On the public citation graph

Harnessing machine learning potentials to understand the functional properties of phase-change materials

https://doi.org/10.1557/mrs.2019.202

0 signed · 81 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/adfm.201500826openalex-snapshot

Microscopic Complexity in Phase‐Change Materials and its Role for Applications

inferredcites →https://doi.org/10.1002/adma.201004255openalex-snapshot

Design Rules for Phase‐Change Materials in Data Storage Applications

inferredcites →https://doi.org/10.1002/anie.201703114openalex-snapshot

First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems

inferredcites →https://doi.org/10.1002/pssb.201200355openalex-snapshot

Breakdown of Stokes–Einstein relation in the supercooled liquid state of phase change materials

inferredcites →https://doi.org/10.1002/pssr.201800590openalex-snapshot

Aging in Phase Change Materials: Getting Insight from Simulation

inferredcites →https://doi.org/10.1002/pssr.201800634openalex-snapshot

Evidence for Thermal‐Based Transition in Super‐Lattice Phase Change Memory

inferredcites →https://doi.org/10.1002/qua.24927openalex-snapshot

G aussian approximation potentials: A brief tutorial introduction

inferredcites →https://doi.org/10.1007/978-3-319-50257-1_100-1openalex-snapshot

Mechanical and Compositional Design of High-Strength Corning Gorilla® Glass

inferredcites →https://doi.org/10.1016/j.actamat.2018.08.002openalex-snapshot

A machine learning approach for engineering bulk metallic glass alloys

inferredcites →https://doi.org/10.1016/j.actamat.2018.08.022openalex-snapshot

Predicting glass transition temperatures using neural networks

inferredcites →https://doi.org/10.1016/j.jnoncrysol.2018.02.023openalex-snapshot

Predicting the dissolution kinetics of silicate glasses using machine learning

inferredcites →https://doi.org/10.1016/j.mee.2013.02.105openalex-snapshot

The race of phase change memories to nanoscale storage and applications

inferredcites →https://doi.org/10.1016/j.sse.2017.03.016openalex-snapshot

Forming operation in Ge-rich Ge x Sb y Te z phase change memories

inferredcites →https://doi.org/10.1016/s0022-3093(02)01859-8openalex-snapshot

A machine learning approach to the estimation of the liquidus temperature of glass-forming oxide blends

inferredcites →https://doi.org/10.1021/acs.chemrev.5b00744openalex-snapshot

Crystal Nucleation in Liquids: Open Questions and Future Challenges in Molecular Dynamics Simulations

inferredcites →https://doi.org/10.1021/acs.jpcb.8b06476openalex-snapshot

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential

inferredcites →https://doi.org/10.1021/acs.jpcc.5b00296openalex-snapshot

Heterogeneous Crystallization of the Phase Change Material GeTe via Atomistic Simulations

inferredcites →https://doi.org/10.1021/acs.jpcc.7b09862openalex-snapshot

Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires

inferredcites →https://doi.org/10.1021/acs.jpcc.8b09917openalex-snapshot

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

inferredcites →https://doi.org/10.1021/jp507361fopenalex-snapshot

Dynamical Heterogeneity in the Supercooled Liquid State of the Phase Change Material GeTe

inferredcites →https://doi.org/10.1021/jz402268vopenalex-snapshot

Fast Crystallization of the Phase Change Compound GeTe by Large-Scale Molecular Dynamics Simulations

inferredcites →https://doi.org/10.1038/nmat2009openalex-snapshot

Phase-change materials for rewriteable data storage

inferredcites →https://doi.org/10.1038/nmat2157openalex-snapshot

Microscopic origin of the fast crystallization ability of Ge–Sb–Te phase-change memory materials

inferredcites →https://doi.org/10.1038/nnano.2011.96openalex-snapshot

Interfacial phase-change memory

inferredcites →https://doi.org/10.1038/nphoton.2017.126openalex-snapshot

Phase-change materials for non-volatile photonic applications

inferredcites →https://doi.org/10.1038/s41563-018-0110-9openalex-snapshot

Monatomic phase change memory

inferredcites →https://doi.org/10.1038/s41578-018-0076-xopenalex-snapshot

Designing crystallization in phase-change materials for universal memory and neuro-inspired computing

