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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Structure-dependent vibrational dynamics of Mg(BH 4 ) 2 polymorphs probed with neutron vibrational spectroscopy and first-principles calculations

We investigated the structure-dependent vibrational properties of different Mg(BH 4 ) 2 polymorphs (α, β, γ, and δ phases) with a combination of neutron vibrational spectroscopy (NVS) measurements and density functional theory (DFT) calculations, with emphasis placed on the effects of the local structure and orientation of the BH 4 - anions. DFT simulations closely match the neutron vibrational spectra. The main bands in the low-energy region (20–80 meV) are associated with the BH4 - librational modes. The features in the intermediate energy region (80–120 meV) are attributed to overtones and combination bands arising from the lower-energy modes. The features in the high-energy region (120–200 meV) correspond to the BH 4 - symmetric and asymmetric bending vibrations, of which four peaks located at 140, 142, 160, and 172 meV are especially intense. There are noticeable intensity distribution variations in the vibrational bands for different polymorphs. We can explain these differences using the spatial distribution of BH 4 - anions within various structures. An example of the possible identification of products after the hydrogenation of MgB 2 , using NVS measurements, is presented. Our results provide fundamental insights of benefit to researchers currently studying these promising hydrogen-storage materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Dynamic formation of preferentially lattice oriented, self trapped hydrogen clusters

Abstract A series of MD and DFT simulations were performed to investigate hydrogen self-clustering and retention in tungsten. Using a newly develop machine learned interatomic potential, spontaneous formation of hydrogen platelets was observed after implanting low-energy hydrogen into tungsten at high fluxes and temperatures. The platelets formed along low miller index orientations and neighboring tetrahedral and octahedral sites and could grow to over 50 atoms in size. High temperatures above 600 K and high hydrogen concentrations were needed to observe significant platelet formation. A critical platelet size of six hydrogen atoms was needed for long term stability. Platelets smaller than this were found to be thermally unstable within a few nanoseconds. To verify these observations, characteristic platelets from the MD simulations were simulated using large-scale DFT. DFT corroborated the MD results in that large platelets were also found to be dynamically stable for five or more hydrogen atoms. The LDOS from the DFT simulated platelets indicated that hydrogen atoms, particularly at the periphery of the platelet, were found to be at least as stable as hydrogen atoms in bulk tungsten. In addition, electrons were found to be localized around hydrogen atoms in the platelet itself and that hydrogen atoms up to 4.2 Å away within the platelet were found to share charge suggesting that the hydrogen atoms are interacting across longer distances than previously suggested. These results reveal a self-clustering mechanisms for hydrogen within tungsten in the absence of radiation induced or microstructural defects that could be a precursor to blistering and potentially explain the experimentally observed high hydrogen retention particularly in the near surface region.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Kohn anomalies and phonon anharmonicity in iridium

Elemental iridium presents surprising challenges for both inelastic neutron scattering (INS) and theoretical thermal transport calculations due to its high neutron absorption cross-section and strong electron-phonon interactions, respectively. Here, in this study, we overcome these challenges to measure temperature-dependent phonon dispersion curves, compare these with calculations based on density functional theory (DFT), and ultimately examine the electron-phonon limited transport behaviors of this material. Our DFT calculations demonstrate Kohn anomalies, near the 𝐾 point of the iridium Brillouin zone, indicating coupling between electrons and phonons. Strong electron-phonon coupling can compete with anharmonic effects to determine electrical and thermal transport behaviors and make the Kohn anomalies challenging to observe. Nonetheless, our INS measurements map these anomalies and other dispersion features over the Brillouin zone from 100 to 700 K. These measurements also uncover unexpectedly large mode specific Grüneisen parameters obtained from the temperature-dependent phonon energies, highlighting strong anharmonicity in iridium. DFT-based Boltzmann transport calculations demonstrate how anharmonicity and electron-phonon couplings determine electronic and lattice transport behaviors. Furthermore, we correlate the Kohn anomalies with calculated electron-phonon nesting functions, Fermi surfaces, and DFT-derived coupling strengths. This study provides detailed insights into the temperature-dependent mode-resolved lattice dynamics and anharmonicity, transport behavior, and electron-phonon interactions.

