Call for Papers: Machine Learning and Statistical Mechanics—Shared Synergies for Next Generation of Chemical Theory and Computation

The Journal of Chemical Theory and Computation seeks submissions for an upcoming Virtual Special Issue, “Machine Learning and Statistical Mechanics: Shared Synergies for Next Generation of Chemical Theory and Computation.”

This Virtual Special Issue delves into the convergence of machine learning and statistical mechanics to reshape chemical theory and computation. Classical and quantum simulation methods, including Molecular Dynamics, Density Functional Theory, quantum dynamics, and others have evolved significantly—yet challenges persist in capturing complex processes at experimentally relevant length and timescales.

Significant progress in developing new methods for computational chemistry has come in recent years through statistical mechanics and machine learning. For example, advances in coarse-graining and enhanced sampling through statistical mechanics complement the impact of machine learning on force fields, structure prediction, and reaction coordinate discovery. Despite these advancements, challenges remain, due to the chemistry's data-scarce nature posing hurdles for machine learning's data-driven approaches.

For this Virtual Special Issue, we seek contributions on novel methods that blend classical/quantum and equilibrium/non-equilibrium statistical mechanics with diverse machine learning paradigms, including deep learning, reinforcement learning, and generative artificial intelligence. The VSI will provide a platform for scientists to showcase how the convergence of machine learning and statistical mechanics can solve chemical problems of the future.

We welcome submissions, but are not limited to, in the following areas:

• Force-field development, including coarse-graining and neural network potentials
• Structure and physicochemical properties prediction for small molecules, biomolecules, and materials
• Zero-temperature and finite-temperature transition state, pathways, and kinetics calculations for catalysis, crystallization, conformational changes, and other biochemical problems
• Enhanced sampling approaches involving dimensionality reduction or generative models

Organizing Editors

Marco De Vivo, Executive Editor, Journal of Chemical Theory and Computation
Istituto Italiano di Tecnologia, Italy

Pratyush Tiwary, Associate Editor, Journal of Chemical Theory and Computation
University of Maryland, USA

Rose Cersonsky, Guest Editor
University Wisconsin Madison, USA

Binqing Cheng, Guest Editor
Institute of Science and Technology, Austria

Submission Instructions

Submissions will be peer-reviewed and, if accepted, will be published in a regular issue of the Journal of Chemical Theory and Computation. Once the Virtual Special Issue is complete, all articles will be publicized as a virtual collection, providing additional exposure for the work.

How to Submit

• Log in to the ACS Paragon Plus submission site.
• Choose the Journal of Chemical Theory and Computation as your journal.
• Select your manuscript type.
• Under the ‘Special Issue Selection’ menu, choose "Machine Learning and Statistical Mechanics: Shared Synergies for Next Generation of Chemical Theory and Computation."

Please see our Author Guidelines for more information on submission requirements. We look forward to your contributions. The deadline for submissions for this Virtual Special Issue is April 1, 2024.