Extending machine learning beyond interatomic potentials for predicting molecular properties

Fedik, Nikita; Zubatyuk, Roman I.; Kulichenko, Maksim; Lubbers, Nicholas; Smith, Justin S.; Nebgen, Benjamin; Messerly, Richard A.; Li, Ying Wai; Boldyrev, Alexander I.; Barros, Kipton; Isayev, Olexandr; Tretiak, Sergei

doi:10.1038/s41570-022-00416-3

Cited by 59 publications

(54 citation statements)

References 179 publications

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“…In particular, very good results were obtained for metals and small molecules, where neural network potentials optimized on QM calculations allowed to transfer the accuracy of first-principles calculations into systems with hundreds of thousands of particles. Several reviews exist on this topic. ,,− …”

Section: Machine Learning Enabled Macromolecular Coarse-grained Simul...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 Enabled Macromolecular Coarse-grained Simul...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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“…94,114 Recently, there has been a great success in the construction of QM-quality MLPs for small to medium size molecules. [115][116][117][118][119][120][121][122][123][124] Our work constitutes a natural extensions of these MLP models to offer an implicit description for the interaction of a QM molecule with their solvent environments. Cost-wise, the results above showed that the QM/MM-quality MLP interaction forces could be predicted using the same ML architecture for the prediction of the MM-quality solute-solvent interaction MLP forces, which was far less expensive than the QM calculation of the solute molecule (see Table 2).…”

Section: Mlp Implicit Solvent Model For Qm Modelingmentioning

confidence: 99%

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

et al. 2023

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“…Due to their great potential for accelerating materials discovery and design, there has been significant interest in machine learning (ML) models that enable the fast and accurate prediction of molecular and materials properties. [1][2][3][4] Consequently, a wide range of neural network (NN) and Kernel ML methods have been developed and applied to systems ranging from isolated molecules to complex amorphous solids. [5][6][7][8][9][10][11][12][13] In this context, many state-of-the-art approaches exploit the approximately local nature of chemical interactions.…”

Section: Introductionmentioning

confidence: 99%

Physics-Inspired Machine Learning of Localized Intensive Properties

Chen

Künkel

Cheng³

et al. 2023

Preprint

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Machine learning (ML) has been widely applied to chemical property prediction, most prominently for the energies and forces in molecules and materials. The strong interest in predicting energies in particular has led to a local energy-based paradigm for modern atomistic ML models, which ensures size-extensivity and a linear scaling of computational cost with system size. However, many electronic properties (such as excitation energies or ionization energies) do not necessarily scale linearly with system size and may even be spatially localized. Using size-extensive models in these cases can lead to large errors. In this work, we explore different strategies for learning intensive and localized properties, using HOMO energies in organic molecules as a representative test case. In particular, we analyze the pooling functions that atomistic neural networks use to predict molecular properties, and suggest an orbital weighted average (OWA) approach that enables the accurate prediction of orbital energies and locations.

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Extending machine learning beyond interatomic potentials for predicting molecular properties

Cited by 59 publications

References 179 publications

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

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

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

Physics-Inspired Machine Learning of Localized Intensive Properties

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