Data-driven plastic anisotropy predictions using crystal plasticity and deep learning models
Abstract not provided.
Publications
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Jump to search filtersUnderstanding and Modeling the Stochastic Nature of Corrosion Reactions in Atmospheric Conditions
Abstract not provided.
Leveraging Machine Learning to Increase Computational Efficiency in Electrochemical Systems: An Application to Galvanic Corrosion
Abstract not provided.
Data-driven plastic anisotropy predictions using crystal plasticity and deep learning models
Abstract not provided.
The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model
Abstract not provided.
Development of structure-property linkages for damage in crystalline microstructures using Bayesian inference and unsupervised learning
Abstract not provided.
Optimal Development of Transferable Machine Learning Interatomic Potentials using Active Learning
Abstract not provided.
Materials Innovation: Making the Future Possible
Abstract not provided.
Materials Data Driven Design (MAD3)
Abstract not provided.
The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model
Abstract not provided.
Training data selection for accuracy and transferability of interatomic potentials
npj Computational Materials
Advances in machine learning (ML) have enabled the development of interatomic potentials that promise the accuracy of first principles methods and the low-cost, parallel efficiency of empirical potentials. However, ML-based potentials struggle to achieve transferability, i.e., provide consistent accuracy across configurations that differ from those used during training. In order to realize the promise of ML-based potentials, systematic and scalable approaches to generate diverse training sets need to be developed. This work creates a diverse training set for tungsten in an automated manner using an entropy optimization approach. Subsequently, multiple polynomial and neural network potentials are trained on the entropy-optimized dataset. A corresponding set of potentials are trained on an expert-curated dataset for tungsten for comparison. The models trained to the entropy-optimized data exhibited superior transferability compared to the expert-curated models. Furthermore, the models trained to the expert-curated set exhibited a significant decrease in performance when evaluated on out-of-sample configurations.
The multifaceted uncertainty nature of structure-property linkage with crystal plasticity finite element model
Abstract not provided.
Investigating plastic anisotropy using crystal plasticity and deep learning models
Abstract not provided.
LDRD 226360 Final Project Report: Simulated X-ray Diffraction and Machine Learning for Optimizing Dynamic Experiment Analysis
This report is the final documentation for the one-year LDRD project 226360: Simulated X-ray Diffraction and Machine Learning for Optimizing Dynamic Experiment Analysis. As Sandia has successfully developed in-house X-ray diffraction tools for study of atomic structure in experiments, it has become increasingly important to develop computational analysis methods to support these experiments. When dynamically compressed lattices and orientations are not known a priori, the identification requires a cumbersome and sometimes intractable search of possible final states. These final states can include phase transition, deformation and mixed/evolving states. Our work consists of three parts: (1) development of an XRD simulation tool and use of traditional data science methods to match XRD patterns to experiments; (2) development of ML-based models capable of decomposing and identifying the lattice and orientation components of multicomponent experimental diffraction patterns; and (3) conducting experiments which showcase these new analysis tools in the study of phase transition mechanisms. Our target material has been cadmium sulfide, which exhibits complex orientation-dependent phase transformation mechanisms. In our current one-year LDRD, we have begun the analysis of high-quality c-axis CdS diffraction data from DCS and Thor experiments, which had until recently eluded orientation identification.
Estimating Uncertainty of Neural Network Predictions for Inelastic Mechanical Deformation using Coupled FEM-NN Approach
Abstract not provided.
Transformation mechanisms for the pressure-induced phase transition in single crystal CdS
Abstract not provided.
Simulated X-ray Diffraction and Machine Learning for Interpretation of Dynamic Compression Experiments
Abstract not provided.
Data-driven plastic anisotropy predictions using crystal plasticity and deep learning models
Abstract not provided.
Predicting plastic anisotropy using crystal plasticity and Bayesian neuralnetwork surrogate models
Abstract not provided.
Investigating plastic anisotropy using CP-FEM and ML techniques
Abstract not provided.
MADDD NM Technology Showcase Presentation
Abstract not provided.
Phase Transition Mechanisms in Cadmium Sulfide from X-ray Diffraction Comparisons of High-pressure Experiments and MD Simulation
Abstract not provided.
Accelerating Phase-field Predictions Via Surrogate Models Trained By Machine Learning Methods
Abstract not provided.
Mesoscale Simulation of Cold Spray Microstructure Formation
Abstract not provided.
Sandia / IBM Discussion on Machine Learning for Materials Applications [Slides]
This report includes a compilation of several slide presentations: 1) Interatomic Potentials for Materials Science and Beyond–Advances in Machine Learned Spectral Neighborhood Analysis Potentials (Wood); 2) Agile Materials Science and Advanced Manufacturing through AI/ML (de Oca Zapiain); 3) Machine Learning for DFT Calculations (Rajamanickam); 4) Structure-preserving ML discovery of a quantum-to-continuum codesign stack (Trask); and 5) IBM Overview of Accelerated Discovery Technology (Pitera)
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