On the importance of microstructure information in materials design: PSP vs PP

Molkeri, Abhilash; Khatamsaz, Danial; Couperthwaite, Richard; James, Jaylen; Arróyave, Raymundo; Allaire, Douglas; Srivastava, Ankit

doi:10.1016/j.actamat.2021.117471

Cited by 17 publications

(4 citation statements)

References 43 publications

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“…Molkeri et al. [ 5 ] proved that incorporating microstructure knowledge into the materials design process results in better and faster solutions by proposing a novel microstructure‐aware framework. Normally, microstructure quantifiers such as grain orientation, precipitate size, and volume fraction [ 6,7 ] are regarded as potential descriptors to build SP linkage.…”

Section: Instructionmentioning

confidence: 99%

“…[1][2][3] With the guidance of physical metallurgy and information strategy, we [4] have previously demonstrated that the microstructure information can improve the prediction of hardness of austenitic steels. Molkeri et al [5] proved that incorporating microstructure knowledge into the materials design process results in better and faster solutions by proposing a novel microstructure-aware…”

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confidence: 99%

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Symbolic Regression and Two‐Point Statistics Assisted Structure‐Property Linkage Based on Irregular‐Representative Volume Element

Chen

Zhao

et al. 2022

Advcd Theory and Sims

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Quantifying the microstructure of materials is of significance in material development, especially for building the relationship between structure and property. To establish a remarkable structure‐property (SP) linkage, a novel concept referred to as irregular‐representative volume element (IRVE) based on panoramic image stitching technology (PIST) is proposed and a data‐driven scheme integrating irregular domain‐oriented two‐point statistics, principal component analysis (PCA), and symbolic regression based on genetic programming (GPSR) is constructed. Combining with advanced image processing and genetic programming technologies, this scheme improves the microstructure quantization framework. This scheme can not only be applied in different complex conditions for extracting the information of a material microstructure, but can also to embody details of microstructure from the perspective of large scale. IRVE is demonstrated to have both strong statistical representativeness and sufficient physical interpretation, which makes the scheme robust and reliable. Performing the scheme on an example of ferrite heat‐resistant steels, it shows a powerful ability in building an equational SP linkage with high precision (R = 0.91, RMSE = 13.17), the generalization ability of the linkage is also validated by an unseen steel (relative percentage error is 2.66%). The scheme has bright application prospects in predicting mechanical property and accelerating alloy design.

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Section: Instructionmentioning

confidence: 99%

mentioning

confidence: 99%

Symbolic Regression and Two‐Point Statistics Assisted Structure‐Property Linkage Based on Irregular‐Representative Volume Element

Chen

Zhao

et al. 2022

Advcd Theory and Sims

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“…Additionally, the capabilities of ML-aided analysis of big data seem to encourage researchers to again focus on process-property-correlations and shift away from process-microstructure-property-links. Yet the microstructure is an essential pillar of materials design [12] and its incorporation into materials design processes is significant, with process-microstructure-property relationships being more relevant than process-property only relationships [13]. Accordingly, microstructure analysis and quantification are relevant in many different respects: the optimization of new materials with regard to their microstructure, the quality control of existing materials and the continuous microstructure-based improvement of existing materials.…”

Section: Introductionmentioning

confidence: 99%

Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures

Müller,

Stiefel,

Bachmann

et al. 2024

Metals

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The foundation of materials science and engineering is the establishment of process–microstructure–property links, which in turn form the basis for materials and process development and optimization. At the heart of this is the characterization and quantification of the material’s microstructure. To date, microstructure quantification has traditionally involved a human deciding what to measure and included labor-intensive manual evaluation. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer exciting new approaches to microstructural quantification, especially classification and semantic segmentation. This promises many benefits, most notably objective, reproducible, and automated analysis, but also quantification of complex microstructures that has not been possible with prior approaches. This review provides an overview of ML applications for microstructure analysis, using complex steel microstructures as examples. Special emphasis is placed on the quantity, quality, and variance of training data, as well as where the ground truth needed for ML comes from, which is usually not sufficiently discussed in the literature. In this context, correlative microscopy plays a key role, as it enables a comprehensive and scale-bridging characterization of complex microstructures, which is necessary to provide an objective and well-founded ground truth and ultimately to implement ML-based approaches.

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“…cornerstone to regulate those desired properties for engineering applications as well as to discover new materials. [9][10][11] Since the features of composition, including chemical formulas and chemical bonds, are not yet digitized, both analytic composition-structure and composition-property relations wait for unveiling, while experimental observations and theoretical calculations just get a simple mapping relationship for materials.…”

Section: Introductionmentioning

confidence: 99%

Causal emergent principles and relations for mechanical properties of covalent and ionic crystals

Hu,

2024

AIP Advances

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A knowledge and data-synergized intelligent computation architecture for materials was proposed within the data science paradigm. As a vital operation, two digital ensemble descriptors implying chemical composition and structural trend for crystals were created using the features contained in the Periodic Table of elements without a priori assumption, which affords causal emergence and regulation principles for the mechanical response of covalent and ionic solids. In addition to a linear correlation among structural state/mechanical response parameters, causal analytic relations in an exponential form between structural and thermodynamic state/mechanical response parameters and a digital ensemble descriptor were unveiled through least squares regression, in which the coefficients are classified in accordance with symmetry principles on the atom and lattice. Thereafter, the underlying physicochemical mechanisms of chemical pressure and chemical bonding are found responsible for the mechanical responses of bulk modulus and hardness of solids. At last, a physical prediction model was established for crystalline solids and demonstrated the feasibility of the predictive design of novel superhard materials. It is believed that by constructing suitable digital ensemble descriptors, this intelligent computation architecture and consequent physical prediction models on the basis of causal analytic relations are able to generalize by depicting crystalline solids with covalent and ionic bonds in other crystallographic structures.

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On the importance of microstructure information in materials design: PSP vs PP

Cited by 17 publications

References 43 publications

Symbolic Regression and Two‐Point Statistics Assisted Structure‐Property Linkage Based on Irregular‐Representative Volume Element

Symbolic Regression and Two‐Point Statistics Assisted Structure‐Property Linkage Based on Irregular‐Representative Volume Element

Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures

Causal emergent principles and relations for mechanical properties of covalent and ionic crystals

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