Buchumschlag

Composite Material Design for Optimized Fracture Toughness Using Machine Learning

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Jahromi, Mohammad Naqizadeh, Ravandi, Mohammad
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Materials Science Machine Learning
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Learning ORDER-Aware Multimodal Representations for Composite Materials Design
von: Li, Xinyao, et al.
Veröffentlicht: (2026)

Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
von: Li, Jiaxiang, et al.
Veröffentlicht: (2025)

Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs
von: Li, Beichen, et al.
Veröffentlicht: (2023)

Linking Properties to Microstructure in Liquid Metal Embedded Elastomers via Machine Learning
von: Anantharanga, Abhijith Thoopul, et al.
Veröffentlicht: (2022)

Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning
von: Barad, Sagar Prakash, et al.
Veröffentlicht: (2024)

A Foundation Model for Material Fracture Prediction
von: Marcato, Agnese, et al.
Veröffentlicht: (2025)

Machine learning model for predicting surface wettability in laser-textured metal alloys
von: Sanandaji, Mohammad Mohammadzadeh, et al.
Veröffentlicht: (2026)

Crystal Systems Classification of Phosphate-Based Cathode Materials Using Machine Learning for Lithium-Ion Battery
von: Yadav, Yogesh, et al.
Veröffentlicht: (2025)

Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing
von: Mishra, Akshansh
Veröffentlicht: (2024)

Machine Learning Models for Reinforced Concrete Pipes Condition Prediction: The State-of-the-Art Using Artificial Neural Networks and Multiple Linear Regression in a Wisconsin Case Study
von: Mohammadagha, Mohsen, et al.
Veröffentlicht: (2025)

Material Microstructure Design Using VAE-Regression with Multimodal Prior
von: Sardeshmukh, Avadhut, et al.
Veröffentlicht: (2024)

Toward Multi-Fidelity Machine Learning Force Field for Cathode Materials
von: Dong, Guangyi, et al.
Veröffentlicht: (2025)

Machine Learning Reveals Composition Dependent Thermal Stability in Halide Perovskites
von: Hering, Abigail R., et al.
Veröffentlicht: (2025)

Compositional Representation of Polymorphic Crystalline Materials
von: Lee, Namkyeong, et al.
Veröffentlicht: (2023)

Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
von: Birks, Fraser, et al.
Veröffentlicht: (2025)

Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training
von: Oerder, Rick, et al.
Veröffentlicht: (2026)

Machine Learning-Driven Creep Law Discovery Across Alloy Compositional Space
von: Chen, Hongshun, et al.
Veröffentlicht: (2026)

A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery
von: Hashimoto, Yusuke, et al.
Veröffentlicht: (2025)

Bridging Theory and Experiment in Materials Discovery: Machine-Learning-Assisted Prediction of Synthesizable Structures
von: Xin, Yu, et al.
Veröffentlicht: (2025)

Deep Gaussian Process-based Cost-Aware Batch Bayesian Optimization for Complex Materials Design Campaigns
von: Alvi, Sk Md Ahnaf Akif, et al.
Veröffentlicht: (2025)

Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
von: Cangi, Attila, et al.
Veröffentlicht: (2024)

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
von: Kang, Kisung, et al.
Veröffentlicht: (2024)

Supervised Pretraining for Material Property Prediction
von: Rahman, Chowdhury Mohammad Abid, et al.
Veröffentlicht: (2025)

Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization
von: Price, Christopher C., et al.
Veröffentlicht: (2024)

Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning
von: Slautin, Boris N., et al.
Veröffentlicht: (2024)

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
von: Benítez, Pol, et al.
Veröffentlicht: (2025)

Quantum Kernel Machine Learning for Autonomous Materials Science
von: Adams, Felix, et al.
Veröffentlicht: (2026)

A Machine Learning Approach for Design of Frequency Selective Surface based Radar Absorbing Material via Image Prediction
von: Sutrakar, Vijay Kumar, et al.
Veröffentlicht: (2025)

Stress, Strain, or Displacement? A Novel Machine Learning Based Framework to Predict Mixed Mode I/II Fracture Toughness
von: Mirzaei, Amir Mohammad
Veröffentlicht: (2025)

Rapid Machine Learning-Driven Detection of Pesticides and Dyes Using Raman Spectroscopy
von: Binh, Quach Thi Thai, et al.
Veröffentlicht: (2025)

Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review
von: Nematov, Dilshod, et al.
Veröffentlicht: (2025)

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials
von: Makino, Daisuke, et al.
Veröffentlicht: (2025)

On Simulating Thin-Film Processes at the Atomic Scale Using Machine Learned Force Fields
von: Natarajan, S. Kondati, et al.
Veröffentlicht: (2025)

VAE for Modified 1-Hot Generative Materials Modeling, A Step Towards Inverse Material Design
von: El-Awady, Khalid
Veröffentlicht: (2023)

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
von: Cheng, Mouyang, et al.
Veröffentlicht: (2025)

Mechanics and Design of Metastructured Auxetic Patches with Bio-inspired Materials
von: Chen, Yingbin, et al.
Veröffentlicht: (2025)

Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks
von: Azzaz, Riadh, et al.
Veröffentlicht: (2024)

AMShortcut: An Inference- and Training-Efficient Inverse Design Model for Amorphous Materials
von: Lin, Yan, et al.
Veröffentlicht: (2026)

Inverse Materials Design by Large Language Model-Assisted Generative Framework
von: Hao, Yun, et al.
Veröffentlicht: (2025)

Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors
von: Kenney, Michael J., et al.
Veröffentlicht: (2024)