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Exploring the Core-shell Structure of BaTiO3-based Dielectric Ceramics Using Machine Learning Models and Interpretability Analysis
A machine learning (ML)-based random forest (RF) classification model algorithm was employed to investigate the main factors affecting the formation...
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Deep learning based identification and interpretability research of traditional village heritage value elements: a case study in Hubei Province
The preservation and transmission of traditional villages is crucial to the prosperity and development of ethnic cultures. However, current...
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Enhancing interpretability in the exploration of high-energy conversion efficiency in CsSnBr3−xIx configurations using crystal graph convolutional neural networks and adversarial example methods
Crystal graph convolutional neural networks (CGCNNs) have revolutionized materials research by eliminating the need for manual feature engineering....
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Interpretable machine learning for materials design
Fueled by the widespread adoption of machine learning and the high-throughput screening of materials, the data-centric approach to materials design...
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Innovative Web Application Revolutionizing Disease Detection, Empowering Users and Ensuring Accurate Diagnosis
This paper presents an innovative enhancement aimed at revolutionizing disease detection and providing users with a reliable source of information...
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Interpretable Machine Learning
ML algorithms, and deep learning modelsModels more so, are notorious for their black-box nature providing little or no insights into the nature of...
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DFU_XAI: A Deep Learning-Based Approach to Diabetic Foot Ulcer Detection Using Feature Explainability
Diabetic foot ulcer (DFU) is a potentially fatal complication of diabetes. Traditional techniques of DFU analysis and therapy are more time-consuming...
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CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry
Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and...
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Pretraining of attention-based deep learning potential model for molecular simulation
Machine learning-assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation. With the...
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Phase classification of multi-principal element alloys via interpretable machine learning
There is intense interest in uncovering design rules that govern the formation of various structural phases as a function of chemical composition in...
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Model Refinement
ModelModels refinement is a critical process in machine learningMachine learning that aims to enhance the performance and generalization of...
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Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning
Data-driven materials science has realized a new paradigm by integrating materials domain knowledge and machine-learning (ML) techniques. However,...
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Identifying key features for predicting glass-forming ability of bulk metallic glasses via interpretable machine learning
Bulk metallic glasses (BMGs) have been receiving extensive attention in the community of physics and materials science due to their attractive...
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Machine Learned Material Simulation
This chapter explores the application of machine learningMachine learning techniques in materials simulations, with a focus on three key areas:...
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Interpretable learning of voltage for electrode design of multivalent metal-ion batteries
Deep learning (DL) has indeed emerged as a powerful tool for rapidly and accurately predicting materials properties from big data, such as the design...
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A Transformer and Random Forest Hybrid Model for the Prediction of Non-metallic Inclusions in Continuous Casting Slabs
Non-metallic inclusions (NMIs) in continuous casting slabs will significantly reduce the performance of final steel products and lead to other...
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Interpretable Machine Learning Method for Modelling Fatigue Short Crack Growth Behaviour
Interpretable machine learning (ML) has become a popular tool in the field of science and engineering. This research proposed a domain knowledge...
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Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy
Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds....
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An interpretable machine learning strategy for pursuing high piezoelectric coefficients in (K0.5Na0.5)NbO3-based ceramics
Perovskite-type lead-free piezoelectric ceramics allow access to illustrious piezoelectric coefficients ( d 33 ) through intricate composition design...
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Calcium-Treated Steel Cleanliness Prediction Using High-Dimensional Steelmaking Process Data
Control of calcium treatment in steel is challenging due to the reactivity of Ca and difficulty of measuring total oxygen of steel in-process to make...
