Exploring the Compositional Ternary Diagram of Ge/S/Cu Glasses for Resistance Switching Memories

Ko, Tsz Wai

doi:10.1021/acs.jpcc.8b12020

Cited by 7 publications

(5 citation statements)

References 65 publications

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“…Structure of bulk metallic glasses (BMG) 1 , 2 , has fascinated scientists for (i) its rich physics, manifesting the interplay of geometric, electronic, thermodynamic and kinetic degrees of freedom; (ii) holding the key to glass-forming-ability (GFA) 3 , 4 (or conversely, the science of crystallization) and its unprecedented technological promise 5 – 9 ; (iii) representing the generic structural template for ubiquitous systems ranging from electronic devices 10 , 11 , catalysts 12 , 13 , engineering materials 6 , liquids 14 , biological systems 15 , 16 . Therefore, the importance of structural characterization of glass can never be over-emphasized.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive characterization of the structure of Zr-based metallic glasses

Lahiri,

Krishna,

Verma

et al. 2024

Sci Rep

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Structure of metallic glasses fascinates as the generic amorphous structural template for ubiquitous systems. Its specification necessitates determination of the complete hierarchical structure, starting from short-range-order (SRO) → medium-range-order (MRO) → bulk structure and free volume (FV) distribution. This link has largely remained elusive since previous investigations adopted one-technique-at-a-time approach, focusing on limited aspects of any one domain. Reconstruction of structure from experimental data inversion is non-unique for many of these techniques. As a result, complete and precise structural understanding of glass has not emerged yet. In this work, we demonstrate the first experimental pathway for reconstruction of the integrated structure, for $${\text{Zr}}_{{{67}}} {\text{Ni}}_{{{33}}}$$ Zr 67 Ni 33 and $${\text{Zr}}_{{{52}}} {\text{Ti}}_{{6}} {\text{Al}}_{{{10}}} {\text{Cu}}_{{{18}}} {\text{Ni}}_{{{14}}}$$ Zr 52 Ti 6 Al 10 Cu 18 Ni 14 glasses. Our strategy engages diverse (× 7) multi-scale techniques [XAFS, 3D-APT, ABED/NBED, FEM, XRD, PAS, FHREM] on the same glass. This strategy complemented mutual limitations of techniques and corroborated common parameters to generate complete, self-consistent and precise parameters. Further, MRO domain size and inter-void separation were correlated to identify the presence of FV at MRO boundaries. This enabled the first experimental reconstruction of hierarchical subset: SRO → MRO → FV → bulk structure. The first ever image of intermediate region between MRO domains emerged from this link. We clarify that determination of all subsets is not our objective; the essence and novelty of this work lies in directing the pathway towards finite solution, in the most logical and unambiguous way.

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Section: Introductionmentioning

confidence: 99%

Comprehensive characterization of the structure of Zr-based metallic glasses

Lahiri,

Krishna,

Verma

et al. 2024

Sci Rep

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“…A main limitation of local MLPs is the lack of long-range interactions beyond the cutoff radius, or beyond the maximum information exchange distance in message passing networks, most importantly electrostatics, which prevents the study of many interesting phenomena from biological processes , to electrochemical interfaces. − A straightforward solution is to include long-range electrostatic contribution explicitly employing, e.g., fixed atomic charges , or flexible environment-dependent charges expressed by machine learning. − Other methods such as the long distance equivariant (LODE) representation can also describe the long-range electrostatic interaction implicitly based on a Coulomb-type atomic density potential. Still, methods based on environment-dependent, i.e., local, atomic charges cannot describe long-range charge transfer , and multiple charge states of a system, as this would require knowledge about the structure and global charge of the entire system.…”

Section: Introductionmentioning

confidence: 99%

Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding

Finkler

Goedecker

et al. 2023

J. Chem. Theory Comput.

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In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemistry to materials science. While most current MLPs are based on environment-dependent atomic energies, the limitations of this locality approximation can be overcome, e.g., in fourth-generation MLPs, which incorporate long-range electrostatic interactions based on an equilibrated global charge distribution. Apart from the considered interactions, the quality of MLPs crucially depends on the information available about the system, i.e., the descriptors. In this work we show that includingin addition to structural informationthe electrostatic potential arising from the charge distribution in the atomic environments significantly improves the quality and transferability of the potentials. Moreover, the extended descriptor allows current limitations of two- and three-body based feature vectors to be overcome regarding artificially degenerate atomic environments. The capabilities of such an electrostatically embedded fourth-generation high-dimensional neural network potential (ee4G-HDNNP), which is further augmented by pairwise interactions, are demonstrated for NaCl as a benchmark system. Employing a data set containing only neutral and negatively charged NaCl clusters, even small energy differences between different cluster geometries can be resolved, and the potential shows an impressive transferability to positively charged clusters as well as the melt.

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“…Structure of bulk metallic glasses (BMG) [1][2] , has fascinated scientists for (i) its rich physics, manifesting the interplay of geometric, electronic, thermodynamic and kinetic degrees of freedom; (ii) holding the key to glass-forming-ability (GFA) [3][4] (or conversely, the science of crystallization) and its unprecedented technological promise [5][6][7][8][9] ; (iii) representing the generic structural template for ubiquitous systems ranging from electronic devices [10][11] , catalysts [12][13] , engineering materials 6 , liquids 14 , biological systems [15][16] . Therefore, the importance of structural characterization of glass can never be over-emphasized.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Characterization of the structure of Zr-based metallic glasses

Lahiri,

Krishna,

Verma

et al. 2023

Preprint

View full text Add to dashboard Cite

Structure of metallic glasses fascinates as the generic amorphous structural template for ubiquitous systems. Its specification necessitates determination of the complete hierarchical structure, starting from short-range-order (SRO) → medium-range-order (MRO) → bulk structure and free volume (FV) distribution. This link has largely remained elusive since previous investigations adopted one-technique-at-a-time approach, focusing on limited aspects of any one domain. Reconstruction of structure from experimental data inversion is non-unique for many of these techniques. As a result, complete and precise structural understanding of glass has not emerged yet. In this work, we demonstrate the first experimental pathway for reconstruction of the integrated structure, for Zr67Ni33 and Zr52Ti6Al10Cu18Ni14 glasses. Our strategy engages diverse (7) multi-scale techniques [XAFS, 3D-APT, ABED/NBED, FEM, XRD, PAS, FHREM] on the same glass. This strategy complemented mutual limitations of techniques and corroborated common parameters to generate complete, self-consistent and precise parameters. Further, MRO domain size and inter-void separation were correlated to identify the presence of FV at MRO boundaries. This enabled the first experimental reconstruction of hierarchical subset: SRO → MRO → FV→ bulk structure. The first ever image of intermediate region between MRO domains emerged from this link. We clarify that determination of all subsets is not our objective; the essence and novelty of this work lies in directing the pathway towards finite solution, in the most logical and unambiguous way.

show abstract

Exploring the Compositional Ternary Diagram of Ge/S/Cu Glasses for Resistance Switching Memories

Cited by 7 publications

References 65 publications

Comprehensive characterization of the structure of Zr-based metallic glasses

Comprehensive characterization of the structure of Zr-based metallic glasses

Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding

Comprehensive Characterization of the structure of Zr-based metallic glasses

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