inferredcites →https://doi.org/10.1038/srep39546openalex-snapshot

Size-dependent and tunable crystallization of GeSbTe phase-change nanoparticles

inferredcites →https://doi.org/10.1039/c8fd00101dopenalex-snapshot

Priming effects in the crystallization of the phase change compound GeTe from atomistic simulations

inferredcites →https://doi.org/10.1063/1.2801626openalex-snapshot

Coexistence of tetrahedral- and octahedral-like sites in amorphous phase change materials

inferredcites →https://doi.org/10.1063/1.4904910openalex-snapshot

Electron-phonon interaction and thermal boundary resistance at the crystal-amorphous interface of the phase change compound GeTe

inferredcites →https://doi.org/10.1063/1.4966192openalex-snapshot

Perspective: Machine learning potentials for atomistic simulations

inferredcites →https://doi.org/10.1080/08927022.2018.1447107openalex-snapshot

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

inferredcites →https://doi.org/10.1088/1367-2630/15/12/123006openalex-snapshot

Reactive potential for the study of phase-change materials: GeTe

inferredcites →https://doi.org/10.1103/physrevb.76.235201openalex-snapshot

Structural phase transitions on the nanoscale: The crucial pattern in the phase-change materials Ge2Sb2Te5 and GeTe

inferredcites →https://doi.org/10.1103/physrevb.85.174103openalex-snapshot

Neural network interatomic potential for the phase change material GeTe

inferredcites →https://doi.org/10.1103/physrevb.86.104301openalex-snapshot

Thermal transport in phase-change materials from atomistic simulations

inferredcites →https://doi.org/10.1103/physrevb.92.054201openalex-snapshot

Microscopic origin of resistance drift in the amorphous state of the phase-change compound GeTe

inferredcites →https://doi.org/10.1103/physrevb.95.014114openalex-snapshot

Stratified construction of neural network based interatomic models for multicomponent materials

inferredcites →https://doi.org/10.1103/physrevb.97.094106openalex-snapshot

Implanted neural network potentials: Application to Li-Si alloys

inferredcites →https://doi.org/10.1103/physrevlett.98.146401openalex-snapshot

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

inferredcites →https://doi.org/10.1103/physrevmaterials.1.053604openalex-snapshot

Neural network potential for Al-Mg-Si alloys

inferredcites →https://doi.org/10.1103/physrevmaterials.1.073001openalex-snapshot

Phase diagram of germanium telluride encapsulated in carbon nanotubes from first-principles searches

inferredcites →https://doi.org/10.1103/physrevmaterials.2.093405openalex-snapshot

Correlating ultrafast calorimetry, viscosity, and structural measurements in liquid GeTe and Ge15Te85

inferredcites →https://doi.org/10.1103/physrevmaterials.3.023804openalex-snapshot

Active learning of uniformly accurate interatomic potentials for materials simulation

inferredcites →https://doi.org/10.1109/ted.2003.823243openalex-snapshot

Electronic Switching in Phase-Change Memories

inferredcites →https://doi.org/10.1126/sciadv.1701816openalex-snapshot

Machine learning unifies the modeling of materials and molecules

inferredcites →https://doi.org/10.1126/science.aaa8415openalex-snapshot

Machine learning: Trends, perspectives, and prospects

inferredcites →https://doi.org/10.1126/science.aao3212openalex-snapshot

Reducing the stochasticity of crystal nucleation to enable subnanosecond memory writing

inferredcites →https://doi.org/10.1557/mrs.2015.227openalex-snapshot

Density-functional theory guided advances in phase-change materials and memories

inferredcites →https://doi.org/10.1557/mrs.2019.205openalex-snapshot

Phase-change memory cycling endurance

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4686129openalex-extracted

Nature of the Order Parameter of Glass

inferred← citeshttps://doi.org/10.1002/adfm.202104422openalex-snapshot

Measurement of Onset of Structural Relaxation in Melt‐Quenched Phase Change Materials

inferred← citeshttps://doi.org/10.1002/adfm.202314565openalex-snapshot

Insights into the Heterogeneous Nuclei of an Ultrafast‐Crystallizing Glassy Solid

inferred← citeshttps://doi.org/10.1002/inf2.12315openalex-snapshot

Deep machine learning unravels the structural origin of mid‐gap states in chalcogenide glass for high‐density memory integration

