Device-scale atomistic modelling of phase-change memory materials DOI Creative Commons
Yuxing Zhou, Wei Zhang, E. Ma

et al.

Nature Electronics, Journal Year: 2023, Volume and Issue: 6(10), P. 746 - 754

Published: Sept. 25, 2023

Abstract Computer simulations can play a central role in the understanding of phase-change materials and development advanced memory technologies. However, direct quantum-mechanical are limited to simplified models containing few hundred or thousand atoms. Here we report machine-learning-based potential model that is trained using data be used simulate range germanium–antimony–tellurium compositions—typical materials—under realistic device conditions. The speed our enables atomistic multiple thermal cycles delicate operations for neuro-inspired computing, specifically cumulative SET iterative RESET. A device-scale (40 × 20 nm 3 ) over half million atoms shows machine-learning approach directly describe technologically relevant processes devices based on materials.

Language: Английский

Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method DOI
Adeleke Maradesa, Baptiste Py, Jake Huang

et al.

Joule, Journal Year: 2024, Volume and Issue: 8(7), P. 1958 - 1981

Published: June 7, 2024

Language: Английский

Citations

57
Ab initio quantum chemistry with neural-network wavefunctions DOI
Jan Hermann,

James Spencer,

Kenny Choo

et al.

Nature Reviews Chemistry, Journal Year: 2023, Volume and Issue: 7(10), P. 692 - 709

Published: Aug. 9, 2023

Language: Английский

Citations

55
Representations of Materials for Machine Learning DOI Creative Commons

James Damewood,

Jessica Karaguesian,

Jaclyn R. Lunger

et al.

Annual Review of Materials Research, Journal Year: 2023, Volume and Issue: 53(1), P. 399 - 426

Published: April 18, 2023

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by the relations between composition, structure, properties exploiting such for design. However, build these connections, must be translated into numerical form, called representation, that can processed an ML model. Data sets in vary format (ranging from images spectra), size, fidelity. Predictive models scope interest. Here, we review context-dependent strategies constructing representations enable use as inputs or outputs models. Furthermore, discuss how modern techniques learn transfer chemical physical information tasks. Finally, outline high-impact questions not been fully resolved thus require further investigation.

Language: Английский

Citations

51
A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management DOI Creative Commons

Maria Drogkoula,

Konstantinos Kokkinos, Nicholas Samaras

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(22), P. 12147 - 12147

Published: Nov. 8, 2023

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain water resource management. Environmental issues, such as climate change ecosystem destruction, pose significant threats to humanity planet. Addressing these challenges necessitates sustainable management increased efficiency. Artificial intelligence (AI) ML technologies present promising solutions this regard. By harnessing AI ML, we can collect analyze vast amounts data from diverse sources, remote sensing, smart sensors, social media. enables real-time monitoring decision making applications, including irrigation optimization, quality monitoring, flood forecasting, demand enhance agricultural practices, distribution models, desalination plants. Furthermore, facilitates integration, supports decision-making processes, enhances overall sustainability. However, wider adoption faces challenges, heterogeneity, stakeholder education, high costs. To provide an management, research focuses on core fundamentals, major (prediction, clustering, reinforcement learning), ongoing issues offer new insights. More specifically, after in-depth illustration algorithmic taxonomy, comparative mapping all specific tasks. At same time, include tabulation works along with some concrete, yet compact, descriptions objectives at hand. leveraging tools, develop plans address world’s supply concerns effectively.

Language: Английский

Citations

50
Device-scale atomistic modelling of phase-change memory materials DOI Creative Commons
Yuxing Zhou, Wei Zhang, E. Ma

et al.

Nature Electronics, Journal Year: 2023, Volume and Issue: 6(10), P. 746 - 754

Published: Sept. 25, 2023

Abstract Computer simulations can play a central role in the understanding of phase-change materials and development advanced memory technologies. However, direct quantum-mechanical are limited to simplified models containing few hundred or thousand atoms. Here we report machine-learning-based potential model that is trained using data be used simulate range germanium–antimony–tellurium compositions—typical materials—under realistic device conditions. The speed our enables atomistic multiple thermal cycles delicate operations for neuro-inspired computing, specifically cumulative SET iterative RESET. A device-scale (40 × 20 nm 3 ) over half million atoms shows machine-learning approach directly describe technologically relevant processes devices based on materials.

Language: Английский

Citations

47