Metal–insulator transition in <i>R</i>NiO3 (<i>R</i> = Pr, Nd, Sm, Gd, Tb, Dy, Ho, Er) induced by Li doping: A first-principles study

Cui, Yuanyuan; Liu, Xiao; Fan, Wei; Ren, Junsong; Gao, Yanfeng

doi:10.1063/5.0050263

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…This adjustment influences the exchange splitting between occupied and unoccupied eigenstates. DFT+U methodologies have proven instrumental in elucidating phenomena such as metal to insulator transitions triggered H/Li doping. ,− Impressively, the theoretical predictions closely align with experimental observations, highlighting the capability of DFT+U to capture complex and subtle material behavior.…”

Section: Theoretical Models To Study Proton-induced Neuromorphic Beha...mentioning

confidence: 68%

Proton Conducting Neuromorphic Materials and Devices

Yuan,

Patel,

Banik

et al. 2024

Chem. Rev.

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Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotronbased spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

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Section: Theoretical Models To Study Proton-induced Neuromorphic Beha...mentioning

confidence: 68%

Proton Conducting Neuromorphic Materials and Devices

Yuan,

Patel,

Banik

et al. 2024

Chem. Rev.

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“…The DFT+U capabilities have been extensively used for predicting lattice parameters, magnetic moments, bandgap for transition metal oxides, [154,155] transition metal sulfides, [156][157][158] transition metal nitrides, [159] rareearth nickelates [160][161][162][163] with a single value of U. Additionally, in case of rareearth nickelates, DFT+U has also been used to study H/Li induced metal-insulator transition which is found to be in agreement with the experi ment. [164][165][166][167] The main limitation of DFT+U approach lies in finding reasonable estimates of the value of U for a particular system. The U parameter can generally be calculated using a selfconsistent basis set in an independent way.…”

Section: Electronic Structure Calculations Of Stable and Metastable P...mentioning

confidence: 99%

Complex Oxides for Brain‐Inspired Computing: A Review

et al. 2022

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The fields of brain‐inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human‐designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal‐to‐insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First‐principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

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“…In these systems, Kotiuga et al and Cui et al respectively studied the effects of oxygen vacancies and Li doping in order to drive and understand changes in electronic structure that give rise to an improved memory device. 57,58 A mechanistic understanding of the interplay between electronic structure changes and magnetic states was provided by computational investigations that could then be used to interpret experiments.…”

Section: A Metal-insulator Transitions In Quantum Materialsmentioning

confidence: 99%

Quantum materials for energy-efficient neuromorphic computing

Hoffmann,

Ramanathan,

Grollier

et al. 2022

Preprint

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Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This paper discusses select examples of these approaches, and provides a perspective for the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

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Metal–insulator transition in RNiO3 (R = Pr, Nd, Sm, Gd, Tb, Dy, Ho, Er) induced by Li doping: A first-principles study

Cited by 11 publications

References 35 publications

Proton Conducting Neuromorphic Materials and Devices

Proton Conducting Neuromorphic Materials and Devices

Complex Oxides for Brain‐Inspired Computing: A Review

Quantum materials for energy-efficient neuromorphic computing

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