Unraveling the size fluctuation and shrinkage of nanovoids during in situ radiation of Cu by automatic pattern recognition and phase field simulation

Nasim, M.; Sreekar, A.R.G.; Niu, Tongjun; Fan, Cuncai; Shang, Zhongxia; Li, Jin; Wang, Haiyan; El-Azab, Anter; Xue, Yexiang; Zhang, X.

doi:10.1016/j.jnucmat.2022.154189

Cited by 2 publications

(4 citation statements)

References 49 publications

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“…Scalable Analysis of in situ Experiment Video Data. Our PHASE-FIELD-LAB has been deployed in the real world to efficiently analyze high volume the experimental in situ video data (Nasim et al 2023;Niu et al 2020Niu et al , 2021. Manual annotation of a single 10-minute in situ video can take upto 3.75 months (Xue et al 2021).…”

Section: Impact Of Phase-field-lab In Scientific Discoverymentioning

confidence: 99%

“…Discovery of Nano-void Defect Size Fluctuation under Heavy Ion Irradiation. Detail analysis of in situ video data with our PHASE-FIELD-LAB has led to the discovery of void defect size fluctuation in metallic materials under high temperature and irradiation (Nasim et al 2023). Previously, partial analysis of the in situ video data by manual efforts revealed that the void defects size decreases monotonically during irradiation (Fan et al 2019;Chen et al 2015).…”

Section: Impact Of Phase-field-lab In Scientific Discoverymentioning

confidence: 99%

“…PHASE-FIELD-LAB is currently being used by material scientists for analyzing material defects evolution in metallic materials, such as the void defects shown in Figure 2. Using our tool provides the researchers the ability to scale up the analysis process (Niu et al 2020), which in turn has led to the discovery of new physics phenomena of the size fluctuations of the void defects in metal (Cu) under irradiation and high temperature (Nasim et al 2023).…”

Section: Introductionmentioning

confidence: 99%

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End-to-End Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning

Nasim,

Zhang,

El-Azab

et al. 2024

AAAI

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The availability of tera-byte scale experiment data calls for AI driven approaches which automatically discover scientific models from data. Nonetheless, significant challenges present in AI-driven scientific discovery: (i) The annotation of large scale datasets requires fundamental re-thinking in developing scalable crowdsourcing tools. (ii) The learning of scientific models from data calls for innovations beyond black-box neural nets. (iii) Novel visualization & diagnosis tools are needed for the collaboration of experimental and theoretical physicists, and computer scientists. We present Phase-Field-Lab platform for end-to-end phase field model discovery, which automatically discovers phase field physics models from experiment data, integrating experimentation, crowdsourcing, simulation and learning. Phase-Field-Lab combines (i) a streamlined annotation tool which reduces the annotation time (by ~50-75%), while increasing annotation accuracy compared to baseline; (ii) an end-to-end neural model which automatically learns phase field models from data by embedding phase field simulation and existing domain knowledge into learning; and (iii) novel interfaces and visualizations to integrate our platform into the scientific discovery cycle of domain scientists. Our platform is deployed in the analysis of nano-structure evolution in materials under extreme conditions (high temperature and irradiation). Our approach reveals new properties of nano-void defects, which otherwise cannot be detected via manual analysis.

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Section: Impact Of Phase-field-lab In Scientific Discoverymentioning

confidence: 99%

Section: Impact Of Phase-field-lab In Scientific Discoverymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

End-to-End Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning

Nasim,

Zhang,

El-Azab

et al. 2024

AAAI

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show abstract

“…A classic example was the SKI-CAT project (Fayyad et al, 1993), which used an induced decision tree to distinguish stars from galaxies in astronomical surveys based on features derived by image processing. More recent instances have used convolutional neural networks to learn classifiers from images in biology (Sarvamangala and Kulkarni, 2022), materials science (Nasim et al, 2023), and other disciplines. These are not interpretable, but their outputs support distributional analyses, which a higherlevel system can use to test models and theories.…”

Section: Measuring and Identifying Variablesmentioning

confidence: 99%

Integrated Systems for Computational Scientific Discovery

Langley

2024

AAAI

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This paper poses the challenge of developing and evaluating integrated systems for computational scientific discovery. We note some distinguishing characteristics of discovery tasks, examine eight component abilities, review previous successes at partial integration, and consider hurdles the AI research community must leap to transform the vision for integrated discovery into reality. In closing, we discuss promising scientific domains in which to test such computational artifacts.

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Unraveling the size fluctuation and shrinkage of nanovoids during in situ radiation of Cu by automatic pattern recognition and phase field simulation

Cited by 2 publications

References 49 publications

End-to-End Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning

End-to-End Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning

Integrated Systems for Computational Scientific Discovery

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