Efficient Free-Energy Landscape Construction Using Graph-Based Active Path Sampling
- 1Thomas Lord Department of Mechanical Engineering and Materials Science
- 2Duke University
Abstract
Constructing free-energy landscapes without prior knowledge of their structure often requires dense sampling of the configuration space, leading to prohibitive computational costs. Here, we introduce an efficient graph-based active-learning framework that adaptively constructs free-energy landscapes while preserving the connectivity required for free-energy determination. The method employs a Gaussian-process surrogate to identify informative configurations and connects each newly sampled set of points to the existing sampled region through a shortest-path search on a graph, enabling efficient, connected exploration required for accurate free-energy determination. We validate the framework using analytical two-and three-dimensional model energy landscapes as well as a many-body free-energy landscape derived from molecular dynamics simulations of polymer-grafted nanoparticles in a polymer melt. Across all cases, the method accurately recovers the multidimensional landscape at much lower effective sampling cost than dense or predetermined grid-based sampling, with sampling efficiency quantified by the cumulative length of the connected sampling tree constructed during active learning. More broadly, this work establishes a general framework for data-efficient exploration of high-dimensional free-energy landscapes in molecular and materials simulations.
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BibTeX
@misc{farshad2026efficient,
title = {{Efficient Free-Energy Landscape Construction Using Graph-Based Active Path Sampling}},
author = {Mohsen Farshad and Gaurav Arya},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15009038/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009038/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Efficient Free-Energy Landscape Construction Using Graph-Based Active Path Sampling
AU - Mohsen Farshad
AU - Gaurav Arya
PY - 2026
DA - 2026/09/18
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009038/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009038/v1
AB - Constructing free-energy landscapes without prior knowledge of their structure often requires dense sampling of the configuration space, leading to prohibitive computational costs. Here, we introduce an efficient graph-based active-learning framework that adaptively constructs free-energy landscapes while preserving the connectivity required for free-energy determination. The method employs a Gaussian-process surrogate to identify informative configurations and connects each newly sampled set of points to the existing sampled region through a shortest-path search on a graph, enabling efficient, connected exploration required for accurate free-energy determination. We validate the framework using analytical two-and three-dimensional model energy landscapes as well as a many-body free-energy landscape derived from molecular dynamics simulations of polymer-grafted nanoparticles in a polymer melt. Across all cases, the method accurately recovers the multidimensional landscape at much lower effective sampling cost than dense or predetermined grid-based sampling, with sampling efficiency quantified by the cumulative length of the connected sampling tree constructed during active learning. More broadly, this work establishes a general framework for data-efficient exploration of high-dimensional free-energy landscapes in molecular and materials simulations.
ER -