Fetching the paper…
Reading the bibliography…
A multi-agent AI model is used to automate the discovery of new metallic alloys, integrating multimodal data and external knowledge including insights from physics via atomistic simulations.
Fast parallel algorithms for short-range molecular dynamics
Steve Plimpton · 1995
Earlier work this paper cites.
A climbing image nudged elastic band method for finding saddle points and minimum energy paths
Graeme Henkelman, Blas P Uberuaga, and Hannes Jónsson · 2000
Earlier work this paper cites.
Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points
Graeme Henkelman and Hannes Jónsson · 2000
Earlier work this paper cites.
A space–time-ensemble parallel nudged elastic band algorithm for molecular kinetics simulation
Aiichiro Nakano · 2008
Earlier work this paper cites.
Refractory high-entropy alloys
ON Senkov, GB Wilks, DB Miracle, CP Chuang, and PK Liaw · 2010
Earlier work this paper cites.
Mechanical properties of nb25mo25ta25w25 and v20nb20mo20ta20w20 refractory high entropy alloys
Oleg N Senkov, Garth B Wilks, James M Scott, and Daniel B Miracle · 2011
Earlier work this paper cites.
Microstructure and room temperature properties of a high-entropy tanbhfzrti alloy
ON Senkov, JM Scott, SV Senkova, DB Miracle, and CF Woodward · 2011
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization 3rd international conference on learning representations
Diederik P Kingma and JL Ba · 2015
Earlier work this paper cites.
High-entropy alloy: challenges and prospects
YF Ye, Qing Wang, Jiatian Lu, CT Liu, and Yancong Yang · 2016
Earlier work this paper cites.
Theory of strengthening in fcc high entropy alloys
Céline Varvenne, Aitor Luque, and William A Curtin · 2016
Earlier work this paper cites.
Perspective: Machine learning potentials for atomistic simulations
Jörg Behler · 2016
Earlier work this paper cites.
Moment tensor potentials: A class of systematically improvable interatomic potentials
Alexander V Shapeev · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Global transition path search for dislocation formation in ge on si (001)
Emile Maras, Oleg Trushin, Alexander Stukowski, Tapio Ala-Nissila, and Hannes Jonsson · 2016
Earlier work this paper cites.
A critical review of high entropy alloys and related concepts
Daniel B Miracle and Oleg N Senkov · 2017
Earlier work this paper cites.
Solute strengthening in random alloys
Céline Varvenne, Gerard Paul M Leyson, Maryam Ghazisaeidi, and William A Curtin · 2017
Earlier work this paper cites.
Machine learning in materials informatics: recent applications and prospects
Rampi Ramprasad, Rohit Batra, Ghanshyam Pilania, Arun Mannodi-Kanakkithodi, and Chiho Kim · 2017
Earlier work this paper cites.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al · 2017
Earlier work this paper cites.
Development and exploration of refractory high entropy alloys—a review
Oleg N Senkov, Daniel B Miracle, Kevin J Chaput, and Jean-Philippe Couzinie · 2018
Earlier work this paper cites.
Compositional effect on microstructure and properties of nbtizr-based complex concentrated alloys
ON Senkov, S Rao, KJ Chaput, and C Woodward · 2018
Earlier work this paper cites.
Microstructures and mechanical properties of tixnbmotaw refractory high-entropy alloys
ZD Han, HW Luan, X Liu, N Chen, XY Li, Y Shao, and KF Yao · 2018
Earlier work this paper cites.
Machine learning for molecular and materials science
Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh · 2018
Cited alongside, same era.
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C Grossman · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
Cited alongside, same era.
High-entropy alloys
Easo P George, Dierk Raabe, and Robert O Ritchie · 2019
Cited alongside, same era.
High temperature strength of refractory complex concentrated alloys
Oleg N Senkov, Stéphane Gorsse, and Daniel B Miracle · 2019
Cited alongside, same era.
Solution hardening in body-centered cubic quaternary alloys interpreted using suzuki’s kink-solute interaction model
High energy barriers for edge dislocation motion in body-centered cubic high entropy alloys
RE Kubilay, A Ghafarollahi, F Maresca, and WA Curtin · 2021
Later among the works it cites.
Artificial intelligence and machine learning in design of mechanical materials
Kai Guo, Zhenze Yang, Chi-Hua Yu, and Markus J Buehler · 2021
Later among the works it cites.
