Nonstationarity Analysis of Materials Microstructures via Fisher Score Vectors

Zhang, Kungang; Apley, Daniel W.; Chen, Wei

doi:10.1016/j.actamat.2021.116818

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…Note that the ML method is shifting the paradigm of materials research in recent years, as it could rapidly accelerate the materials discovery and shed light on the structureproperty relation in complex systems. [41][42][43][44][45][46] For those relevant to PCM, Sosso et al developed ML potentials to understand the functional properties in phase-change materials 47 ; Konstantinou et al took advantage of the ML trained potential to generate multiple amorphous models, so that the MGS in Ge 2 Sb 2 Te 5 glass could be revealed 39 ; Kusne et al have proposed a real-time closedloop active learning method for materials exploration and optimization, and led to the discovery of several new phase-change materials. 48 In our work, the brain-like deep learning algorithm allows us to trace the structural origin of defect states, predicting the electronic structure in any atomic models without expensive energy band calculations.…”

Section: Introductionmentioning

confidence: 99%

“…We could use this method to screen effective structural features that determine MGS, so that the defect states or even the OTS behavior can be predicted to guide the discovery of new materials. Note that the ML method is shifting the paradigm of materials research in recent years, as it could rapidly accelerate the materials discovery and shed light on the structure–property relation in complex systems 41–46 . For those relevant to PCM, Sosso et al developed ML potentials to understand the functional properties in phase‐change materials 47 ; Konstantinou et al took advantage of the ML trained potential to generate multiple amorphous models, so that the MGS in Ge 2 Sb 2 Te 5 glass could be revealed 39 ; Kusne et al have proposed a real‐time closed‐loop active learning method for materials exploration and optimization, and led to the discovery of several new phase‐change materials 48 .…”

Section: Introductionmentioning

confidence: 99%

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Deep machine learning unravels the structural origin of mid‐gap states in chalcogenide glass for high‐density memory integration

Miao

2022

InfoMat

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The recent development of three-dimensional semiconductor integration technology demands a key component-the ovonic threshold switching (OTS) selector to suppress the current leakage in the high-density memory chips. Yet, the unsatisfactory performance of existing OTS materials becomes the bottleneck of the industrial advancement. The sluggish development of OTS materials, which are usually made from chalcogenide glass, should be largely attributed to the insufficient understanding of the electronic structure in these materials, despite of intensive research in the past decade. Due to the heavy first-principles computation on disordered systems, a universal theory to explain the origin of mid-gap states (MGS), which are the key feature leading to the OTS behavior, is still lacking. To avoid the formidable computational tasks, we adopt machine learning method to understand and predict MGS in typical OTS materials. We build hundreds of chalcogenide glass models and collect major structural features from both short-range order (SRO) and medium-range order (MRO) of the amorphous cells. After training the artificial neural network using these features, the accuracy has reached ~95% when it recognizes MGS in new glass. By analyzing the synaptic weights of the input structural features, we discover that the bonding and coordination environments from SRO and particularly MRO are closely related to MGS. The trained model could be used in many other OTS chalcogenides after minor modification. The intelligent machine learning allows us to understand the OTS mechanism from vast amount of structural data without heavy computational tasks, providing a new strategy to design functional amorphous materials from first principles.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep machine learning unravels the structural origin of mid‐gap states in chalcogenide glass for high‐density memory integration

Miao

2022

InfoMat

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Simulation-free determination of microstructure representative volume element size via Fisher scores

Liu,

Mojumder,

Liu

et al. 2024

APL Machine Learning

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A representative volume element (RVE) is a reasonably small unit of microstructure that can be simulated to obtain the same effective properties as the entire microstructure sample. Finite element (FE) simulation of RVEs, as opposed to much larger samples, saves computational expenses, especially in multiscale modeling. Therefore, it is desirable to have a framework that determines the RVE size prior to FE simulations. Existing methods select the RVE size based on when the FE-simulated properties of samples of increasing sizes converge with insignificant statistical variations, with the drawback being that many samples must be simulated. We propose a simulation-free alternative that determines the RVE size based only on a micrograph. The approach utilizes a machine learning model trained to implicitly characterize the stochastic nature of the input micrograph. The underlying rationale is to view RVE size as the smallest moving window size for which the stochastic nature of the microstructure within the window is stationary as the window moves across a large micrograph. For this purpose, we adapt a recently developed Fisher score-based framework for microstructure nonstationarity monitoring. Because the resulting RVE size is based solely on the micrograph and does not involve any FE simulation of specific properties, it constitutes an RVE for any property of interest that solely depends on the microstructure characteristics. Through numerical experiments of simple and complex microstructures, we validate our approach and show that our selected RVE sizes are consistent with when the chosen FE-simulated properties converge.

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Nonstationarity Analysis of Materials Microstructures via Fisher Score Vectors

Cited by 2 publications

References 41 publications

Deep machine learning unravels the structural origin of mid‐gap states in chalcogenide glass for high‐density memory integration

Deep machine learning unravels the structural origin of mid‐gap states in chalcogenide glass for high‐density memory integration

Simulation-free determination of microstructure representative volume element size via Fisher scores

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