Robots can classify household materials using spectroscopy.
problem Estimating the material of household objects during manipulation.
method Used spectroscopy to estimate material classification of 50 flat objects.
result Achieved 94.6% material classification accuracy with one spectral sample per object.
New stable distance for classifying materials from point cloud data.
problem Classifying materials from noisy and sparse data.
method A new distance on persistence diagrams for matching and comparing topological features.
result Stability of the new distance provides theoretical justification for its use in materials classification.
Improved online classification for manual material handling using wearable sensors.
problem Online monitoring of manual material handling activities using wearable sensors.
method Optimizes dictionary learning to improve sparse representation classification (SRC) accuracy and computational efficiency.
result Proposed method outperforms benchmark methods in accuracy and computational time for online monitoring.
Framework automates microstructure image analysis for materials science.
problem Complex microstructures in materials require automated analysis.
method Combines unsupervised and supervised learning for classification and segmentation.
result Framework can automatically segment and classify micrographs.
A geometrical interpretation of the G-structures associated to elastic material bodies is given. In addition, characterizations of their integrability are obtained. Since the lack of integrability is a geometrical measure of the lack of homogeneity, the corresponding inhomogeneity conditions are obtained
Deep learning improves classification and characterization of amorphous materials.
problem Challenges in quantifying structure-property relationships and identifying structural features in amorphous materials.
method Application of convolutional neural networks and message passing neural networks to molecular dynamics simulations.
result Message passing neural networks outperform convolutional neural networks in classifying and characterizing amorphous materials.
Machine learning predicts superconducting critical temperatures.
problem Understanding the relationship between superconductivity and material properties.
method Developed classification and regression models using machine learning.
result Models accurately predict superconducting critical temperatures and identify new materials.
Deep learning improves material recognition in construction monitoring.
problem Varying illuminations and low accuracy rates in material classification.
method Deep learning methods, including convolutional neural networks, were used to classify and recognize materials in construction sites.
result Achieved 97.35% accuracy rate for material classification.
Transfer learning improves model accuracy on sparse materials datasets.
problem Irreducible errors in analyses due to differing measurements across datasets.
method Three transfer learning techniques: multi-task, difference, and explicit latent variable architectures.
result Explicit latent variable method is most accurate for activation energies of NO reduction steps.
Model predicts DFT formation energies without atomic positions.
problem Fast prediction of material properties without atomic positions.
method Symmetry-labeled graphs and message passing neural network.
result Mean absolute error below 0.1 eV for selenides.
Machine learning predicts failure in brittle materials with high accuracy.
problem Predicting failure in brittle materials under repetitive loads.
method Phase-field model combined with supervised machine learning.
result Framework predicts failure with acceptable accuracy even in noisy data.
Model predicts multiple material properties with reduced error.
problem Limited materials data and lack of universal material descriptors.
method Integrates CGCNN with multi-task learning.
result Reduces test error by up to 8% for correlated properties.
Study compares feature selection methods for stress hotspot classification.
problem Optimizing feature selection for stress hotspot classification in materials.
method Applied various feature selection methods to microstructural data.
result Demonstrated some feature selection techniques are biased, highlighting a preferred method.
Machine learning predicts perovskite formability and classifies crystal structures.
problem Predicting and classifying perovskite formability and crystal structures.
method Machine learning, specifically Random Forest, with 5-fold cross-validation.
result 98.57% accuracy in predicting perovskite formability and 90.53% in classifying crystal structures.
Unified framework for non-uniform materials evolving over time.
problem Dealing with non-uniform materials evolving over time.
method Constructing a material groupoid and material distribution.
result Unified framework for general non-uniform evolution materials.
Generative models accelerate chemical design from properties to structures.
problem Expensive and incremental strategies for optimizing chemical properties.
method Review of current deep generative models and their application to molecular systems.
result Generative models can expedite the design of novel useful compounds.
Paper proposes a new method for designing materials using deep learning.
problem Designing high-performance material distributions from given distributions.
method Iterative process of selecting, generating, and merging material distributions using a deep generative model.
result The method improves material performance through iterative refinement.
