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🏛️ The cond-mat.mtrl-sci Crypt
cond-mat.mtrl-sci: Where cond-mat.mtrl-sci papers rest without their code.
291
Total Papers
134
No Code
2
Twilight
155
Has Code
53.3%
Survival Rate
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Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design
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Deep learning and the Schrödinger equation
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MatterGen: a generative model for inorganic materials design
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Polymer Informatics with Multi-Task Learning
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Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
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AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
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Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
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Computational discovery of new 2D materials using deep learning generative models
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AI-accelerated Discovery of Altermagnetic Materials
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Interpretable models for extrapolation in scientific machine learning
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Graph Convolutional Neural Networks for Polymers Property Prediction
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Modelling the Nonlinear Response of Fibre-reinforced Bending Fluidic Actuators
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Materials Property Prediction with Uncertainty Quantification: A Benchmark Study
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Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
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Equivariant Networks for Crystal Structures
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AI-driven inverse design of materials: Past, present and future
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Exploring Supervised Machine Learning for Multi-Phase Identification and Quantification from Powder X-Ray Diffraction Spectra
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Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
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Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks
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Machine-learned metrics for predicting the likelihood of success in materials discovery
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A physics-aware deep learning model for energy localization in multiscale shock-to-detonation simulations of heterogeneous energetic materials
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Accelerating Process Development for 3D Printing of New Metal Alloys
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