Structure Prediction of Ionic Epitaxial Interfaces with Ogre Demonstrated for Colloidal Heterostructures of Lead Halide Perovskites

ACS Nano, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 2, 2025

Colloidal epitaxial heterostructures are nanoparticles composed of two different materials connected at an interface, which can exhibit properties from those their individual components. Combining dissimilar offers exciting opportunities to create a wide variety functional heterostructures. However, assessing structural compatibility─the main prerequisite for growth─is challenging when pairing complex with lattice parameters and crystal structures. This complicates both the selection target synthesis assignment interface models new obtained. Here, we demonstrate Ogre as powerful tool accelerate design characterization colloidal To this end, implemented developments tailored high-efficiency prediction interfaces between ionic/polar materials, encompass most semiconductors. These include use pre-screening candidate based on charge balance classical potential fast energy evaluations, automatically calculated input bulk validated perovskite-based CsPbBr3/Pb4S3Br2 heterostructures, where produces in excellent agreement density theory experiments. Furthermore, rationalize templating effect CsPbCl3 growth lead sulfochlorides, perovskite seeds induce formation Pb4S3Cl2 rather than Pb3S2Cl2 due better compatibility. Finally, combining simulations experimental data enables us unravel structure composition hitherto unsolved CsPbBr3/BixPbySz assign several other reported metal halide- oxide-based interfaces. The package is available GitHub or via OgreInterface desktop application, Windows, Linux, Mac.

Language: Английский

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Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(6)

Published: Feb. 12, 2025

Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity often makes it challenging to address potential finite-size effects. Here, we present a specialized graphics processing units (GPUs) implementation of PIMD methods, including ring-polymer (RPMD) and thermostatted (TRPMD), into the open-source Graphics Processing Units Molecular Dynamics (GPUMD) package, combined with highly accurate efficient machine-learned neuroevolution (NEP) models. This approach achieves almost accuracy first-principles calculations efficiency empirical potentials, enabling large-scale atomistic that incorporate effects, effectively overcoming limitations at relatively affordable cost. We validate demonstrate efficacy NEP-PIMD by examining various thermal properties diverse materials, lithium hydride (LiH), three porous metal–organic frameworks (MOFs), liquid water, elemental aluminum. For LiH, our successfully capture isotope effect, reproducing experimentally observed dependence lattice parameter on reduced mass. MOFs, results reveal achieving good agreement experimental data requires consideration both dispersive interactions. significant impact its microscopic structure. aluminum, TRPMD method captures expansion phonon properties, aligning well mechanical predictions. GPU-accelerated GPUMD package provides an alternative, accessible, accurate, scalable tool exploring complex material influenced applications across broad range

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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Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains

Small, Journal Year: 2023, Volume and Issue: 20(3)

Published: Sept. 21, 2023

Abstract Metal halide perovskites are multifunctional semiconductors with tunable structures and properties. They highly dynamic crystals complex octahedral tilting patterns strongly anharmonic atomic behavior. In the higher temperature, symmetry phases of these materials, several structural features observed. The local structure can differ greatly from average there is evidence that 2D correlated motion form. An understanding underlying atomistic dynamics is, however, still lacking. this work, inorganic perovskite CsPbI 3 investigated using a new machine learning force field based on cluster expansion framework. Through analysis temporal spatial correlation observed during large‐scale simulations, it revealed low frequency tilts implies double‐well effective potential landscape, even well into cubic phase. Moreover, regions lower present within both phases. These planar length timescales reported. Finally, arrangement their interactions visualized, providing comprehensive picture in

Language: Английский

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Sensing Utilities of Cesium Lead Halide Perovskites and Composites: A Comprehensive Review

Sensors, Journal Year: 2024, Volume and Issue: 24(8), P. 2504 - 2504

Published: April 13, 2024

Recently, the utilization of metal halide perovskites in sensing and their application environmental studies have reached a new height. Among different perovskites, cesium lead (CsPbX3; X = Cl, Br, I) composites attracted great interest applications owing to exceptional optoelectronic properties. Most CsPbX3 nanostructures possess structural stability, luminescence, electrical properties for developing distinct optical photonic devices. When exposed light, heat, water, can display stable utilities. Many been reported as probes detection diverse analytes, such ions, anions, important chemical species, humidity, temperature, radiation photodetection, so forth. So far, covering all metallic organic–inorganic already reviewed many studies. Nevertheless, detailed review utilities could be helpful researchers who are looking innovative designs using these nanomaterials. Herein, we deliver thorough composites, quantitation chemicals, explosives, bioanalytes, pesticides, fungicides, cellular imaging, volatile organic compounds (VOCs), toxic gases, radiation, photodetection. Furthermore, this also covers synthetic pathways, design requirements, advantages, limitations, future directions material.

