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🏛️ The cond-mat.mtrl-sci Crypt
cond-mat.mtrl-sci: Where cond-mat.mtrl-sci papers rest without their code.
279
Total Papers
134
No Code
2
Twilight
143
Has Code
51.3%
Survival Rate
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Machine Learning and Polymer Self-Consistent Field Theory in Two Spatial Dimensions
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Real-time experiment-theory closed-loop interaction for autonomous materials science
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Reward driven workflows for unsupervised explainable analysis of phases and ferroic variants from atomically resolved imaging data
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Skeleton Repo
Particle Swarm Based Hyper-Parameter Optimization for Machine Learned Interatomic Potentials
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Leveraging Legacy Data to Accelerate Materials Design via Preference Learning
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Learning crystal plasticity using digital image correlation: Examples from discrete dislocation dynamics
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Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators
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Machine Learning for Screening Large Organic Molecules
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Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data
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Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
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SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
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Electronic properties of binary compounds with high fidelity and high throughput
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Accessible computational materials design with high fidelity and high throughput
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A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information
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Energy-GNoME: A Living Database of Selected Materials for Energy Applications
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Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS
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Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest
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Detecting Atomic Scale Surface Defects in STM of TMDs with Ensemble Deep Learning
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Novel models for fatigue life prediction under wideband random loads based on machine learning
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MatNexus: A Comprehensive Text Mining and Analysis Suite for Materials Discover
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ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model
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Neural Structure Fields with Application to Crystal Structure Autoencoders
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