New material groupoid theory subdivides non-uniform bodies into smoothly uniform parts and isolated points.
problem Lack of differentiability in material bodies leads to non-uniformity.
method Introducing material groupoid and material distribution to study non-uniform bodies rigorously.
result Material bodies can be subdivided into smoothly uniform parts and isolated points.
DECT-MULTRA improves material decomposition in CT images.
problem Noise and artifacts degrade material images in DECT imaging.
method Combines PWLS estimation with MULTRA model for efficient clustering and sparse coding.
result Superior material image quality and decomposition accuracy compared to other methods.
MatGAN uses GAN to efficiently generate new inorganic materials.
problem Efficiently searching the vast chemical design space for new materials.
method Generative adversarial network (GAN) trained on ICSD materials database.
result 92.53% novelty and 84.5% chemically valid samples generated.
Ordinal data are often seen in real applications. Regular multicategory classification methods are not designed for this data type and a more proper treatment is needed. We consider a framework of ordinal classification which pools the results from binary classifiers together. An inherent difficulty of this framework i…
Deep learning model reconstructs material microstructures from feature representations.
problem Reconstructing complex material microstructures accurately and efficiently.
method Convolutional deep belief network for automated feature learning and dimension reduction.
result Material reconstructions preserve microstructural features and material properties.
Predicts fracture evolution and material failure in brittle materials.
problem Predicting how fractures propagate and materials fail in brittle materials.
method Recurrent graph convolutional neural networks trained on simulation data.
result Predictions within 3% for fracture damage and 15% for time to failure.
System learns user preferences to synthesize materials quickly.
problem Slow material synthesis for novice and expert users.
method Gaussian Process Regression for user preferences, neural network for real-time image predictions.
result Real-time material synthesis enables novice users to generate hundreds of models.
Material properties linked to Lie groupoids and algebroids in continuum mechanics.
problem Understanding material properties through groupoids and algebroids.
method Intuitive treatment of material groupoids and their algebroids.
result Material groupoids and algebroids are linked to material properties and their deformations.
A Lie groupoid, called \textit{material Lie groupoid}, is associated in a natural way to any elastic material. The corresponding Lie algebroid, called \textit{material algebroid}, is used to characterize the uniformity and the homogeneity properties of the material. The relation to previous results in terms of G−stru…
Detects radical content on Twitter using textual, psychological, and behavioral signals.
problem Limit the spread of extremist narratives on social media.
method Analyzed extremist material, created contextual text-based model, inferred psychological properties, evaluated on Twitter.
result Radical users exhibit distinguishable textual, psychological, and behavioral properties.
Study improves material similarity measures considering distinctiveness.
problem Improving similarity measures for materials science applications.
method Used machine learning techniques with specific descriptors and kernels.
result Minimizing loss of distinctiveness improves prediction accuracy.
Calibrating materials to engineering targets improves wind turbine design.
problem Separate material and mechanical design processes lead to uncertainty.
method Repurpose calibration techniques to integrate material and mechanical design.
result Materials can be designed with specific engineering targets in mind.
Explains Thurston geometries and visualization techniques.
problem Classifying Thurston geometries and their visualization.
method Survey of history and recent techniques.
result Discussion of recent immersive visualization techniques.
Robots learn material recognition from unlabeled data using GANs.
problem Difficulties in collecting labeled training data for robots.
method Semi-supervised learning with GANs for haptic features.
result Achieves ~90% accuracy in material estimation with 92% unlabeled data.
Nonsingular estimation of high dimensional covariance matrices is an important step in many statistical procedures like classification, clustering, variable selection an future extraction. After a review of the essential background material, this paper introduces a technique we call slicing for obtaining a nonsingular …
Deep learning model predicts material microstructures from processing methods.
problem Linking processing conditions to material properties for material design.
method Conditional image synthesis using Wasserstein GAN with gradient penalty.
result Deep learning model synthesizes high-quality microstructures for given cooling methods.
