This Virtual Special Issue will showcase exciting new work at the interface of physical chemistry and machine learning/data science. Submit your manuscript by October 31, 2023.

abstract digital artwork for the journals of physical chemistry

Following up on the highly successful Journal of Physical Chemistry (JPC) A/B/C Virtual Special Issue (VSI) on Machine Learning in Physical Chemistry published in 2020, we are pleased to announce a Call for Papers for the second edition in the series. This VSI is designed to celebrate and promote exciting new work at the interface of physical chemistry and machine learning/data science. The journals enthusiastically welcome contributions from anyone in the scientific community who would like to submit a paper within the scientific scope of the journal that develops, adopts, adapts, or improves machine learning and data science tools to drive and enable new physical insight and discovery. The deadline for submission is October 31, 2023.

This VSI will span the three Journal parts:

  • JPC A: Molecules, Clusters, and Aerosols; New Tools and Methods in Experiment and Theory
  • JPC B: Biophysics, Biomaterials, Liquids, and Soft Matter
  • JPC C: Energy, Materials, and Catalysis

Research areas of particular interest for the VSI include but are not limited to:

  • New representations for quantum machine learning
  • Accelerating and improving the accuracy of quantum chemistry and quantum dynamics through machine learning
  • Discovery of materials for novel reactivity via machine learning
  • New molecular featurizations for machine learning
  • New algorithms for materials or molecular design
  • Production of new materials or synthesis routes enabled by AI
  • Machine learning for new understanding of biomolecular folding and assembly
  • AI in the chemical industry
  • AI in microscopy and characterization
  • Prediction of new materials or molecules with bespoke properties from ML algorithms
  • New methods and techniques for machine learning in the chemical and materials domain
  • Case studies for rational design and inverse engineering enabled by machine learning
  • Examples of closed design-synthesis-characterization loops
  • Discovery of new structure–property relationships for materials design
  • AI-driven decision making in chemical research/industry
  • Democratization of machine learning approaches in the chemistry community
  • Advances of software for machine learning in chemistry and materials

Further details can be found in the accompanying Editorial.

Recently Published Articles as part of the VSI include:

Interpretable Attribution Assignment for Octanol–Water Partition Coefficient
Daisuke Yokogawa and Kayo Suda
J. Phys. Chem. B 2023, 127, 31, 7004–7010

Machine Learning Accelerated Study of Defect Energy Levels in Perovskites
Xiaoyu Wu, Haiyuan Chen, Jianwei Wang, and Xiaobin Niu
J. Phys. Chem. C 2023, 127, 23, 11387–11395

Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts
Antti Pihlajamäki, Sami Malola, Tommi Kärkkäinen, and Hannu Häkkinen
J. Phys. Chem. C 2023, 127, 29, 14211–14221

Parametrically Managed Activation Function for Fitting a Neural Network Potential with Physical Behavior Enforced by a Low-Dimensional Potential
Farideh Badichi Akher, Yinan Shu, Zoltan Varga, Suman Bhaumik, and Donald G. Truhlar
J. Phys. Chem. A 2023, 127, 24, 5287–5297

Utilizing Data-Driven Optimization to Automate the Parametrization of Kinetic Monte Carlo Models
Ioannis Kouroudis, Manuel Gößwein, and Alessio Gagliardi
J. Phys. Chem. A 2023, 127, 28, 5967–5978

Editorial Team

This Virtual Special Issue will be managed by:

Andrew L. Ferguson, Topic Editor, JPC A/B/C
University of Chicago, U.S.

Jim Pfaendtner, Senior Editor, JPC A/B/C
University of Washington, U.S.

Submission Instructions

To ensure an unbiased peer-review process, the journal asks that you do not indicate within your manuscript that the submission is intended for the VSI. If you do, your manuscript will be returned for correction. Instead, when you submit your manuscript, please indicate this in your cover letter and note what part and section you feel will be the best fit. You can find a complete list of sections and other important information for authors in the JPC Author Guidelines.

As with all submissions to JPC, contributions should represent a rigorous scientific report of original research, provide new physical insight, including illumination of the complexity of systems and concepts already being discussed in the literature, and/or present new theoretical or computational methods of broad interest. VSI submissions will be peer reviewed with the same standards as a regular submission. Contributors are also strongly encouraged to consider the criteria for successful machine learning submissions to JPC outlined in the recent editorial Characteristics of Impactful Machine Learning Contributions to The Journal of Physical Chemistry.

Contributing to this Virtual Special Issue

If you are unsure if your research is within the scope of this VSI or have other questions about submitting a manuscript to this VSI, please email the Editor-in-Chief’s office at

Want the latest stories delivered to your inbox each month?