Unsupervised learning of representative local atomic arrangements in molecular dynamics data

Roncoroni, Fabrice; Sanz-Matı́as, Ana; Sundararaman, Siddharth

doi:10.1039/d3cp00525a

Cited by 4 publications

(3 citation statements)

References 65 publications

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“…We then identify the characteristic time using a CNN to determine the dynamic exponent z. The dynamic exponent is identified as z = 1.758 (8). Furthermore, we measure the critical exponent ν ⊥ according to the finite-size scaling law, and the result is ν ⊥ = 1.83.…”

Section: Discussionmentioning

confidence: 99%

“…Over the past decade, machine learning (ML) has shown great potential in physics research owing to its incomparable capabilities for complex data processing and feature extraction [1]. Various concepts regarding ML have been developed to solve vexing problems in high-energy physics [2,3], astronomy [4,5], quantum information [6,7],and molecular dynamics [8].…”

Section: Introductionmentioning

confidence: 99%

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Supervised and unsupervised learning of (1+1) -dimensional even-offspring branching annihilating random walks

Wang,

Li,

Liu

et al. 2024

Mach. Learn.: Sci. Technol.

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Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically. The combination of ML and most renowned nonequilibrium model, directed percolation (DP), led to some significant findings. In this study, ML is applied to (1+1)-d, even offspring branching annihilating random walks (BAW), whose universality class is not DP-like. The supervised learning of (1+1)-d BAW via convolutional neural networks (CNN) results in a more accurate prediction of the critical point than the Monte Carlo (MC) simulation for the same system sizes. The dynamic exponent \;$z$\; and spatial correlation length correlation exponent \;$\nu_{\perp}$\ were also measured and found to be consistent with their respective theoretical values. Furthermore, the unsupervised learning of (1+1)-d BAW via an autoencoder (AE) gives rise to a transition point, which is the same as the critical point. The latent layer of AE, through a single neuron, can be regarded as the order parameter of the system being properly re-scaled. Therefore, we believe that ML has exciting application prospects in reaction-diffusion systems such as BAW and DP.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised and unsupervised learning of (1+1) -dimensional even-offspring branching annihilating random walks

Wang,

Li,

Liu

et al. 2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…UMAP has been used to cluster and visualize molecular structures [216,[234][235][236], and constructing CVs [237]. For examples of applications on simple datasets, we refer to [238].…”

Section: Applicationsmentioning

confidence: 99%

Manifold learning in atomistic simulations: a conceptual review

Rydzewski

Chen

Valsson

2023

Mach. Learn.: Sci. Technol.

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Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. Such practice is needed in atomistic simulations of complex systems where even thousands of degrees of freedom are sampled. An abundance of such data makes gaining insight into a specific physical problem strenuous. Our primary aim in this review is to focus on unsupervised machine learning methods that can be used on simulation data to find a low-dimensional manifold providing a collective and informative characterization of the studied process. Such manifolds can be used for sampling long-timescale processes and free-energy estimation. We describe methods that can work on datasets from standard and enhanced sampling atomistic simulations. Unlike recent reviews on manifold learning for atomistic simulations, we consider only methods that construct low-dimensional manifolds based on Markov transition probabilities between high-dimensional samples. We discuss these techniques from a conceptual point of view, including their underlying theoretical frameworks and possible limitations.

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Ca-dimers, solvent layering, and dominant electrochemically active species in Ca(BH4)2 in THF

Sanz Matias,

Roncoroni,

Sundararaman

et al. 2024

Nat Commun

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Divalent ions (Mg, Ca, and Zn) are being considered as competitive, safe, and earth-abundant alternatives to Li-ion electrochemistry, but present challenges for stable cycling due to undesirable interfacial phenomena. We explore the formation of electroactive species in the electrolyte Ca(BH4)2∣THF using molecular dynamics coupled with a continuum model of bulk and interfacial speciation. Free-energy analysis and unsupervised learning indicate a majority population of neutral Ca dimers and monomers with diverse molecular conformations and an order of magnitude lower concentration of the primary electroactive charged species – the monocation, CaBH$${}_{4}^{+}$$ 4 + – produced via disproportionation of neutral complexes. Dense layering of THF molecules within ~1 nm of the electrode surface strongly modulates local electrolyte species populations. A dramatic increase in monocation population in this interfacial zone is induced at negative bias. We see no evidence for electrochemical activity of fully-solvated Ca2+. The consequences for performance are discussed in light of this molecular-scale insight.

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Unsupervised learning of representative local atomic arrangements in molecular dynamics data

Abstract: Molecular dynamics (MD) simulations present a data-mining challenge, given that they can generate a considerable amount of data but often rely on limited or biased human interpretation to examine their...

Cited by 4 publications

References 65 publications

Supervised and unsupervised learning of (1+1) -dimensional even-offspring branching annihilating random walks

Supervised and unsupervised learning of (1+1) -dimensional even-offspring branching annihilating random walks

Manifold learning in atomistic simulations: a conceptual review

Ca-dimers, solvent layering, and dominant electrochemically active species in Ca(BH4)2 in THF

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