Atomistic insights into the mechanical anisotropy and fragility of monolayer fullerene networks using quantum mechanical calculations and machine-learning molecular dynamics simulations

Extreme Mechanics Letters, Journal Year: 2022, Volume and Issue: 58, P. 101929 - 101929

Published: Nov. 21, 2022

Language: Английский

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Pressure Stabilized Lithium-Aluminum Compounds with Both Superconducting and Superionic Behaviors

Physical Review Letters, Journal Year: 2022, Volume and Issue: 129(24)

Published: Dec. 9, 2022

Superconducting and superionic behaviors have physically intriguing dynamic properties of electrons ions, respectively, both which are conceptually important great potential for practical applications. Whether these two phenomena can appear in the same system is an interesting question. Here, using crystal structure predictions first-principle calculations combined with machine learning, we identify several stable Li-Al compounds electride behavior under high pressure, find that electronic density states some has characteristics two-dimensional electron gas. Among them, estimate Li_{6}Al at 150 GPa a superconducting transition temperature around 29 K enters state wide pressure range. The diffusion found to be affected by attributed atomic collective motion. Our results indicate alkali metal alloys effective platforms study abundant physical their manipulation temperature.

Language: Английский

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Sub-Micrometer Phonon Mean Free Paths in Metal–Organic Frameworks Revealed by Machine Learning Molecular Dynamics Simulations

ACS Applied Materials & Interfaces, Journal Year: 2023, Volume and Issue: 15(30), P. 36412 - 36422

Published: July 23, 2023

Metal-organic frameworks (MOFs) are a family of materials that have high porosity and structural tunability hold great potential in various applications, many which requiring proper understanding the thermal transport properties. Molecular dynamics (MD) simulations play an important role characterizing properties materials. However, due to complexity structures, it is difficult construct accurate empirical interatomic potentials for reliable MD MOFs. To this end, we develop set yet highly efficient machine-learned three typical MOFs, including MOF-5, HKUST-1, ZIF-8, using neuroevolution approach as implemented GPUMD package, perform extensive study Although lattice conductivity (LTC) values MOFs all predicted be smaller than 1 $\rm{W/(m\ K)}$ at room temperature, phonon mean free paths (MFPs) found reach sub-micrometer scale low-frequency region. As consequence, apparent LTC only converges diffusive limit micrometer single crystals, means heavily reduced nanocrystalline The MFPs also correlated with moderate temperature dependence between those crystalline amorphous Both large fundamentally change our

Language: Английский

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Phase Transitions in Inorganic Halide Perovskites from Machine-Learned Potentials

The Journal of Physical Chemistry C, Journal Year: 2023, Volume and Issue: 127(28), P. 13773 - 13781

Published: July 5, 2023

The atomic scale dynamics of halide perovskites have a direct impact not only on their thermal stability but also optoelectronic properties. Progress in machine-learned potentials has recently enabled modeling the finite temperature behavior these materials using fully atomistic methods with near first-principles accuracy. Here, we systematically analyze heating and cooling rate, simulation size, model uncertainty, role underlying exchange-correlation functional phase CsPbX3 X = Cl, Br, I, including both perovskite δ-phases. We show that rates below approximately 60 K/ns system sizes at least few tens thousands atoms should be used to achieve convergence regard parameters. By controlling factors constructing models are specific for different functionals, then assess seven widely semilocal functionals (LDA, vdW-DF-cx, SCAN, SCAN+rVV10, PBEsol, PBE, PBE+D3). based LDA, SCAN+rVV10 agree well experimental data tetragonal-to-cubic-perovskite transition CsPbI3 reasonable agreement perovskite-to-delta temperature. They underestimate, however, orthorhombic-to-tetragonal All other models, those CsPbBr3 CsPbCl3, predict temperatures experimentally observed values all transitions considered here. Among vdW-DF-cx yield closest experiment, followed by PBE+D3. Our work provides guidelines systematic analysis inorganic similar systems. It serves as benchmark further development functionals.