DFT↗
DOE OSTI2021

J eff = $\frac{3}{2}$ metallic phase and unconventional superconductivity in GaTa 4 Se 8

By means of density functional theory plus dynamical mean-field theory (DFT+DMFT) calculations and resonant inelastic x-ray scattering (RIXS) experiments, we investigate the high-pressure phases of the spin-orbit-coupled Jeff = 3/2 insulator GaTa 4 Se 8 . Its metallic phase, derived from the Mott state by applying pressure, is found to carry J eff = 3/2 moments. The characteristic excitation peak in the RIXS spectrum maintains its destructive quantum interference of J eff at the Ta L2-edge up to 10.4 GPa. Our exact diagonalization based DFT+DMFT calculations including spin-orbit coupling also reveal that the J eff = 3/2 character can be clearly identified under high pressure. These results establish the intriguing nature of the correlated metallic magnetic phase, which represents the first confirmed example of J eff =3/2 moments residing in a metal. They also indicate that the pressure-induced superconductivity is likely unconventional and influenced by these J eff = 3/2 moments. Based on a self-energy analysis, we furthermore propose the possibility of doping-induced superconductivity related to a spin-freezing crossover.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Real-space representation of the quasiparticle self-consistent GW self-energy and its application to defect calculations

The quasiparticle self-consistent (QS) GW (G for Green's function, W for screened Coulomb interaction) approach incorporates the corrections of the quasiparticle energies from their Kohn-Sham density functional theory (DFT) eigenvalues by means of an energy-independent and Hermitian self-energy matrix usually given in the basis set of the DFT eigenstates. By expanding these into an atom-centered basis set (specifically here the linearized muffin-tin orbitals) a real space representation of the self-energy corrections becomes possible. In this work, We show that this representation is relatively short-ranged. This offers opportunities to construct the self-energy of a complex system from parts of the system by a cut-and-paste method. Specifically for a point defect, represented in a large supercell, the self-energy can be constructed from those of the host and a smaller defect-containing cell. The self-energy of the periodic host can be constructed simply from a GW calculation for the primitive cell. We show for the case of the As Ga in GaAs that the defect part can already be well represented by a minimal eight-atom cell and allows us to construct the self-energy for a 64-atom cell in good agreement with direct QSGW calculations for the large cell. Using this approach to an even larger 216-atom cell shows the defect band approaches an isolated defect level. The calculations also allow us to identify a second defect band which appears as a resonance near the conduction band minimum. The results on the extracted defect levels agree well with Green's function calculations for an isolated defect and with experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Electronic properties of doped and defective NiO: A quantum Monte Carlo study

NiO is a canonical Mott (or charge-transfer) insulator and as such is notoriously difficult to describe using density functional theory (DFT) based electronic structure methods. Doped Mott insulators such as NiO are of interest for various applications but rigorous theoretical descriptions are lacking. Here, we use quantum Monte Carlo methods, which very accurately include electron-electron interactions, to examine energetics, charge- and spin-structures of NiO with various point defects, such as vacancies or substitutional doping with potassium. The formation energy of a potassium dopant is significantly lower than for a Ni vacancy, making potassium an attractive monovalent dopant for NiO. We compare our results with DFT results that include an on-site Hubbard U (DFT+U) to account for correlations and find relatively large discrepancies for defect formation energies as well as for charge and spin redistributions in the presence of point defects. Finally, it is unlikely that single-parameter fixes of DFT may be able to obtain accurate accounts of anything but a single parameter, e.g., band gap; responses that, maybe in addition to the band gap, depend in subtle and complex ways on ground state properties, such as charge and spin densities, are likely to contain quantitative and qualitative errors.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Geometries of edge and mixed dislocations in bcc Fe from first principles calculations

