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2 months ago | 
onlinelibrary.wiley.com | SeongMin Kim |Joshua Schrier |Yousung Jung
Supporting Information As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.
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Jan 6, 2025 | 
nature.com | Zhihao Xu |SeongMin Kim |Eungkyu Lee
AbstractHigh Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging.
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Dec 1, 2024 | 
mdpi.com | Seon-Mi Lee |Aeran Seol |SeongMin Kim |Hyunkyoung Seo
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Nov 12, 2024 | 
nature.com | Zhihao Xu |SeongMin Kim |Eungkyu Lee
AbstractQuantum algorithms are emerging tools in the design of functional materials due to their powerful solution space search capability. How to balance the high price of quantum computing resources and the growing computing needs has become an urgent problem to be solved. We propose a novel optimization strategy based on an active learning scheme that combines the Quantum-inspired Genetic Algorithm (QGA) with machine learning surrogate model regression.
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Oct 4, 2024 | 
pubs.rsc.org | SeongMin Kim |Jungang Heo |Sungjun Kim |Min-Hwi Kim
Dual functionality of NbOx memristors for synaptic and neuronal emulations in advanced neuromorphic systems†
In this study, we reveal a novel NbOx memristor structure that significantly advances neuromorphic computing by modulating compliance current (CC). This structure emulates the dynamic functionalities of artificial synapses and neurons, addressing the challenge of accurately imitating biological counterparts.
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Sep 5, 2024 | 
onlinelibrary.wiley.com | SeongMin Kim |Sungjun Kim |Jungang Heo |Min-Hwi Kim
1 Introduction There has been an increase in the demand for large-scale data processing in a variety of applications following the fourth industrial revolution, including big data, artificial intelligence, and the Internet of Things. However, there are challenges associated with the processing of vast amounts of data in existing computer systems given their von Neumann structures.
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Jul 25, 2024 | 
mdpi.com | Jihyun Lee |SeongMin Kim
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Jul 24, 2024 | 
mdpi.com | SeongMin Kim |Seon-Mi Lee |Aeran Seol |SangHoon Lee
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Jun 10, 2024 | 
mdpi.com | YeEun Kim |Jaehyun Nam |SeongMin Kim |Sara Hong
All articles published by MDPI are made immediately available worldwide under an open access license. No specialpermission is required to reuse all or part of the article published by MDPI, including figures and tables. Forarticles published under an open access Creative Common CC BY license, any part of the article may be reused withoutpermission provided that the original article is clearly cited. For more information, please refer tohttps://www.mdpi.com/openaccess.
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Apr 30, 2024 | 
pubs.acs.org | Shuan Chen |Juhwan Noh |Jidon Jang |SeongMin Kim
ConspectusThe field of chemical research boasts a long history of developing software to automate synthesis planning and reaction prediction. Early software relied heavily on expert systems, requiring significant effort to encode vast amounts of synthesis knowledge into a computer-readable format. However, recent advancements in deep learning have shifted the focus toward AI models, offering improved prediction capabilities.
