work and the tools and resources you need to elevate and amplify your research. LEARN MORE ABOUT OUR CHEMICAL PHYSICS PORTFOLIO > Follow us on social media   Copyright © 2026 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