New algorithm improves materials discovery using max K-Armed Bandit.
problem Maximizing material breakthroughs in materials discovery.
method Proposed a search algorithm based on max K-Armed Bandit (MKB) for materials discovery.
result Stable performance in late search stages, outperforming other bandit algorithms.
IRNet improves material property prediction from composition and crystal structure.
problem Predicting material properties from composition and crystal structure.
method Deep residual regression network with individual residual learning.
result IRNet outperforms state-of-the-art machine learning approaches in predicting material properties.
New method integrates latent variables for Bayesian Optimization of materials with both qualitative and quantitative factors.
problem Bayesian Optimization for materials design with mixed qualitative and quantitative variables.
method Integrates latent variables for mixed-variable Gaussian process modeling within the Bayesian Optimization framework.
result LVGP provides superior modeling accuracy compared to existing methods for mixed-variable problems.
Deep neural networks predict material properties from images.
problem Tailoring material properties for advanced turbomachinery.
method Developed deep convolutional neural networks to predict processing-structure-property relations.
result Models accurately predict material properties from images, surpassing current methods.
The paper introduces new uniformity and homogeneity concepts for Cosserat media.
problem Characterizing uniformity and homogeneity in Cosserat media.
method Using groupoids and smooth distributions, the authors derive three canonical equations to characterize uniformity and homogeneity.
result The paper provides a unique and maximal division of Cosserat media into uniform and second-grade parts.
FlowLLM uses LLMs and flow matching to efficiently generate novel materials.
problem Challenging material discovery due to vast chemical space.
method Combines LLMs and Riemannian flow matching to design novel crystalline materials.
result Significantly increases generation rate of stable materials and unique crystals.
4-dim intrinsic (material) Riemannian metric G of the material 4-D space-time continuum P is utilized as the characteristic of the aging processes developing in the material. Manifested through variation of basic material characteristics such as density, moduli of elasticity, yield stress, strength, and toughness.,…
Machine learning optimizes polymer fiber synthesis.
problem Complex material synthesis requires impractical experimentation.
method Bayesian optimisation using machine learning.
result Efficiently directs synthesis to achieve material and process objectives.
Review of automation's role in chemical discoveries.
problem Improving autonomous discovery in chemistry.
method Classification of discovery types, assessment of autonomy, case studies.
result Rapid advancements in automation and machine learning are transforming experimentation and modeling.
New ML framework for reliable and explainable material predictions.
problem Challenges in applying ML to materials science, especially with imbalanced data.
method Proposes a general-purpose explainable and reliable machine-learning framework using ensembles of simpler models.
result Demonstrates improved reliability and explainability in material property predictions.
Study uniformity of material bodies using groupoids.
problem Understanding uniformity and homogeneity of material bodies.
method Using groupoids and algebraic subgroupoids to analyze material bodies.
result Uniform material bodies can be covered by transitive material foliations and their leaves generate transitive Lie groupoids.
Method reveals dissimilarity in alloys' Curie temperatures.
problem Tackles the dissimilarity between rare-earth transition metal binary alloys.
method Ensemble learning with Kernel ridge regression.
result Reveals meaningful relations between alloys' structure and Curie temperature.
We enhance autonomous materials research with problem-aware models.
problem Complex decision-making in autonomous materials.
method Bayesian framework, machine learning, physics-based models, operational considerations.
result Improved models reflect problem-specific structure.
A new method optimizes material discovery by balancing exploration and exploitation.
problem Substantial experimental costs and lengthy development periods in material discovery.
method Threshold-Driven UCB-EI Bayesian Optimization (TDUE-BO) method.
result TDUE-BO significantly outperforms traditional BO methods in material discovery.
Deep learning speeds up material property quantification using stress waves.
problem Quantifying material properties from stress waves in complex media.
method Surrogate deep learning FWI scheme trained on random sampled properties and local minima.
result Demonstrates feasibility of deep learning for high-accuracy material property estimation.