Publisher Correction: Atomistic simulations of out-of-equilibrium quantum nuclear dynamics.
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Works matching IS 20573960 AND DT 2025 AND VI 11 AND IP 1
Results: 142
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Electronic properties of stacking faults in Bernal graphite.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01641-2
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Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01639-w
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Unexpected density functional dependence of the antipolar Pbcn phase in HfO<sub>2</sub>.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01647-w
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5
Electro-chemo-mechanical modelling of structural battery composite full cells.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01646-x
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6
Computational discovery of metallic MBenes for two-dimensional semiconductor contacts approaching the quantum limit.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01640-3
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Interpretable machine learning for atomic scale magnetic anisotropy in quantum materials.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01637-y
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8
Crystal-like thermal transport in amorphous carbon.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01625-2
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9
Predicting practical reduction potential of electrolyte solvents via computational hydrogen electrode and interpretable machine-learning models.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01582-w
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Experimentally validated inverse design of FeNiCrCoCu MPEAs and unlocking key insights with explainable AI.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01600-x
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11
Ferroelectric control of diverse hyperbolic polaritons in the visible spectrum.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01644-z
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Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01636-z
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Benchmarking machine learning models for predicting lithium ion migration.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01571-z
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14
Simulating magnetic transition states via string method in first principle calculation.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01603-8
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15
JAX-BTE: a GPU-accelerated differentiable solver for phonon Boltzmann transport equations.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01635-0
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16
Cartesian atomic moment machine learning interatomic potentials.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01623-4
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17
Multidimensional coherent spectroscopy of correlated lattice systems.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01619-0
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18
Polarization switching of HfO<sub>2</sub> ferroelectric in bulk and electrode/ferroelectric/electrode heterostructure.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01633-2
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19
Machine learned potential for high-throughput phonon calculations of metal—organic frameworks.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01611-8
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20
Dzyaloshinskii–Moriya interaction manipulation in multiferroic Janus monolayers.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01585-7
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21
Exhaustive screening of high-fold degenerate topological semimetal with chiral structure.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01624-3
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22
Unraveling charge effects on interface reactions and dendrite growth in lithium metal anode.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01615-4
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23
Ab initio mechanisms and design principles for photodesorption from TiO<sub>2</sub>.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01612-7
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24
Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01629-y
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An extensible open-source solution for research digitalisation in materials science.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01618-1
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26
Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01599-1
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27
Verification and validation of zero-point electron-phonon renormalization of the bandgap, mass enhancement, and spectral functions.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01587-5
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28
High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01617-2
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29
Electron-mediated anharmonicity and its role in the Raman spectrum of graphene.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01610-9
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30
Strain and ligand effects in the 1-D limit: reactivity of steps.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01616-3
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31
Photoinduced ferroelectric phase transition triggering photocatalytic water splitting.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01601-w
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32
Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01608-3
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A machine learning model with minimize feature parameters for multi-type hydrogen evolution catalyst prediction.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01607-4
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ELEQTRONeX: A GPU-accelerated exascale framework for non-equilibrium quantum transport in nanomaterials.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01604-7
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35
Uncertainty quantification for neural network potential foundation models.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01572-y
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36
Hierarchy-boosted funnel learning for identifying semiconductors with ultralow lattice thermal conductivity.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01583-9
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37
Insights into oxygen diffusion in rare earth disilicate environmental barrier coatings.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-024-01508-y
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38
Design of BCC/FCC dual-solid solution refractory high-entropy alloys through CALPHAD, machine learning and experimental methods.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01597-3
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P-type dopability in Half-Heusler thermoelectric semiconductors.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01595-5
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40
Machine learning enabled accurate prediction of structural and magnetic properties of cobalt ferrite.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01598-2
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41
Atomistic simulations of out-of-equilibrium quantum nuclear dynamics.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01588-4
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42
Magnons from time-dependent density-functional perturbation theory and nonempirical Hubbard functionals.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01570-0
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43
Optical line shapes of color centers in solids from classical autocorrelation functions.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01565-x
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Transforming machine learning model knowledge into material insights for multi-principal-element superalloy phase design.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01578-6
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45
Generative deep learning for predicting ultrahigh lattice thermal conductivity materials.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01592-8
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46
Bayesian exploration of the composition space of CuZrAl metallic glasses for mechanical properties.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01591-9
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47
A general approach for determining applicability domain of machine learning models.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01573-x
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48
Increased Curie temperature in lithium substituted ferroelectric niobate perovskite via soft polar mode enhancement.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01584-8
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49
Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01579-5
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50
Machine learning and high-throughput computational guided development of high temperature oxidation-resisting Ni-Co-Cr-Al-Fe based high-entropy alloys.
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- NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-025-01568-8
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