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.
Study handles in 3D shapes, applies to material patterns.
problem Understanding 3D shapes through handlebody decompositions.
method Introduced handlebody decompositions, showed stability, applied to materials.
result Stable equivalence of handlebody decompositions in 3-manifolds.
RG-VFM extends VFM to curved manifolds for better material and protein design.
problem Designing materials and proteins on curved manifolds.
method Riemannian Gaussian Variational Flow Matching (RG-VFM) for generative modeling on manifolds.
result RG-VFM more effectively captures manifold structure and improves performance.
New method uses broken scattering to uniquely identify Finsler manifolds.
problem Identifying Finsler manifolds from scattering data.
method Uses broken scattering relation to compare geodesics.
result Two reversible Finsler manifolds with the same broken scattering relation are isometric.
Entropy-Isomap improves low-dimensional visualization of dynamic processes.
problem Mapping high-dimensional, temporally correlated data to a low-dimensional manifold.
method Entropy-Isomap, a novel method addressing temporal correlations in data.
result Correctly captures process control variables and material morphology evolution.
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.
Classical elasticity is concerned with bodies that can be modeled as smooth manifolds endowed with a reference metric that represents local equilibrium distances between neighboring material elements. The elastic energy associated with a configuration of a body in classical elasticity is the sum of local contributions …
Lie groupoids and algebroids help analyze material uniformity and homogeneity.
problem Analyzing uniformity and homogeneity of material bodies.
method Associated Lie groupoids and algebroids to elastic materials, using them to characterize uniformity and homogeneity.
result Characterized uniformity and homogeneity of materials using Lie groupoids and algebroids.
Gopakumar-Vafa large N duality is a correspondence between Chern-Simons invariants of a link in a 3-manifold and relative Gromov-Witten invariants of a 6-dimensional symplectic manifold relative to a Lagrangian submanifold. We address the correspondence between the Chern-Simons free energy of S^3 with no link and the G…
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.
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.
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.
This paper has been withdrawn, because its material has been revised and became part of paper math.GT/0010184
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.
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.
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.
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.
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.
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.
This is an expository and introductory note on some results obtained in "Coisotropic embeddings in Poisson manifolds" (ArXiv math/0611480). Some original material is contained in the last two sections, where we consider linear Poisson structures.
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.
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.
Study material evolution using groupoids to track intrinsic properties.
problem Tracking material evolution without considering the whole body.
method Construct a groupoid encoding intrinsic properties and characteristic foliations.
result Define the evolution equation for material points.
Improves material discovery through better model evaluation metrics.
problem Standard error metrics mislead in material discovery.
method Introduces Pareto shell-scope error for model evaluation.
result Novel diagnostic tools and insights for acquisition function design.
New geometric model explains material evolution in morphogenesis.
problem Understanding non-uniform processes of material evolution.
method Groupoid theory and continuous distributions.
result Explicit equation for material distributions.
Bayesian optimization speeds up materials design.
problem Finding optimal materials designs efficiently.
method Bayesian optimization using Gaussian process regression.
result Optimal material designs achieved with fewer experiments.
Researchers use manifold learning to analyze 4D-STEM data of graphene, revealing atomic structure details.
problem Challenges in processing and interpreting large 4D-STEM datasets, especially for light materials.
method Data-driven manifold learning approaches for visualization and exploration of 4D-STEM datasets.
result Extracted patterns relate to individual atom sites and sublattice structures, effectively discriminating single dopant anomalies.
Automated synthesis planning from scientific literature using AI.
problem Accelerate materials design and discovery by connecting scientific literature to synthesis insights.
method Word embeddings from language models, named entity recognition, conditional variational autoencoder.
result The model predicts precursors for perovskite materials using historical data.
Physics-constrained GP predicts material states under shockwave conditions.
problem Predicting material states under extreme shockwave conditions.
method Physics-constrained Gaussian Process regression with Rankine-Hugoniot constraints.
result Reproduces Hugoniot curves with satisfactory accuracy and uncertainty quantification.
New Bayesian optimization models for efficient material screening.
problem Efficiently screening materials with expensive and cheap tests.
method Flexible multi-test Bayesian optimization models with complex relationships.
result Demonstrated power on synthetic and real data.
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.