Language: Английский

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calorine: A Python package for constructing and sampling neuroevolution potential models

The Journal of Open Source Software, Journal Year: 2024, Volume and Issue: 9(95), P. 6264 - 6264

Published: March 6, 2024

Molecular dynamics (MD) simulations are a key tool in computational chemistry, physics, and materials science, aiding the understanding of microscopic processes but also guiding development novel materials.A MD simulation requires model for interatomic interactions.To this end, one traditionally often uses empirical potentials or force fields, which fast inaccurate, ab-initio methods based on electronic structure theory such as density functional theory, accurate computationally very expensive (Müser et al., 2023).Machine-learned (MLIPs) have recent years emerged an alternative to these approaches, combining speed heuristic fields with accuracy techniques (Unke 2021).Neuroevolution (NEPs), implemented GPUMD package, particular, highly efficient class MLIPs (Fan 2021, 2022;Fan, 2022).NEP models already been used study variety properties range materials, examples including radiation damage tungsten (Liu 2023), phase transitions (Fransson, Wiktor, 2023) halide perovskites Rosander, well thermal transport two-dimensional (Sha 2023).Here, we present calorine, Python package that simplifies construction, analysis use NEP via GPUMD.

Language: Английский

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To Pair or not to Pair? Machine-Learned Explicitly-Correlated Electronic Structure for NaCl in Water

The Journal of Physical Chemistry Letters, Journal Year: 2024, Volume and Issue: 15(23), P. 6081 - 6091

Published: May 31, 2024

The extent of ion pairing in solution is an important phenomenon to rationalize transport and thermodynamic properties electrolytes. A fundamental measure this the potential mean force (PMF) between solvated ions. relative stabilities paired solvent shared states PMF barrier them are highly sensitive underlying energy surface. However, direct application accurate electronic structure methods challenging, since long simulations required. We develop wave function based machine learning potentials with random phase approximation (RPA) second order Møller–Plesset (MP2) perturbation theory for prototypical system Na Cl ions water. show both agreement, predicting have similar energies (within 0.2 kcal/mol). also provide same benchmarks different DFT functionals as well insight into on simple analyses interactions system.

Language: Английский

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Polarizability models for simulations of finite temperature Raman spectra from machine learning molecular dynamics

Physical Review Materials, Journal Year: 2024, Volume and Issue: 8(4)

Published: April 12, 2024

While the efficacy of machine learning (ML) force fields in simulating molecular dynamics (MD) trajectories has already been well established, Raman spectra from them requires polarizability models which are much less explored. In this work, three compared using widely different materials, namely boron arsenide, 2D molybdenum disulfide and inorganic halide perovskites. The obtained combination with ML MD to experiments, allowing us highlight advantages shortcomings each model.

Language: Английский

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Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations

Chemistry of Materials, Journal Year: 2023, Volume and Issue: 35(17), P. 6737 - 6744

Published: Aug. 21, 2023

Halide perovskites have emerged as one of the most interesting materials for optoelectronic applications due to their favorable properties, such defect tolerance and long charge carrier lifetimes, which are attributed dynamic softness. However, this softness has led apparent disagreements between local instantaneous global average structures these materials. In study, we rationalize situation through an assessment tilt angles octahedra in perovskite structure using large-scale molecular dynamics simulations based on machine-learned potentials trained density functional theory calculations. We compare structural properties given by different functionals [local approximation, PBE, PBE + D3, PBEsol, strongly constrained appropriately normed (SCAN), SCAN rVV10, van der Waals with consistent exchange] establish trends across a family CsMX3 M = Sn or Pb X Cl, Br I. Notably, demonstrate strong short-range ordering cubic phase halide perovskites. This is reminiscent tetragonal provides bridge disordered arrangement. Our results provide deeper understanding distortions, crucial further properties.

Language: Английский

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ACEpotentials.jl: A Julia implementation of the atomic cluster expansion

The Journal of Chemical Physics, Journal Year: 2023, Volume and Issue: 159(16)

Published: Oct. 23, 2023

We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion [R. Drautz, Phys. Rev. B 99, 014104 (2019)]. As latter provides complete description of atomic environments, including invariance to overall translation and rotation as well permutation like atoms, resulting are systematically improvable efficient. Furthermore, descriptor's expressiveness enables use linear model, facilitating rapid evaluation straightforward application Bayesian techniques for active learning. summarize capabilities ACEpotentials.jl demonstrate its strengths (simplicity, interpretability, robustness, performance) on selection prototypical atomistic modelling workflows.

Language: Английский

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