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At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...

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Updated: Jul 8, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

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Differentiable rotamer sampling with molecular force fields.

Congzhou M Sha^1,2, Jian Wang², Nikolay V Dokholyan^1,2,3,4,5

¹Department of Engineering Science and Mechanics, Penn State University, University Park, PA USA.

Briefings in Bioinformatics

|December 14, 2023

Summary

This summary is machine-generated.

Boltzmann generators, a neural network approach, offer faster convergence to thermodynamic equilibrium than traditional molecular dynamics (MD) for studying macromolecule structure and function.

Keywords:

Boltzmann generator differentiable programming molecular dynamics neural network rotameric sampling statistical mechanics

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Area of Science:

Computational biology
Structural biology
Machine learning in science

Background:

Molecular dynamics (MD) is a key computational tool for exploring macromolecule structure and function.
Boltzmann generators offer a novel, neural network-based alternative to MD for simulating molecular systems.
Current limitations in theory and computational feasibility hinder the widespread adoption of Boltzmann generators.

Purpose of the Study:

To establish a robust mathematical foundation for Boltzmann generators.
To demonstrate the speed and feasibility of Boltzmann generators as an alternative to traditional MD.
To provide a toolkit for utilizing neural networks in molecular energy landscape exploration.

Main Methods:

Development of a mathematical framework for Boltzmann generators.
Training generative neural networks to replace time-based integration in molecular simulations.
Application of the developed methods to complex macromolecules, including proteins.

Main Results:

The study provides a foundational mathematical basis for Boltzmann generators.
Demonstrated that Boltzmann generators can achieve faster convergence to thermodynamic equilibrium compared to traditional MD.
The approach is shown to be computationally feasible for complex macromolecules like proteins.

Conclusions:

Boltzmann generators, supported by a strong mathematical foundation, can effectively replace traditional MD in specific applications.
This work significantly enhances the usability of neural network-based approaches for molecular simulations.
A comprehensive toolkit is provided for exploring molecular energy landscapes using neural networks.