inferred← citeshttps://doi.org/10.1002/msd2.12021openalex-snapshot

Machine‐learning‐based interatomic potentials for advanced manufacturing

inferred← citeshttps://doi.org/10.1002/pssr.202000403openalex-snapshot

Structure and Dynamics of Supercooled Liquid Ge2Sb2Te5 from Machine‐Learning‐Driven Simulations

inferred← citeshttps://doi.org/10.1007/978-3-031-37196-7_5openalex-snapshot

Machine-Learning for Static and Dynamic Electronic Structure Theory

inferred← citeshttps://doi.org/10.1016/j.commatsci.2021.110967openalex-snapshot

A 3D orthogonal vision-based band-gap prediction using deep learning: A proof of concept

inferred← citeshttps://doi.org/10.1016/j.commatsci.2023.112063openalex-snapshot

Prediction of organic material band gaps using graph attention network

inferred← citeshttps://doi.org/10.1016/j.isci.2024.109673openalex-snapshot

Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

inferred← citeshttps://doi.org/10.1016/j.mattod.2020.07.016openalex-snapshot

Unveiling the structural origin to control resistance drift in phase-change memory materials

inferred← citeshttps://doi.org/10.1016/j.mattod.2023.08.001openalex-snapshot

Non-volatile tunable optics by design: From chalcogenide phase-change materials to device structures

inferred← citeshttps://doi.org/10.1016/j.mssp.2021.106080openalex-snapshot

Bonding nature and optical contrast of TiTe2/Sb2Te3 phase-change heterostructure

inferred← citeshttps://doi.org/10.1016/j.xcrp.2023.101760openalex-snapshot

Vacancy-induced phonon localization in boron arsenide using a unified neural network interatomic potential

inferred← citeshttps://doi.org/10.1021/acs.chemrev.1c00022openalex-snapshot

Gaussian Process Regression for Materials and Molecules

inferred← citeshttps://doi.org/10.1021/acs.jpclett.9b03664openalex-snapshot

Quantum Chemistry in the Age of Machine Learning

inferred← citeshttps://doi.org/10.1021/acsami.5c12334openalex-snapshot

Unveiling Defect Motifs in Amorphous GeSe Using Machine Learning Interatomic Potentials

inferred← citeshttps://doi.org/10.1021/acsenergylett.2c01836openalex-snapshot

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

inferred← citeshttps://doi.org/10.1038/s41524-023-01033-4openalex-snapshot

Thermal conductivity of glasses: first-principles theory and applications

inferred← citeshttps://doi.org/10.1039/d0cp01112fopenalex-snapshot

Prediction of optoelectronic properties of Cu 2 O using neural network potential

inferred← citeshttps://doi.org/10.1039/d5tc01074hopenalex-snapshot

Chalcogenide phase-change materials: unveiling new horizons with big data and machine learning

inferred← citeshttps://doi.org/10.1063/5.0006498openalex-snapshot

Hierarchical machine learning of potential energy surfaces

inferred← citeshttps://doi.org/10.1063/5.0055248openalex-snapshot

The seven deadly sins: When computing crystal nucleation rates, the devil is in the details

inferred← citeshttps://doi.org/10.1063/5.0134317openalex-snapshot

Deep language models for interpretative and predictive materials science

inferred← citeshttps://doi.org/10.1073/pnas.2531605123openalex-snapshot

Liquid anomalies and fragility of supercooled antimony

inferred← citeshttps://doi.org/10.1103/physrevmaterials.6.040301openalex-snapshot

Deep dive into machine learning density functional theory for materials science and chemistry

inferred← citeshttps://doi.org/10.1557/mrs.2019.201openalex-snapshot

Phase-change materials in electronics and photonics

inferred← citeshttps://doi.org/10.1615/ihtc17.50-150openalex-snapshot

MODELLING HEAT AND MASS TRANSFER PHENOMENA IN NANOSTRUCTURED MATERIALS FOR THERMAL APPLICATIONS

inferred← citeshttps://doi.org/10.3390/foundations5010009openalex-snapshot

Nature of the Order Parameters of Glass

inferred← citeshttps://doi.org/10.3724/cjos.2025.025openalex-snapshot

AI-driven explorations of the key atomic mechanism of phase-change memory semiconductors

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.