Tensor completion accelerates lattice structure design in materials science

Researchers have developed a novel approach using tensor completion as a surrogate model to accelerate the design of optimal lattice structures for specific mechanical properties. This method addresses challenges in materials design where training data is often non-uniformly sampled due to experimental convenience. Experiments demonstrate that tensor completion outperforms traditional machine learning methods like Gaussian Process and XGBoost in scenarios with biased sampling, achieving approximately 5% higher R^2 scores. The technique also shows comparable performance to methods using uniformly random sampling across the design space. AI

IMPACT This research could significantly speed up the discovery of new materials with desired properties by improving the efficiency of machine learning models in design processes.

RANK_REASON The cluster contains an academic paper detailing a new methodology for materials design using machine learning techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX Code Finder for Papers
• CORE Recommender
• DagsHub
• Gaussian process
• Gotit.pub
• Hugging Face
• IArxiv Recommender
• Influence Flower
• ScienceCast
• Shaan Pakala
• XGBoost

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →