General-purpose machine-learned potential for 16 elemental metals and their alloys

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Nov. 25, 2024

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum elements and their alloys limits applicability. Here, we present promising approach constructing unified MLP numerous elements, demonstrated through model (UNEP-v1) 16 elemental metals alloys. To achieve complete representation chemical space, show, via principal component analysis diverse test datasets, that employing one-component two-component systems suffices. Our UNEP-v1 exhibits superior performance across various physical properties compared to widely used embedded-atom method potential, while maintaining efficiency. We demonstrate our approach's effectiveness reproducing experimentally observed order stable phases, large-scale simulations plasticity primary radiation damage in MoTaVW

Language: Английский

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Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials

Journal of Applied Physics, Journal Year: 2024, Volume and Issue: 135(16)

Published: April 24, 2024

Molecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably predicting is the use accurate efficient interatomic potentials. Recently, machine-learned potentials (MLPs) have shown great promise providing required accuracy a broad range In this mini-review tutorial, we delve into fundamentals transport, explore pertinent MD simulation methods, survey applications MLPs transport. Furthermore, provide step-by-step tutorial on developing highly predictive simulations, utilizing neuroevolution as implemented GPUMD package. Our aim with to empower researchers valuable insights cutting-edge methodologies that can significantly enhance efficiency studies.

Language: Английский

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Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(8), P. 3273 - 3284

Published: April 4, 2024

Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids, solids, as spectra contain a wealth information concerning, in particular, dynamics these systems. Atomic scale simulations can be to predict such but often severely limited due high computational cost or need strong approximations that limit application range reliability. Here, we introduce machine learning (ML) accelerated approach addresses shortcomings provides significant performance boost terms data efficiency compared with earlier ML schemes. To this end, generalize neuroevolution potential enable prediction rank one two tensors obtain tensorial (TNEP) scheme. We apply resulting framework construct models dipole moment, polarizability, susceptibility molecules, solids show our compares favorably several from literature respect accuracy efficiency. Finally, demonstrate TNEP infrared liquid water, molecule (PTAF

Language: Английский

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Phonon coherence and minimum thermal conductivity in disordered superlattices

Physical review. B./Physical review. B, Journal Year: 2025, Volume and Issue: 111(8)

Published: Feb. 12, 2025

Language: Английский

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Octahedral Tilt-Driven Phase Transitions in BaZrS₃ Chalcogenide Perovskite

The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: unknown, P. 2064 - 2071

Published: Feb. 19, 2025

Chalcogenide perovskites are lead-free materials for potential photovoltaic or thermoelectric applications. BaZrS3 is the most-studied member of this family due to its superior thermal and chemical stability, desirable optoelectronic properties, low conductivity. Phase transitions in remain underexplored literature, as most experimental characterizations material have been performed at ambient conditions where orthorhombic Pnma phase reported be stable. In work, we study dynamics across a range temperatures pressures using an accurate machine learning interatomic trained with data from hybrid density functional theory calculations. At 0 Pa, find first-order transition tetragonal I4/mcm 610 K, second-order cubic Pm3̅m 880 K. The stable over larger temperature higher pressures. To confirm validity our model compare results published report prediction X-ray diffraction pattern function temperature.

Language: Английский

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Advances in modeling complex materials: The rise of neuroevolution potentials

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Interatomic potentials are essential for driving molecular dynamics (MD) simulations, directly impacting the reliability of predictions regarding physical and chemical properties materials. In recent years, machine-learned (MLPs), trained against first-principles calculations, have become a new paradigm in materials modeling as they provide desirable balance between accuracy computational cost. The neuroevolution potential (NEP) approach, implemented open-source GPUMD software, has emerged promising potential, exhibiting impressive exceptional efficiency. This review provides comprehensive discussion on methodological practical aspects NEP along with detailed comparison other representative state-of-the-art MLP approaches terms training accuracy, property prediction, We also demonstrate application approach to perform accurate efficient MD addressing complex challenges that traditional force fields typically cannot tackle. Key examples include structural liquid amorphous materials, order alloy systems, phase transitions, surface reconstruction, material growth, primary radiation damage, fracture two-dimensional nanoscale tribology, mechanical behavior compositionally alloys under various loadings. concludes summary perspectives future extensions further advance this rapidly evolving field.

Language: Английский

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Thermal and mechanical properties of deep-ultraviolet light sources candidate materials BeGeN2 by machine-learning molecular dynamics simulations

Physical Review Materials, Journal Year: 2025, Volume and Issue: 9(3)

Published: March 24, 2025

Language: Английский

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Grain boundary segregation spectra from a generalized machine-learning potential

Scripta Materialia, Journal Year: 2025, Volume and Issue: 264, P. 116682 - 116682

Published: April 16, 2025

Language: Английский

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Optical line shapes of color centers in solids from classical autocorrelation functions

npj Computational Materials, Journal Year: 2025, Volume and Issue: 11(1)

Published: April 16, 2025

Language: Английский

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Impact of Organic Spacers and Dimensionality on Templating of Halide Perovskites

ACS Energy Letters, Journal Year: 2024, Volume and Issue: 9(8), P. 3947 - 3954

Published: July 18, 2024

Two-dimensional (2D) halide perovskites (HPs) are promising materials for various optoelectronic applications; yet, a comprehensive understanding of their dynamics is still elusive. Here, we offer insight into the prototypical 2D HPs based on MAPbI3 as function linker molecule and number perovskite layers using atomic-scale simulations. We show that closest to undergo transitions distinct from those interior layers. These can take place anywhere between few tens Kelvin degrees below more than 100 K above cubic–tetragonal transition bulk MAPbI3. In combination with thickness layer, this enables one template phase tune over wide temperature range. Our results thereby reveal details an important generalizable design mechanism tuning properties these materials.

Language: Английский

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