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 …
Predicting lattice thermal conductivity via machine learning: a mini review
Y Luo, M Li, H Yuan, H Liu, Y Fang - NPJ Computational Materials, 2023 - nature.com
Over the past few decades, molecular dynamics simulations and first-principles calculations
have become two major approaches to predict the lattice thermal conductivity (κ L), which …
have become two major approaches to predict the lattice thermal conductivity (κ L), which …
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 …
A machine-learning-based investigation on the mechanical/failure response and thermal conductivity of semiconducting BC2N monolayers
Graphene-like lattices consisting of neighboring elements of boron, carbon and nitrogen are
currently among the most attractive two-dimensional (2D) nanomaterials. Most recently, a …
currently among the most attractive two-dimensional (2D) nanomaterials. Most recently, a …
Anisotropic and outstanding mechanical, thermal conduction, optical, and piezoelectric responses in a novel semiconducting BCN monolayer confirmed by first …
Graphene-like nanomembranes made of the neighboring elements of boron, carbon and
nitrogen elements, are well-known of showing outstanding physical properties. Herein, with …
nitrogen elements, are well-known of showing outstanding physical properties. Herein, with …
When Machine Learning Meets 2D Materials: A Review
B Lu, Y Xia, Y Ren, M Xie, L Zhou, G Vinai… - Advanced …, 2024 - Wiley Online Library
The availability of an ever‐expanding portfolio of 2D materials with rich internal degrees of
freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique …
freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique …
A theoretical investigation of the structural, electronic and mechanical properties of pristine and nitrogen-terminated carbon nanoribbons composed of 4–5–6–8 …
B Mortazavi - Journal of Composites Science, 2023 - mdpi.com
Among the exciting recent advances in the field of carbon-based nanomaterials, the
successful realization of a carbon nanoribbon composed of 4–5–6–8-membered rings (ACS …
successful realization of a carbon nanoribbon composed of 4–5–6–8-membered rings (ACS …
Ultrahigh strength and negative thermal expansion and low thermal conductivity in graphyne nanosheets confirmed by machine-learning interatomic potentials
B Mortazavi, X Zhuang - FlatChem, 2022 - Elsevier
After several decades of experimental endeavors, most recently large-area γ-graphyne
layered materials have been synthesized via a reversible dynamic alkyne metathesis …
layered materials have been synthesized via a reversible dynamic alkyne metathesis …
Machine Learning Interatomic Potentials: Keys to First-Principles Multiscale Modeling
B Mortazavi - Machine Learning in Modeling and Simulation …, 2023 - Springer
Abstract Machine learning interatomic potentials (MLIPs) provide exceptional opportunities
to accurately simulate atomistic systems and/or accelerate the evaluation of diverse physical …
to accurately simulate atomistic systems and/or accelerate the evaluation of diverse physical …
Low and anisotropic tensile strength and thermal conductivity in the single-layer fullerene network predicted by machine-learning interatomic potentials
B Mortazavi, X Zhuang - Coatings, 2022 - mdpi.com
In the latest ground-breaking experimental advancement (Nature (2022), 606, 507), zero-
dimensional fullerenes (C60) have been covalently bonded to form single-layer two …
dimensional fullerenes (C60) have been covalently bonded to form single-layer two …