We use density functional theory (DFT) to compute the core structures of $a_0[100](010)$ edge, $a_0[100](011)$ edge, $a_0/2[\bar{1}\bar{1}1](1\bar{1}0)$ edge, and $a_0/2[111](1\bar{1}0)$ $71^{\circ}$ mixed dislocations in body-centered cubic (bcc) Fe. The calculations are performed using flexible boundary conditions (FBC), which effectively allow the dislocations to relax as isolated defects by coupling the DFT core to an infinite harmonic lattice through the lattice Green function (LGF). We use the LGFs of the dislocated geometries in contrast to most previous FBC-based dislocation calculations that use the LGF of the bulk crystal. The dislocation LGFs account for changes in the topology of the crystal in the core as well as local strain throughout the crystal lattice. A simple bulk-like approximation for the force constants in a dislocated geometry leads to dislocation LGFs that optimize the core structures of the $a_0[100](010)$ edge, $a_0[100](011)$ edge, and $a_0/2[111](1\bar{1}0)$ $71^{\circ}$ mixed dislocations. This approximation fails for the $a_0/2[\bar{1}\bar{1}1](1\bar{1}0)$ dislocation however, so in this case we derive the LGF from more accurate force constants computed using a Gaussian approximation potential. The standard deviations of the dislocation Nye tensor distributions quantify the widths of the dislocation cores. The relaxed cores are compact, and the local magnetic moments on the Fe atoms closely follow the volumetric strain distributions in the cores. We also compute the core structures of these dislocations using eight different classical interatomic potentials, and quantify symmetry differences between the cores using the Fourier coefficients of their Nye tensor distributions. Most of the core structures computed using the classical potentials agree well with the DFT results. Furthermore the DFT core geometries provide benchmarking for classical potential studies of work-hardening, as well as substitutional and interstitial sites for computing solute-dislocation interactions that serve as inputs for mesoscale models of solute strengthening and solute diffusion near dislocations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Understanding Twinning and Deformation in High Entropy Alloys

A combination of high strength and high ductility has been observed in multi-principal element alloys due to twin formation attributed to low stacking fault energy (SFE). In the pursuit of low SFE alloys, a key bottleneck is the lack of understanding of the composition–SFE cor- relations that would guide tailoring SFE via alloy composition. Using density functional theory (DFT), we show that dopant radius, which have been postulated as a key descriptor for SFE in dilute alloys, does not fully explain SFE trends across different host metals. Instead, charge density is a much more central descriptor. It allows us to (1) explain contrasting SFE trends in Ni and Cu host metals due to various dopants in dilute concentrations, (2) explain the large SFE variations observed in the literature even within a given alloy composition due to the nearest neighbor environments in “model” concentrated alloys, and (3) develop a machine learning model that can be used to predict SFEs in multi-elemental alloys. This model opens a possibility to use charge density as a descriptor for predicting SFE in alloys. Furthermore, a descriptor-less machine learning (ML) model based only on charge density images extracted from density functional theory (DFT) is developed to predict stacking fault energies (SFE) in concentrated alloys. The model is based on convolutional neural networks (CNNs) as one of the promising ML techniques for dealing with complex images and data. Identification of correct descriptors is a key bottleneck to develop ML models for predicting materials properties. Often, in most ML models, textbook physical descriptors such as atomic radius, valence charge and electronegativity are used as descriptors which have limitations because these properties change in concentrated alloys when multiple elements are mixed to form a solid solution. We illustrate that, within the scope of DFT, the search for descriptors can be circumvented by electronic charge density, which is the backbone of the Kohn-Sham DFT and describes the system completely. The performance of our model is demonstrated by predicting SFE of concentrated alloys with an RMSE and R2 of 6.18 mJ/m2 and 0.87, respectively, validating the accuracy of the proposed approach.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Synergistic Density Functional Theory and Molecular Dynamics Approach to Elucidate PNIPAM–Water Interaction Mechanisms

This study employs Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations to investigate interactions between water molecules and Poly(N-isopropylacrylamide) (PNIPAM). DFT reveals preferential water binding sites, with enhanced binding energy observed in the linker zone. Quantum Theory of Atoms in Molecules (QTAIM) and electron localization function (ELF) analyses highlight the roles of hydrogen bonding and steric hindrance. MD simulations unveil temperature-dependent hydration dynamics, with structural transitions marked by changes in the radius of gyration (Rg) and the radial distribution function (RDF), aligning with DFT findings. Our work goes beyond prior studies by combining a DFT, QTAIM and MD simulations approach across different PNIPAM monomer-to-30mer structures. It introduces a systematic quantification of pseudo-saturation thresholds and explores water clustering dynamics with structural specificity, which have not been previously reported in the literature. These novel insights establish a more complete molecular-level picture of PNIPAM hydration behavior and temperature responsiveness, emphasizing the importance of amide hydrogen and carbonyl oxygen sites in hydrogen bonding, which weakens above the lower critical solution temperature (LCST), resulting in increased hydrophobicity and paving the way for understanding water sorption mechanisms, offering guidance for future applications such as dehumidification and atmospheric water harvesting.

