# A coarse-grained deep neural network model for liquid water

@article{Patra2019ACD, title={A coarse-grained deep neural network model for liquid water}, author={Tarak K. Patra and Troy David Loeffler and Henry Chan and Mathew J. Cherukara and Badri Narayanan and Subramanian K.R.S. Sankaranarayanan}, journal={Applied Physics Letters}, year={2019} }

We introduce a coarse-grained deep neural network model (CG-DNN) for liquid water that utilizes 50 rotational and translational invariant coordinates, and is trained exclusively against energies of ~30,000 bulk water configurations. Our CG-DNN potential accurately predicts both the energies and molecular forces of water; within 0.9 meV/molecule and 54 meV/angstrom of a reference (coarse-grained bond-order potential) model. The CG-DNN water model also provides good prediction of several…

## 12 Citations

Active learning a coarse-grained neural network model for bulk water from sparse training data

- Computer Science
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This work demonstrates that with ∼300 reference data, the AL-NN water model is able to accurately predict both the energies and the molecular forces of water, within 2 meV per molecule and 40 meV A−1 of the reference model.

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The potential of the proposed data-driven machine learning procedure has been demonstrated for parameterizing a MD-based material model that exhibits excellent performance with a reasonable balance among the four crucial physical properties of water.

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Several important aspects in force field development are discussed and features in BLAST (Bridging Length/time scales via Atomistic Simulation Toolkit) that enable its functionalities and ease of use are highlighted.

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Understanding materials dynamics under extreme conditions of pressure, temperature, and strain rate is a scientific quest that spans nearly a century. Atomic simulations have had a considerable…

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- Computer ScienceThe journal of physical chemistry. A
- 2020

We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of…

Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data

- Physics
- 2020

Molecular dynamics with predefined functional forms is a popular technique for understanding dynamical evolution of systems. The predefined functional forms impose limits on the physics that can be...

Computational Modeling of Battery Materials

- EngineeringReference Module in Earth Systems and Environmental Sciences
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Deep Learning Order Parameter for Polymer Phase Transition

- Computer Science, Materials Science
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A deep autoencoder that autonomously discovers an appropriate order parameter from molecular dynamics simulation data to characterize the coil to globule phase transition of a polymer is reported.

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