Harnessing structural stochasticity in the computational discovery and design of microstructures

Xu, Leidong; Hoffman, Nathaniel; Wang, Zihan; Xu, Hongyi

doi:10.1016/j.matdes.2022.111223

Cited by 20 publications

(5 citation statements)

References 81 publications

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“…In recent years, machine learning (ML) has actively integrated into the fields of chemistry and materials science, opening fundamentally new opportunities for designing new compounds/materials or functionalities. Thus, materials informatics methodologies have been successfully applied for the rational screening of compounds with tailored characteristics [99][100][101][102][103], predicting crystal structures [104], designing experiments [105][106][107], using natural language processing for experimental data acquisition and analysis [108,109], analyzing data from physicochemical characterization methods [110,111], and microstructural informatics [112][113][114].…”

Section: Machine Learning Modeling and Data Analysismentioning

confidence: 99%

Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning

Kireeva,

Tsivadze,

Pervov

2023

Batteries

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All-solid-state batteries (ASSBs) are the important attributes of the forthcoming technologies for electrochemical energy storage. A key element of ASSBs is the solid electrolyte materials. Garnets are considered promising candidates for solid electrolytes of ASSBs due to their chemical stability with Li metal anodes, reasonable kinetic characteristics (σLi∼ 10−3–10−4 S · cm−1) and a wide electrochemical window. This study is aimed at the analysis of the experimental data available for garnet thin films, examining the ionic conductivity through the film/substrate lattice mismatch, the elastic properties and the difference in the thermal expansion characteristics of the film and the substrate, the deposition temperature of the film, and the melting point and the dielectric constant of the substrate. Based on the results of this analysis and by introducing the corresponding characteristics involved as the descriptors, the quantitative models for predicting the ionic conductivity values were developed. Some important characteristic features for ion transport in garnet films, which are primarily concerned with the film/substrate misfit, elastic properties, deposition temperature, cation segregation and the space charge effects, are discussed.

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Section: Machine Learning Modeling and Data Analysismentioning

confidence: 99%

Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning

Kireeva,

Tsivadze,

Pervov

2023

Batteries

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“…Materials informatics was emphasized as an efficient way towards the rational synthesis of new compounds and new properties. It was efficiently applied in the design of the materials with desired characteristics [5,6,7,8,9], in the design of synthesis and for the autonomous laboratories [10,11,12,13], for natural language processing to obtain and analyze experimental data in chemistry and materials science [14,15,16,17,18], for modeling the microstructure [19,20], for the analysis of the output of physicochemical methods of characterization [21,22], for inverse design of materials [23,24] and in many other areas of their application.…”

Section: Introductionmentioning

confidence: 99%

Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes

Kireeva,

Tsivadze

2024

Preprint

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Recent decades have shown arising growth-on-demand of integrating the machine learning into all areas of chemistry and materials science. In this study, we consider one of the aspects of applying these technologies to gain advantage in the search for new knowledge extracted from experimental data obtained in ever-growing number of studies. The novelty detection approaches are aimed to identify the artefacts in these data that may be of importance in many direc- tions. The analysis of "outliers" in details of the synthesis in the research studies of garnet-structured solid electrolytes was chosen as the object of demonstration of one of the practical applications of this methodology. Particular attention was paid to the choice of precursors. The thermodynamic data such as the heat of formation from the pure oxides as well as the results of drop solution calorimetry for simple oxides were involved as the descriptors of the studied systems. The overall performance of novelty/outlier detection of all types of outliers was characterized for the data described varying the complexity of description using ROC-AUC statistics and was assessed to be 0.71 – 0.72 using the Area-Under-Curve statistics. It was found that all “outlier” compounds related to those as the result of using the rare precursors in synthesis were successfully identified. The complementary regression analysis was performed to elucidate the relationship between the data diversity and the complexity of data description.

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“…This approach is conducted in many fields to reduce the computational resources for composite microstructures and lattice structures, protein structures, and origami/kirigami structures [40][41][42]. Beyond the prediction of stress-strain response, there have been numerous efforts to investigate the use of DL models for generating microstructures that have specific desired morphology [43][44][45][46][47][48][49][50][51], as well as models that can predict mechanical and thermal behavior based on the microstructure geometry [46,52].…”

Section: Introductionmentioning

confidence: 99%

“…They showed that their design framework enables controlling microstructural features in a continuous space within the latent space of VAE. Xu et al [45] also exhibited that the morphology and the stochasticity of various microstructures (e.g., random fiber, particle, and ellipse) can be controlled using their trained VAE-based models. Meanwhile, a significant drawback of VAE is that the generated samples are often distorted and blurred, which can result in the quality of the samples not meeting desired standards [55,56].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Framework for Designing Microstructure of Multifunctional Composites with Deep-Learned Diffusion-Based Generative Models

Lee

Lim

Yun

2023

Preprint

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This paper puts forward a novel integrated microstructure design methodology that replaces the common existing design approaches for multifunctional composites: 1) reconstruction of microstructures, 2) analyzing and quantifying material properties, and 3) inverse design of materials using the diffusion-based generative model (DGM). The problem of microstructure reconstruction is addressed using DGM, which is a new state-of-the-art generative model formulated with a forward Markovian diffusion process and the reverse process. Then, the conditional formulation of DGM is introduced for guidance to the embedded desired material properties with a transformer-based attention mechanism, which enables the inverse design of multifunctional composites. A convolutional neural network (CNN)-based surrogate model is utilized to facilitate the prediction of nonlinear material properties for building microstructure-property linkages. Combined, the proposed artificial intelligence-based design framework enables large data processing and database construction that is often not affordable with resource-intensive finite element method (FEM)-based direct numerical simulation (DNS) and iterative reconstruction methods. What is important is that the proposed DGM-based methodology is not susceptible to unstable training or mode collapse, which are common issues in neural network models that are often difficult to address even with extensive hyperparameter tuning. An example case is presented to demonstrate the effectiveness of the proposed approach, which is designing mechanoluminescence (ML) particulate composites made of europium and dysprosium ions. The results show that the inversely-designed multiple ML microstructure candidates with the proposed generative and surrogate models meet the multiple design requirements (e.g., volume fraction, elastic constant, and light sensitivity). The evaluation of the generated samples' quality and the surrogate models' performance using appropriate metrics are also included. This assessment demonstrates that the proposed integrated methodology offers an end-to-end solution for practical material design applications.

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Harnessing structural stochasticity in the computational discovery and design of microstructures

Cited by 20 publications

References 81 publications

Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning

Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning

Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes

A Data-Driven Framework for Designing Microstructure of Multifunctional Composites with Deep-Learned Diffusion-Based Generative Models

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