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 …
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
B Mortazavi - Advanced Energy Materials, 2024 - Wiley Online Library
This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …
materials. Initially, ML algorithms were successfully applied to screen materials databases …
Structural, electronic, thermal and mechanical properties of C60-based fullerene two-dimensional networks explored by first-principles and machine learning
B Mortazavi - Carbon, 2023 - Elsevier
Recent experimental reports on the realizations of two-dimensional (2D) networks of the C
60-based fullerenes with anisotropic and nanoporous lattices represent a significant …
60-based fullerenes with anisotropic and nanoporous lattices represent a significant …
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 …
Sub-micrometer phonon mean free paths in metal–organic frameworks revealed by machine learning molecular dynamics simulations
Metal–organic frameworks (MOFs) are a family of materials that have high porosity and
structural tunability and hold great potential in various applications, many of which require a …
structural tunability and hold great potential in various applications, many of which require a …
Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics
Amorphous silica (a-SiO 2) is a foundational disordered material for which the thermal
transport properties are important for various applications. To accurately model the …
transport properties are important for various applications. To accurately model the …
Vibrational anharmonicity results in decreased thermal conductivity of amorphous at high temperature
While the high-temperature thermal transport in crystalline materials has been recently
carefully addressed, it is much less explored for amorphous materials. Most of the existing …
carefully addressed, it is much less explored for amorphous materials. Most of the existing …
The thermoelastic properties of monolayer covalent organic frameworks studied by machine-learning molecular dynamics
Two-dimensional (2D) covalent organic frameworks (COFs) are emerging as promising 2D
polymeric materials with broad applications owing to their unique properties, among which …
polymeric materials with broad applications owing to their unique properties, among which …
Stability and Strength of Monolayer Polymeric C60
B Peng - Nano Letters, 2023 - ACS Publications
Two-dimensional fullerene networks have been synthesized in several forms, and it is
unknown which monolayer form is stable under ambient conditions. Using first-principles …
unknown which monolayer form is stable under ambient conditions. Using first-principles …
[HTML][HTML] Development of a neuroevolution machine learning potential of Pd-Cu-Ni-P alloys
R Zhao, S Wang, Z Kong, Y Xu, K Fu, P Peng, C Wu - Materials & Design, 2023 - Elsevier
Abstract Pd-Cu-Ni-P alloy is an ideal model system of metallic glass known for its
exceptional glass-forming ability. However, few correlation of structures with properties was …
exceptional glass-forming ability. However, few correlation of structures with properties was …