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Molecular identification with atomic force microscopy and conditional generative adversarial networks
《npj Computational Materials》2024年第1期3080-3090,共11页Jaime Carracedo-Cosme Rubén Pérez 
We would like to acknowledge support from the Comunidad de Madrid Industrial Doctorate Programme 2017 under reference number IND2017/IND-7793 and from Quasar Science Resources S.L.R.P;acknowledges support from the Spanish Ministry of Science and Innovation,through projects PID2020-115864RB-I00,TED2021-132219A-I00,and the“María de Maeztu”Programme for Units of Excellence in R&D(CEX2018-000805-M).
Frequency modulation(FM)atomic force microscopy(AFM)with metal tips functionalized with a CO molecule at the tip apex(referred as High-Resolution AFM,HR-AFM)has provided access to the internal structure of molecules w...
关键词：NETWORKS ATOMIC CONVERT 
NSGAN:a non-dominant sorting optimisation-based generative adversarial design framework for alloy discovery
《npj Computational Materials》2024年第1期2087-2101,共15页Z.Li N.Birbilis 
The design and discovery of new materials is fundamental to advancing scientific and technological innovation.The recent emergence of the materials genome concept holds great promise in revolutionising materials scien...
关键词：ALLOY offering holds 
Generative adversarial network(GAN)enabled Statistically equivalent virtual microstructures(SEVM)for modeling cold spray formed bimodal polycrystals
《npj Computational Materials》2024年第1期2919-2932,共14页Brayan Murgas Joshua Stickel Somnath Ghosh 
sponsored by a grant from the Office of Naval Research Aero Structures&Materials program,USA through Grant Number:N00014-20-1-4004;supported by the National Science Foundation(NSF)grant number OAC 1920103;Computational support for this work is also provided by an AFOSR DURIP grant FA9550-21-1-0303.
Image-based micromechanical models,necessary for the development of structure-property-response relations,are far from mature for complex microstructures with multi-modal distributions of morphological and crystallogr...
关键词：microstructure MICROSTRUCTURES integrating 
Accelerated discovery of eutectic compositionally complex alloys by generative machine learning
《npj Computational Materials》2024年第1期1091-1102,共12页Z.Q.Chen Y.H.Shang X.D.Liu Y.Yang 
supported by Research Grants Council(RGC),the Hong Kong government through General Research Fund(GRF)with grant numbers of CityU 11206362 and CityU 11201721 and through NSFC-RGC Joint Research Schemewith grant number of N_CityU 109/21;YY also acknowledges the support by City University of Hong Kong through CityU new research initiative with grant number of 9610603。
Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties,as well as their technological relevance.However,the discovery of eutectic compositionally complex alloys(E...
关键词：EUTECTIC ALLOYS alloy 
A generative deep learning framework for inverse design of compositionally complex bulk metallic glasses被引量：3
《npj Computational Materials》2023年第1期2196-2203,共8页Ziqing Zhou Yinghui Shang Xiaodi Liu Yong Yang 
The research of Y.Y.is supported by the research grant Council(RGC),the Hong Kong government,through the general research fund(GRF)with the grant numbers of N_CityU 109/21,CityU11200719 and CityU11213118.
The design of bulk metallic glasses(BMGs)via machine learning(ML)has been a topic of active research recently.However,the prior ML models were mostly built upon supervised learning algorithms with human inputs to navi...
关键词：INVERSE METALLIC ENTROPY 
Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks
《npj Computational Materials》2022年第1期650-660,共11页Orkun Furat Donal P.Finegan Zhenzhen Yang Tom Kirstein Kandler Smith Volker Schmidt 
For a deeper understanding of the functional behavior of energy materials,it is necessary to investigate their microstructure,e.g.,via imaging techniques like scanning electron microscopy (SEM).However,active material...
关键词：microstructure CRACK NETWORKS 
Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures被引量：7
《npj Computational Materials》2021年第1期588-594,共7页Teng Long Nuno M.Fortunato Ingo Opahle Yixuan Zhang Ilias Samathrakis Chen Shen Oliver Gutfleisch Hongbin Zhang 
The authors gratefully acknowledge computational time on the Lichtenberg High-Performance Supercomputer.Teng Long thanks the financial support from the China Scholarship Council(CSC).Part of this work was supported by the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(Grant No.743116-project Cool Innov);This work was also supported by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)–Project-ID 405553726–TRR 270;We also acknowledge support by the Deutsche Forschungsgemeinschaft(DFG–German Research Foundation)and the Open Access Publishing Fund of Technical University of Darmstadt.
Autonomous materials discovery with desired properties is one of the ultimate goals for materials science,and the current studies have been focusing mostly on high-throughput screening based on density functional theo...
关键词：properties CRYSTAL INVERSE 
Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials被引量：13
《npj Computational Materials》2020年第1期964-970,共7页Yabo Dan Yong Zhao Xiang Li Shaobo Li Ming Hu Jianjun Hu 
This work as partially supported by the National Science Foundation under grant numbers:1940099,1905775,OIA-1655740,and SC EPSCoR GEAR Grant 19-GC02 and by DOE under grant number DE-SC0020272;The authors also acknowledge funding from the National Natural Science Foundation of China under grant number 51741101;This work is also partially supported by National Major Scientific and Technological Special Project of China under grant number 2018AAA0101803;also by Guizhou Province Science&Technology Plan Talent Program under grant number[2017]5788.
A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties.One effective strategy is to develop sampling algorithms that can exploit...
关键词：CHEMICAL INVERSE NETWORKS 
Pores for thought:generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries被引量：5
《npj Computational Materials》2020年第1期971-981,共11页Andrea Gayon-Lombardo Lukas Mosser Nigel P.Brandon Samuel J.Cooper 
This work was supported by funding from both the CONACYT-SENER fund and the EPSRC Faraday Institution Multi-Scale Modelling project(https://faraday.ac.uk/,EP/S003053/1,grant number FIRG003).
The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices.This work implements a deep convolutional generative adversarial network(D...
关键词：microstructure ELECTRODE phase 
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