Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials
Since the birth of the concept of machine learning interatomic potentials (MLIPs) in 2007, a
growing interest has been developed in the replacement of empirical interatomic potentials …
growing interest has been developed in the replacement of empirical interatomic potentials …
MAGUS: machine learning and graph theory assisted universal structure searcher
Crystal structure predictions based on first-principles calculations have gained great
success in materials science and solid state physics. However, the remaining challenges …
success in materials science and solid state physics. However, the remaining challenges …
DeePMD-kit v2: A software package for deep potential models
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …
simulations using machine learning potentials known as Deep Potential (DP) models. This …
General-purpose machine-learned potential for 16 elemental metals and their alloys
Abstract Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the
lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their …
lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their …
E (n)-Equivariant cartesian tensor message passing interatomic potential
Abstract Machine learning potential (MLP) has been a popular topic in recent years for its
capability to replace expensive first-principles calculations in some large systems …
capability to replace expensive first-principles calculations in some large systems …
Machine learning for polaritonic chemistry: Accessing chemical kinetics
Altering chemical reactivity and material structure in confined optical environments is on the
rise, and yet, a conclusive understanding of the microscopic mechanisms remains elusive …
rise, and yet, a conclusive understanding of the microscopic mechanisms remains elusive …
Pressure stabilized lithium-aluminum compounds with both superconducting and superionic behaviors
Superconducting and superionic behaviors have physically intriguing dynamic properties of
electrons and ions, respectively, both of which are conceptually important and have great …
electrons and ions, respectively, both of which are conceptually important and have great …
Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations
Amorphous silicon (a-Si) is an important thermal-management material and also serves as
an ideal playground for studying heat transport in strongly disordered materials. Theoretical …
an ideal playground for studying heat transport in strongly disordered materials. Theoretical …
Atomistic insights into the mechanical anisotropy and fragility of monolayer fullerene networks using quantum mechanical calculations and machine-learning …
In this work, we comprehensively study the mechanical properties of the newly synthesized
monolayer quasi-hexagonal-phase fullerene (qHPF) membrane [Hou et al., 2022] under …
monolayer quasi-hexagonal-phase fullerene (qHPF) membrane [Hou et al., 2022] under …
Anisotropic and high thermal conductivity in monolayer quasi-hexagonal fullerene: A comparative study against bulk phase fullerene
Recently a novel two-dimensional (2D) C 60 based crystal called quasi-hexagonal-phase
fullerene (QHPF) has been fabricated and demonstrated to be a promising candidate for 2D …
fullerene (QHPF) has been fabricated and demonstrated to be a promising candidate for 2D …