Reese E. Jones
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View article: Metal oxide candidates for thermochemical water splitting obtained with a generative diffusion model
Metal oxide candidates for thermochemical water splitting obtained with a generative diffusion model Open
Generative diffusion models (DMs) for inorganic crystalline materials are being actively investigated for their potential to expand the chemical and structural design spaces for known functional materials. Generative candidates are particu…
View article: Metal oxide candidates for thermochemical water splitting obtained with a generative diffusion model
Metal oxide candidates for thermochemical water splitting obtained with a generative diffusion model Open
Generative diffusion models for inorganic crystalline materials are being actively investigated for their potential to expand the chemical and structural design spaces for known functional materials. Generative candidates are particularly …
View article: A General, Automated Method for Building Structural Tensors of Arbitrary Order for Anisotropic Function Representations
A General, Automated Method for Building Structural Tensors of Arbitrary Order for Anisotropic Function Representations Open
We present a general, constructive procedure to find the basis for tensors of arbitrary order subject to linear constraints by transforming the problem to that of finding the nullspace of a linear operator. The proposed method utilizes sta…
View article: Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials
Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials Open
Multi-material 3D printing, particularly through polymer jetting, enables the fabrication of digital materials by mixing distinct photopolymers at the micron scale within a single build to create a composite with tunable mechanical propert…
View article: Thermodynamic Modeling of Complex Solid Solutions in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>Lu</mml:mi></mml:math>-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mrow><mml:mi mathvariant="normal">H</mml:mi></mml:mrow></mml:mrow></mml:math>-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mrow><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:mrow></mml:math> System via Graph Neural Network Accelerated Monte Carlo Simulations
Thermodynamic Modeling of Complex Solid Solutions in the -- System via Graph Neural Network Accelerated Monte Carlo Simulations Open
Metal hydrides are important across diverse applications, such as hydrogen storage, batteries, gas sensors, nuclear reactions, and high-temperature superconductivity. Previous computational studies of metal hydrides under extreme pressures…
View article: Input Specific Neural Networks
Input Specific Neural Networks Open
The black-box nature of neural networks limits the ability to encode or impose specific structural relationships between inputs and outputs. While various studies have introduced architectures that ensure the network's output adheres to a …
View article: An attention-based neural ordinary differential equation framework for modeling inelastic processes
An attention-based neural ordinary differential equation framework for modeling inelastic processes Open
To preserve strictly conservative behavior as well as model the variety of dissipative behavior displayed by solid materials, we propose a significant enhancement to the internal state variable-neural ordinary differential equation (ISV-NO…
View article: A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity
A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity Open
This paper proposes a new approach for the calibration of material parameters in local elastoplastic constitutive models. The calibration is posed as a constrained optimization problem, where the constitutive model evolution equations for …
View article: Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Open
We propose a Stein variational gradient descent method to concurrently sparsify, train, and provide uncertainty quantification of a complexly parameterized model such as a neural network. It employs a graph reconciliation and condensation …
View article: BeyondFingerprinting: AI-guided discovery of robust materials & processes
BeyondFingerprinting: AI-guided discovery of robust materials & processes Open
BeyondFingerprinting was a 2021-2024 Sandia Grand Challenge LDRD exploring the potential to develop new resilient materials and manufacturing processes by taking an artificial-intelligence (AI)-guided approach that integrates human-subject…
View article: Accurate data‐driven surrogates of dynamical systems for forward propagation of uncertainty
Accurate data‐driven surrogates of dynamical systems for forward propagation of uncertainty Open
Stochastic collocation (SC) is a well‐known non‐intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for full‐field uncertainty propagation that characterizes the d…
View article: Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models Open
Most scientific machine learning (SciML) applications of neural networks involve hundreds to thousands of parameters, and hence, uncertainty quantification for such models is plagued by the curse of dimensionality. Using physical applicati…
View article: Multiscale simulation of spatially correlated microstructure via a latent space representation
Multiscale simulation of spatially correlated microstructure via a latent space representation Open
When deformation gradients act on the scale of the microstructure of a part due to geometry and loading, spatial correlations and finite-size effects in simulation cells cannot be neglected. We propose a multiscale method that accounts for…
View article: Polyconvex neural network models of thermoelasticity
Polyconvex neural network models of thermoelasticity Open
Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonlinear data and their ability to incorporate constitutive co…
View article: Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response
Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response Open
Composite materials with different microstructural material symmetries are common in engineering applications where grain structure, alloying and particle/fiber packing are optimized via controlled manufacturing. In fact these microstructu…