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🏛️ The cond-mat.mtrl-sci Crypt
cond-mat.mtrl-sci: Where cond-mat.mtrl-sci papers rest without their code.
251
Total Papers
134
No Code
2
Twilight
115
Has Code
45.8%
Survival Rate
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Solvers for the Hermitian and the pseudo-Hermitian Bethe-Salpeter equation in the Yambo code: Implementation and Performance
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Large Scale Finite-Temperature Real-time Time Dependent Density Functional Theory Calculation with Hybrid Functional on ARM and GPU Systems
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Eternal Rest
Efficient Materials Informatics between Rockets and Electrons
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Discovery of 2D Materials via Symmetry-Constrained Diffusion Model
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Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
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Graph neural network framework for energy mapping of hybrid monte-carlo molecular dynamics simulations of Medium Entropy Alloys
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Simultaneous Energy Harvesting and Hand Gesture Recognition in Large Area Monolithic Dye-Sensitized Solar Cells
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Photoemission Orbital Tomography Using Robust Sparse PhaseLift
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Deep learning surrogate interacting Markov chain Monte Carlo based full wave inversion scheme for properties of materials quantification
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3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys
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Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis
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Unsupervised segmentation of irradiation$\unicode{x2010}$induced order$\unicode{x2010}$disorder phase transitions in electron microscopy
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The High-dimensional Phase Diagram and the Large CALPHAD Model
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Composition based oxidation state prediction of materials using deep learning
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Dara: Automated multiple-hypothesis phase identification and refinement from powder X-ray diffraction
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Generating new coordination compounds via multireference simulations, genetic algorithms and machine learning: the case of Co(II) molecular magnets
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Accelerating Multi-Objective Collaborative Optimization of Doped Thermoelectric Materials via Artificial Intelligence
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Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
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When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem
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Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
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Application of machine learning in grain-related clustering of Laue spots in a polycrystalline energy dispersive Laue pattern
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Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
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