Visualizing Energy Landscapes through Manifold Learning

Energy landscapes provide a conceptual framework for structure prediction, and a detailed understanding of their topological features is necessary to develop efficient methods for their exploration. The ability to visualize these surfaces is essential, but the high dimensionality of the correspondin...

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Autores principales:	Benjamin W. B. Shires, Chris J. Pickard
Formato:	article
Lenguaje:	EN
Publicado:	American Physical Society 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/042cc5d208404eca9d4c0e0168cbf0ee
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id	oai:doaj.org-article:042cc5d208404eca9d4c0e0168cbf0ee
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spelling	oai:doaj.org-article:042cc5d208404eca9d4c0e0168cbf0ee2021-11-05T14:20:49ZVisualizing Energy Landscapes through Manifold Learning10.1103/PhysRevX.11.0410262160-3308https://doaj.org/article/042cc5d208404eca9d4c0e0168cbf0ee2021-11-01T00:00:00Zhttp://doi.org/10.1103/PhysRevX.11.041026http://doi.org/10.1103/PhysRevX.11.041026https://doaj.org/toc/2160-3308Energy landscapes provide a conceptual framework for structure prediction, and a detailed understanding of their topological features is necessary to develop efficient methods for their exploration. The ability to visualize these surfaces is essential, but the high dimensionality of the corresponding configuration spaces makes this visualization difficult. Here, we present stochastic hyperspace embedding and projection (SHEAP), a method for energy landscape visualization inspired by state-of-the-art algorithms for dimensionality reduction through manifold learning, such as t-SNE and UMAP. The performance of SHEAP is demonstrated through its application to the energy landscapes of Lennard-Jones clusters, solid-state carbon, and the quaternary system C+H+N+O. It produces meaningful and interpretable low-dimensional representations of these landscapes, reproducing well-known topological features such as funnels and providing fresh insight into their layouts. In particular, an intrinsic low dimensionality in the distribution of local minima across configuration space is revealed.Benjamin W. B. ShiresChris J. PickardAmerican Physical SocietyarticlePhysicsQC1-999ENPhysical Review X, Vol 11, Iss 4, p 041026 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Benjamin W. B. Shires Chris J. Pickard Visualizing Energy Landscapes through Manifold Learning
description	Energy landscapes provide a conceptual framework for structure prediction, and a detailed understanding of their topological features is necessary to develop efficient methods for their exploration. The ability to visualize these surfaces is essential, but the high dimensionality of the corresponding configuration spaces makes this visualization difficult. Here, we present stochastic hyperspace embedding and projection (SHEAP), a method for energy landscape visualization inspired by state-of-the-art algorithms for dimensionality reduction through manifold learning, such as t-SNE and UMAP. The performance of SHEAP is demonstrated through its application to the energy landscapes of Lennard-Jones clusters, solid-state carbon, and the quaternary system C+H+N+O. It produces meaningful and interpretable low-dimensional representations of these landscapes, reproducing well-known topological features such as funnels and providing fresh insight into their layouts. In particular, an intrinsic low dimensionality in the distribution of local minima across configuration space is revealed.
format	article
author	Benjamin W. B. Shires Chris J. Pickard
author_facet	Benjamin W. B. Shires Chris J. Pickard
author_sort	Benjamin W. B. Shires
title	Visualizing Energy Landscapes through Manifold Learning
title_short	Visualizing Energy Landscapes through Manifold Learning
title_full	Visualizing Energy Landscapes through Manifold Learning
title_fullStr	Visualizing Energy Landscapes through Manifold Learning
title_full_unstemmed	Visualizing Energy Landscapes through Manifold Learning
title_sort	visualizing energy landscapes through manifold learning
publisher	American Physical Society
publishDate	2021
url	https://doaj.org/article/042cc5d208404eca9d4c0e0168cbf0ee
work_keys_str_mv	AT benjaminwbshires visualizingenergylandscapesthroughmanifoldlearning AT chrisjpickard visualizingenergylandscapesthroughmanifoldlearning
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Visualizing Energy Landscapes through Manifold Learning

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