Atomistic simulations of dislocation mobility in refractory high-entropy alloys and the effect of chemical short-range order
Sheng Yin, Yunxing Zuo, Anas Abu-Odeh, Hui Zheng, Xiang-Guo Li, Jun Ding, Shyue Ping Ong, Mark Asta, and Robert O Ritchie · 2021
Later among the works it cites.
Screw-controlled strength of bcc non-dilute and high-entropy alloys
Alireza Ghafarollahi and William A Curtin · 2022
Later among the works it cites.
Screw vs. edge dislocation strengthening in body-centered-cubic high entropy alloys and implications for guided alloy design
C Baruffi, F Maresca, and WA Curtin · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
SI Rao, E Antillon, C Woodward, B Akdim, TA Parthasarathy, and ON Senkov · 2019
Cited alongside, same era.
Solute/screw dislocation interaction energy parameter for strengthening in bcc dilute to high entropy alloys
A Ghafarollahi, F Maresca, and WA Curtin · 2019
Cited alongside, same era.
Machine learning in materials science
Jing Wei, Xuan Chu, Xiang-Yu Sun, Kun Xu, Hui-Xiong Deng, Jigen Chen, Zhongming Wei, and Ming Lei · 2019
Cited alongside, same era.
Machine learning interatomic potentials as emerging tools for materials science
Volker L Deringer, Miguel A Caro, and Gábor Csányi · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
Cited alongside, same era.
High entropy alloys: A focused review of mechanical properties and deformation mechanisms
Easo P George, William A Curtin, and Cemal Cem Tasan · 2020
Cited alongside, same era.
Theory-guided design of high-strength, high-melting point, ductile, low-density, single-phase bcc high entropy alloys
Y Rao, C Baruffi, A De Luca, C Leinenbach, and WA Curtin · 2022
Later among the works it cites.
Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks
Zhenze Yang and Markus J Buehler · 2022
Later among the works it cites.
Rapid prediction of protein natural frequencies using graph neural networks
Kai Guo and Markus J Buehler · 2022
Later among the works it cites.
Refractory high-entropy alloys: A focused review of preparation methods and properties
Wei Xiong, Amy XY Guo, Shuai Zhan, Chain-Tsuan Liu, and Shan Cecilia Cao · 2023
Later among the works it cites.
Autogen: An automated dynamic model generation framework for recommender system
Chenxu Zhu, Bo Chen, Huifeng Guo, Hang Xu, Xiangyang Li, Xiangyu Zhao, Weinan Zhang, Yong Yu, and Ruiming Tang · 2023
Later among the works it cites.
Sciagents: Automating scientific discovery through multi-agent intelligent graph reasoning
Alireza Ghafarollahi and Markus J. Buehler · 2024
Closest in time.
Leveraging biomolecule and natural language through multi-modal learning: A survey
Qizhi Pei, Lijun Wu, Kaiyuan Gao, Jinhua Zhu, Yue Wang, Zun Wang, Tao Qin, and Rui Yan · 2024
Closest in time.
Large language model based multi-agents: A survey of progress and challenges
Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V Chawla, Olaf Wiest, and Xiangliang Zhang · 2024
Closest in time.
Large multimodal agents: A survey
Junlin Xie, Zhihong Chen, Ruifei Zhang, Xiang Wan, and Guanbin Li · 2024
Closest in time.
Exploring large language model based intelligent agents: Definitions, methods, and prospects
Yuheng Cheng, Ceyao Zhang, Zhengwen Zhang, Xiangrui Meng, Sirui Hong, Wenhao Li, Zihao Wang, Zekai Wang, Feng Yin, Junhua Zhao, et al · 2024
Closest in time.
Augmenting large language models with chemistry tools
Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller · 2024
Closest in time.
Generative retrieval-augmented ontologic graph and multiagent strategies for interpretive large language model-based materials design
Markus J Buehler · 2024
Closest in time.
Mechagents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
Bo Ni and Markus J Buehler · 2024
Closest in time.
Molecular analysis and design using multimodal generative artificial intelligence via multi-agent modeling
Isabella Stewart and Markus Buehler · 2024
Closest in time.
Protagents: protein discovery via large language model multi-agent collaborations combining physics and machine learning
Alireza Ghafarollahi and Markus J Buehler · 2024
Closest in time.
Alireza Ghafarollahi and Markus J Buehler · 2024
Closest in time.
Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning
Markus J. Buehler · 2024
Closest in time.