Fetching the paper…
Reading the bibliography…
Graph neural networks (GNNs) have excelled in predictive modeling for both crystals and molecules, owing to the expressiveness of graph representations.
“Elasticity and Viscoelasticity”
Marcé Meyers and Krishan Chawla · 2008
Earlier work this paper cites.
“Scikit-learn: Machine learning in Python”
Fabian Pedregosa et al · 2011
Earlier work this paper cites.
“Neural Message Passing for Quantum Chemistry”
Justin Gilmer et al · 2017
Earlier work this paper cites.
“Deep Sets”
Manzil Zaheer et al · 2017
Earlier work this paper cites.
“Attention Is All You Need”
Ashish Vaswani et al · 2017
Earlier work this paper cites.
“Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties”
Tian Xie and Jeffrey. Grossman · 2018
Earlier work this paper cites.
“Modeling the structure and thermodynamics of high-entropy alloys”
Michael Widom · 2018
Earlier work this paper cites.
“High-Entropy Alloys as a Discovery Platform for Electrocatalysis”
Thomas.. Batchelor et al · 2019
Earlier work this paper cites.
“Decoupled Weight Decay Regularization”
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
“Heteroanionic materials by design: progress toward targeted properties”
Jaye Harada, Nenian Charles, Kenneth Poeppelmeier and James Rondinelli · 2019
Earlier work this paper cites.
“Deliberate deficiencies: Expanding electronic function through non-stoichiometry”
James Rondinelli and Steven May · 2019
Earlier work this paper cites.
“Weisfeiler and Leman go neural: Higher-order graph neural networks”
Christopher Morris et al · 2019
Earlier work this paper cites.
“Neural Network-Assisted Development of High-Entropy Alloy Catalysts: Decoupling Ligand and Coordination Effects”
Zhuole Lu, Zhi Chen and Chandra Singh · 2020
Earlier work this paper cites.
“Experiment Tracking with Weights and Biases” Software available from wandb.com, 2020
Lukas Biewald · 2020
Earlier work this paper cites.
“E(n) Equivariant Graph Neural Networks”
Víctor Satorras, Emiel Hoogeboom and Max Welling · 2021
Cited alongside, same era.
“Atomistic Line Graph Neural Network for improved materials property predictions”
Kamal Choudhary and Brian Decost · 2021
Cited alongside, same era.
“Equivariant message passing for the prediction of tensorial properties and molecular spectra”
Kristof. Schütt, Oliver. Unke and Michael Gastegger · 2021
Cited alongside, same era.
“Mechanical Behavior of High-Entropy Alloys: A Review”
Yuanyuan Shang, Jamieson Brechtl, Claudio Pistidda and Peter. Liaw · 2021
Cited alongside, same era.
“Graph neural networks for materials science and chemistry”
Patrick Reiser et al · 2022
Cited alongside, same era.
“Machine learning–enabled high-entropy alloy discovery”
Ziyuan Rao et al · 2022
Cited alongside, same era.
“CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling”
Bowen Deng et al · 2023
Later among the works it cites.
“Machine learning for high-entropy alloys: Progress, challenges and opportunities”
Xianglin Liu, Jiaxin Zhang and Zongrui Pei · 2023
Later among the works it cites.
“Understanding and leveraging short-range order in compositionally complex alloys”
Mitra. Taheri et al · 2023
Later among the works it cites.
“Molecular dynamics simulations of tensile response for FeNiCrCoCu high-entropy alloy with voids”
Tinghong Gao et al · 2023
Later among the works it cites.
“Density functional theory-machine learning characterization of the adsorption energy of oxygen intermediates on high-entropy alloys made of earth-abundant metals”
Geng Yuan, Mingyue Wu and Luis Ruiz · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Exceptional fracture toughness of CrCoNi-based medium- and high-entropy alloys at 20 kelvin”
Dong Liu et al · 2022
Cited alongside, same era.
“Machine-learning-driven high-entropy alloy catalyst discovery to circumvent the scaling relation for CO 2 reduction reaction”
Zhi Chen et al · 2022
Cited alongside, same era.
“Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys”
Guillermo Vazquez et al · 2022
Cited alongside, same era.
“Element-wise representations with ECNet for material property prediction and applications in high-entropy alloys”
Xinming Wang et al · 2022
Cited alongside, same era.
“A universal graph deep learning interatomic potential for the periodic table”
Chi Chen and Shyue Ong · 2022
Cited alongside, same era.
“MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields”
Ilyes Batatia et al · 2022
Cited alongside, same era.
“Exploiting redundancy in large materials datasets for efficient machine learning with less data”
Kangming Li et al · 2023
Later among the works it cites.
“Chemical and structural factors affecting the stability of Wadsley–Roth block phases”
Muna Saber et al · 2023
Later among the works it cites.
“Spin-dependent graph neural network potential for magnetic materials”
Hongyu Yu et al · 2024
Closest in time.
“Learning molecular mixture property using chemistry-aware graph neural network”
Hengrui Zhang et al · 2024
Closest in time.
“Chemprop: A Machine Learning Package for Chemical Property Prediction”
Esther Heid et al · 2024
Closest in time.
“Machine learning assisted design of high-entropy alloys with ultra-high microhardness and unexpected low density”
Shunli Zhao et al · 2024
Closest in time.
“Supervised AI and Deep Neural Networks to Evaluate High-Entropy Alloys as Reduction Catalysts in Aqueous Environments”
Rafael. Araujo and Tomas Edvinsson · 2024
Closest in time.
“Negative mixing enthalpy solid solutions deliver high strength and ductility”
Zibing An et al · 2024
Closest in time.
“Distinguishing Elements at the Sub-Nanometer Scale on the Surface of a High Entropy Alloy”
Lauren Kim et al · 2024
Closest in time.