Accelerating discrete dislocation dynamics simulations with graph neural networks

Bertin, Nicolas; Zhou, Fei

doi:10.1016/j.jcp.2023.112180

Cited by 10 publications

(5 citation statements)

References 46 publications

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“…We used the processor from MeshGraphNets 41 for implementing graph convolutions. Although we have consid-ered rotationally equivariant GNNs for predicting the noise ε (a vector quantity), the simple design of MeshGraphNets renders the computation to be fast without much loss to accuracy, as demonstrated in previous surrogate models for dislocation 42,43 and microstructure evolution. 44 The computational speed is an important aspect when performing long rollouts that would take hundreds of thousands of function evaluations.…”

Section: And An Mlp Mmentioning

confidence: 99%

Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model

Hsu,

Sadigh,

Bulatov

et al. 2024

J. Chem. Theory Comput.

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We propose score dynamics (SD), a general framework for learning accelerated evolution operators with large timesteps from molecular dynamics (MD) simulations. SD is centered around scores or derivatives of the transition log-probability with respect to the dynamical degrees of freedom. The latter play the same role as force fields in MD but are used in denoising diffusion probability models to generate discrete transitions of the dynamical variables in an SD time step, which can be orders of magnitude larger than a typical MD time step. In this work, we construct graph neural network-based SD models of realistic molecular systems that are evolved with 10 ps timesteps. We demonstrate the efficacy of SD with case studies of the alanine dipeptide and short alkanes in aqueous solution. Both equilibrium predictions derived from the stationary distributions of the conditional probability and kinetic predictions for the transition rates and transition paths are in good agreement with MD. Our current SD implementation is about 2 orders of magnitude faster than the MD counterpart for the systems studied in this work. Open challenges and possible future remedies to improve SD are also discussed.

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Section: And An Mlp Mmentioning

confidence: 99%

Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model

Hsu,

Sadigh,

Bulatov

et al. 2024

J. Chem. Theory Comput.

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“…We largely adopt the MeshGraphNet of references [34,35], a type of message-passing graph neural network. We used 48 hidden features in 2D and 64 in 3D.…”

Section: Message-passing Graph Neural Networkmentioning

confidence: 99%

“…Following [34,35], our GNN starts with vertex and edge encoders ENC V , ENC E transforming concatenated input features into a latent space:…”

Section: Message-passing Graph Neural Networkmentioning

confidence: 99%

“…Simulating complex physics with GNNs is becoming an attractive alternative to CNN-based surrogate models in mesh-based fluid simulations [27,34]. MeshGraphNet (MGN) [34], a recent GNN variant, has proved well suited for representation of complex physics [35,36]. In an MGN, the values of each vertex is passed to its nearest connected neighbors through some learned function in the message passing layer (MPL), with multiple MPLs creating a local spatial awareness.…”

Section: Introductionmentioning

confidence: 99%

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Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution

Fan,

Hitt,

Tang

et al. 2024

Mach. Learn.: Sci. Technol.

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Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.

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“…The dislocations are then represented as random walkers which jump between neighboring nodes following a Poisson process. More recently, Bertin and Zhou [33] developed a Graph Neural Network (GNN) approach to model DDD, with the aim of replacing its time-integration procedure. They demonstrated that this DDD-GNN approach can learn ground-truth (GT) DDD data of a dislocation gliding in a őeld of obstacles.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning

Tian,

Bagchi,

Myhill

et al. 2024

Preprint

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Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

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Accelerating discrete dislocation dynamics simulations with graph neural networks

Cited by 10 publications

References 46 publications

Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model

Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model

Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning

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