Accurate fourth-generation machine learning potentials by electrostatic embedding

TW Ko, JA Finkler, S Goedecker… - Journal of Chemical …, 2023 - ACS Publications
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 …

High-surface-area functionalized nanolaminated membranes for energy-efficient nanofiltration and desalination in forward osmosis

W Wang, N Onofrio, E Petit, BA Karamoko, H Wu, J Liu… - Nature Water, 2023 - nature.com
Stacking two-dimensional nanosheets into laminar membranes to create nanochannels has
attracted widespread attention at both fundamental and practical levels in separation …

Computational Study on Filament Growth Dynamics in Microstructure-Controlled Storage Media of Resistive Switching Memories

P Xu, W Fa, S Chen - ACS nano, 2023 - ACS Publications
The filament growth processes, crucial to the performance of nanodevices like resistive
switching memories, have been widely investigated to realize the device optimization. With …

Comprehensive characterization of the structure of Zr-based metallic glasses

D Lahiri, KVM Krishna, AK Verma, P Modak… - Scientific Reports, 2024 - nature.com
Abstract Structure of metallic glasses fascinates as the generic amorphous structural
template for ubiquitous systems. Its specification necessitates determination of the complete …

Boron nitride and molybdenum disulfide as 2D composite element selectors with flexible threshold switching

Y Sun, D Wen, F Sun - Journal of Alloys and Compounds, 2021 - Elsevier
Abstract 2D composite nanomaterials of insulating boron nitride (BN) and semiconductor
molybdenum disulfide (MoS 2)-based flexible threshold switching selectors (Ag/BN+ MoS …

Density functional simulations of a conductive bridging random access memory cell: Ag filament formation in amorphous

J Akola, K Konstantinou, RO Jones - Physical Review Materials, 2022 - APS
Density functional/molecular dynamics simulations have been performed to shed light on the
drift of Ag atoms in an amorphous Ge S 2 solid-state electrolyte between Ag and Pt …

Development of a Generally Applicable Machine Learning Potential with Accurate Long-Range Electrostatic Interactions

TW Ko - 2022 - ediss.uni-goettingen.de
Abstract Machine learning potentials (MLPs) have become an indispensable tool for large-
scale atomistic simulations, due to their accuracy comparable with ab-initio methods at …