Language: Английский

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Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids

npj Computational Materials, Journal Year: 2023, Volume and Issue: 9(1)

Published: Oct. 6, 2023

Language: Английский

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Accurate prediction of heat conductivity of water by a neuroevolution potential

The Journal of Chemical Physics, Journal Year: 2023, Volume and Issue: 158(20)

Published: May 24, 2023

We propose an approach that can accurately predict the heat conductivity of liquid water. On one hand, we develop accurate machine-learned potential based on neuroevolution-potential achieve quantum-mechanical accuracy at cost empirical force fields. other combine Green-Kubo method and spectral decomposition within homogeneous nonequilibrium molecular dynamics framework to account for quantum-statistical effects high-frequency vibrations. Excellent agreement with experiments under both isobaric isochoric conditions a wide range temperatures is achieved using our approach.

Language: Английский

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Limits of the phonon quasi-particle picture at the cubic-to-tetragonal phase transition in halide perovskites

Communications Physics, Journal Year: 2023, Volume and Issue: 6(1)

Published: July 12, 2023

Abstract The soft modes associated with continuous-order phase transitions are strong anharmonicity. This leads to the overdamped limit where phonon quasi-particle picture can break down. However, this is commonly restricted a narrow temperature range, making it difficult observe its signature feature, namely breakdown of inverse relationship between relaxation time and damping. Here we present physically intuitive based on times mode coordinate conjugate momentum, which at instability approach infinity damping factor, respectively. We demonstrate behavior for cubic-to-tetragonal transition inorganic halide perovskite CsPbBr 3 via molecular dynamics simulations, show that region extends almost 200 K above temperature. Further, investigate how these change when crossing transition.

Language: Английский

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Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics

Physical review. B./Physical review. B, Journal Year: 2023, Volume and Issue: 108(18)

Published: Nov. 20, 2023

Amorphous silica (a-${\mathrm{SiO}}_{2}$) is a foundational disordered material for which the thermal transport properties are important various applications. To accurately model interatomic interactions in classical molecular dynamics (MD) simulations of a-${\mathrm{SiO}}_{2}$, we herein develop an accurate yet highly efficient machine-learned potential that allows us to generate a-${\mathrm{SiO}}_{2}$ samples closely resembling experimentally produced ones. Using homogeneous nonequilibrium MD method and proper quantum-statistical correction results, quantitative agreement with experiments achieved conductivities bulk 190-nm-thick films over wide range temperatures. interrogate vibrations at different temperatures, calculated current correlation functions corresponding transverse acoustic longitudinal collective vibrations. The results reveal that, below Ioffe-Regel crossover frequency, phonons as well-defined excitations remain applicable play predominant role low resulting temperature-dependent increase conductivity. In high-temperature region, more excited, accompanied by intense liquidlike diffusion event. We attribute temperature-independent conductivity collaborative involvement excited phonon scattering heat conduction. These findings provide physical insights into expected be applied vast amorphous materials.

Language: Английский

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Advances of machine learning in materials science: Ideas and techniques

Frontiers of Physics, Journal Year: 2023, Volume and Issue: 19(1)

Published: Nov. 24, 2023

Abstract In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular both industry and academia. recent times, field materials science is also undergoing a revolution, database repositories appearing everywhere. Traditionally, trial-and-error field, computational experimental departments. With advent learning-based techniques, there paradigm shift: can now be screened quickly using ML models even generated based on similar properties; quietly infiltrated many sub-disciplinary under science. However, remains relatively new to expanding its wing quickly. There are plethora readily-available architectures abundance software; The call integrate all these elements comprehensive research procedure becoming an important direction material research. review, we attempt provide introduction reference scientists, covering as much possible commonly used methods applications, discussing future possibilities.

Language: Английский

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Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten

Physical review. B./Physical review. B, Journal Year: 2023, Volume and Issue: 108(5)

Published: Aug. 24, 2023

Simulating collision cascades and radiation damage poses a long-standing challenge for existing interatomic potentials, both in terms of accuracy efficiency. Machine-learning-based potentials have shown sufficiently high simulations, but most ones are still not efficient enough to model high-energy with large space timescales. To this end, we here extend the highly neuroevolution potential (NEP) framework by combining it Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential, obtaining NEP-ZBL framework. We train tungsten demonstrate its elastic properties, melting point, various energetics defects that relevant damage. then perform large-scale molecular dynamics simulations up 8.1 million atoms 240 ps (using single 40-GB A100 GPU) study difference primary bulk thin-foil tungsten. While our findings consistent results simulated embedded atom method models, differs significantly foils shows larger more vacancy clusters as well smaller fewer interstitial produced due presence free surface.

Language: Английский

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