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

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

(2019) Henry Chan et al.   Journal of Physical Chemistry C

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

(2019) Wei Zhang et al.   Nature Reviews Materials

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

(2019) Mattia Boniardi et al.   Physica Status Solidi-Rapid Research Letters

Aging in Phase Change Materials: Getting Insight from Simulation

(2019) Jean-Yves Raty   Physica Status Solidi-Rapid Research Letters

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

Phase-change memory cycling endurance

(2019) SangBum Kim et al.   MRS BULLETIN

Predicting the dissolution kinetics of silicate glasses using machine learning

(2018) N.M. Anoop Krishnan et al.   JOURNAL OF NON-CRYSTALLINE SOLIDS

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

(2018) Gabriele C. Sosso et al.   MOLECULAR SIMULATION

Monatomic phase change memory

(2018) Martin Salinga et al.   NATURE MATERIALS

A machine learning approach for engineering bulk metallic glass alloys

(2018) Logan Ward et al.   ACTA MATERIALIA

Predicting glass transition temperatures using neural networks

(2018) Daniel R. Cassar et al.   ACTA MATERIALIA

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

(2018) Hans Weber et al.   PHYSICAL REVIEW MATERIALS

Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires

(2017) S. Gabardi et al.   Journal of Physical Chemistry C

Phase-change materials for non-volatile photonic applications

(2017) M. Wuttig et al.   Nature Photonics

Reducing the stochasticity of crystal nucleation to enable subnanosecond memory writing

(2017) Feng Rao et al.   SCIENCE

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

(2017) Elisabetta Palumbo et al.   SOLID-STATE ELECTRONICS

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Neural network potential for Al-Mg-Si alloys

(2017) Ryo Kobayashi et al.   PHYSICAL REVIEW MATERIALS

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

(2017) Jamie M. Wynn et al.   PHYSICAL REVIEW MATERIALS

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

(2016) Gabriele C. Sosso et al.   CHEMICAL REVIEWS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

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

(2016) Bin Chen et al.   Scientific Reports

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

(2015) Volker L. Deringer et al.   ADVANCED FUNCTIONAL MATERIALS

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

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

(2015) Davide Campi et al.   JOURNAL OF APPLIED PHYSICS

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

(2015) Wei Zhang et al.   MRS BULLETIN

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

(2015) S. Gabardi et al.   PHYSICAL REVIEW B

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

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

(2014) Gabriele C. Sosso et al.   JOURNAL OF PHYSICAL CHEMISTRY B

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

(2013) Gabriele C. Sosso et al.   Journal of Physical Chemistry Letters

The race of phase change memories to nanoscale storage and applications

(2013) Andrea L. Lacaita et al.   MICROELECTRONIC ENGINEERING

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

(2013) Federico Zipoli et al.   NEW JOURNAL OF PHYSICS

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

(2012) G. C. Sosso et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

Thermal transport in phase-change materials from atomistic simulations

(2012) Gabriele C. Sosso et al.   PHYSICAL REVIEW B

Neural network interatomic potential for the phase change material GeTe

(2012) Gabriele C. Sosso et al.   PHYSICAL REVIEW B

Design Rules for Phase-Change Materials in Data Storage Applications

(2011) Dominic Lencer et al.   ADVANCED MATERIALS

Interfacial phase-change memory

(2011) R. E. Simpson et al.   Nature Nanotechnology

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

(2008) J. Hegedüs et al.   NATURE MATERIALS