Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning

Andrews, James; Gkountouna, Olga; Blaisten‐Barojas, Estela

doi:10.1039/d2sc01216b

Cited by 6 publications

(5 citation statements)

References 63 publications

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“…ML methods have also been applied to identify criteria to diagnose whether an MD simulation is sufficiently long or needs to be extended. 121 For this scope, different Recurrent Neural Network (RNN) 61 architectures were tested by Andrews et al 121 to predict the intramolecular potential energy and the intermolecular interaction energy between a macromolecular polymer−lipid aggregate in a nonpolar solvent and forecast the longer time solvation process based on MD simulations of adequate length.…”

Section: Machine Learning For the Processing And Interpretation Of Si...mentioning

confidence: 99%

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Ricci

Vergadou

2023

J. Phys. Chem. B

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Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.

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Section: Machine Learning For the Processing And Interpretation Of Si...mentioning

confidence: 99%

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Ricci

Vergadou

2023

J. Phys. Chem. B

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“…Andrews et al studied the performance of RNN and their variants on the behavior of energetic properties of a liquid solution containing an aggregation of polymer-lipid macromolecules in an organic solvent. 73 The NNs were trained on potential energies time series of DSPE-PEG (1,2-distearoyl- sn-glycero -3-phosphoethanolamine- N -(polyethylene glycol) n amine) aggregates solvated in ethyl acetate developed through MD simulations. Semine et al used LSTM, a variant of RNN, to predict the optical spectra using coarse-grained models.…”

Section: Fundamentalsmentioning

confidence: 99%

Computational and data-driven modelling of solid polymer electrolytes

Wang,

Shi,

et al. 2023

Digital Discovery

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“…This also holds true at the molecular scale, where phenomena such as nucleation, defect propagation, and phase transitions are intricately linked to these fluctuations. The integration of advanced molecular descriptors with machine learning (ML) has been playing a key role in analyzing molecular trajectories, contributing to a better understanding of diverse nanoscale systems, ranging from atomistic to supramolecular levels [1][2][3][4][5][6][7][8][9][10][11]. Standard human-based descriptors, tailored for building detailed analyses and investigating specific systems like, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Standard human-based descriptors, tailored for building detailed analyses and investigating specific systems like, i.e. ice-water interfaces [12] or metal clusters [13,14], have increasingly left more and more space to abstract descriptors, [15][16][17][18][19][20][21] often combined with supervised and unsupervised ML methods [1][2][3][4][5][6][7][8][9][10]. These ML-based techniques offer valuable insights into the structural and dynamical properties of the systems.…”

Section: Introductionmentioning

confidence: 99%

Machine learning of microscopic structure-dynamics relationships in complex molecular systems

Crippa,

Cardellini,

Cioni

et al. 2023

Mach. Learn.: Sci. Technol.

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In many complex molecular systems, the macroscopic ensemble’s properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships between local structural environments and the dynamical events originating from them would allow unveiling microscopic-level structure-dynamics relationships fundamental to understand the macroscopic behavior of complex systems. Here we show that, by coupling advanced structural (e.g. Smooth Overlap of Atomic Positions, SOAP) with local dynamical descriptors (e.g. Local Environment and Neighbor Shuffling, LENS) in a unique dataset, it is possible to improve both individual SOAP- and LENS-based analyses, obtaining a more complete characterization of the system under study. As representative examples, we use various molecular systems with diverse internal structural dynamics. On the one hand, we demonstrate how the combination of structural and dynamical descriptors facilitates decoupling relevant dynamical fluctuations from noise, overcoming the intrinsic limits of the individual analyses. Furthermore, machine learning approaches also allow extracting from such combined structural/dynamical dataset useful microscopic-level relationships, relating key local dynamical events (e.g. LENS fluctuations) occurring in the systems to the local structural (SOAP) environments they originate from. Given its abstract nature, we believe that such an approach will be useful in revealing hidden microscopic structure-dynamics relationships fundamental to rationalize the behavior of a variety of complex systems, not necessarily limited to the atomistic and molecular scales.

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Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning

Abstract: Machine learning techniques including neural networks are popular tools for chemical, physical and materials applications searching for viable alternative methods in the analysis of structure and energetics of systems ranging...

Cited by 6 publications

References 63 publications

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Computational and data-driven modelling of solid polymer electrolytes

Machine learning of microscopic structure-dynamics relationships in complex molecular systems

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