08 HYDROGEN↗
DOE OSTI2025

Prediction of Solute Segregation at Metal/Oxide Interfaces Using Machine Learning Approaches

The atomic structure and chemistry at metal/oxide interfaces play a crucial role in determining their properties. However, studying semi-coherent metal/oxide interfaces that include misfit dislocations through density functional theory (DFT) is often computationally expensive due to the large number of atoms involved, ranging from hundreds to thousands. In this study, we explore solute segregation behavior at the Fe/Y 2 O 3 interface—an important model interface for cladding applications in nuclear fission reactors—by combining DFT calculations with a machine learning (ML) approach. ML models are trained using DFT-calculated segregation energies (𝐸 𝑆𝑒𝑔 ) to identify the key chemical and geometric factors influencing solute segregation at metal/oxide interfaces, revealing the competition between these features in determining 𝐸 𝑆𝑒𝑔 . Moreover, the segregation behavior at a specific Fe/Y 2 O 3 interface is predicted with high accuracy using ML models trained on data from this interface. Furthermore, it is found that the ML models could also predict solute segregation at a different Fe/Y 2 O 3 interface with a new orientation relationship (OR), at a computational cost of less than 1/45 of that required for similar DFT calculations.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (similar to 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. In this work, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (similar to 1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with similar to 500 total reference calculations. Using an extensive DFT test set of similar to 1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/angstrom of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Models for Single–Site Heterogeneous Catalysts on Carbon: MoO 2 Epoxidation Catalyst Anchored to a Fullerene

Single-site molybdenum dioxo catalysts, fullerenol/MoO 2 , are prepared via grafting precursor (DME)MoO 2 Cl 2 onto a highly polyhydroxylated fullerene (ful) and an isomerically-pure and well-defined fullerene (ful*). These catalyst structures are characterized by ICP-OES, XPS, XANES, EXAFS, DRIFT, Raman, and NMR spectroscopy, and DFT. Mo 3d 5/2 XPS and Mo K-edge XANES assign the oxidation state as Mo(VI). Mo EXAFS data fitting reveals two Mo=O double and two Mo–O single bonds at distances of 1.7 and 1.9 Å, respectively, while an Mo=O stretchingl mode is observed at ~950 cm –1 by DRIFT and Raman spectroscopy. These data align well with DFT computational results, supporting the proposed catalyst structure as Fullerene(-μ-O-) 2 M(=O) 2 . Additionally, DFT provides insight into the energetically favorable grafting sites for an isomerically pure fullerenol. The scope of fullerenol/MoO 2 mediated alkene epoxidation includes abiotic alkenes, natural occurring terpenes, and conjugated olefins. For cyclooctene the rate law is first-order in [Mo], near first order in [olefin] and zero-order in [t-butyl hydroperoxide]. A plausible reaction mechanism involves peroxide addition first and then cyclooctene addition directly across the peroxo bond forming the epoxide product, consistent with DFT computation. Overall, fullerenol/MoO 2 shows promise as a sustainable and structurally well-defined system with versatile catalytic activity and good epoxidation recyclability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

On the Sensitivity to Density-Functional Approximations for CO Binding Energies of Single-Atom Catalysts in Nitrogen-Doped Graphene

Density functional theory (DFT) methods are the working horse in screening new catalytic materials. They are widely used to predict trends in binding energies, which are then used to compare the activity of different materials. Here, the binding strength of CO is an important descriptor to the CO 2 reduction catalytic activity of the single transition metal atoms embedded on nitrogen-doped graphene (TM/NG). In this work, however, we show that CO binding strengths in different TM/NG has very different sensitivity to DFT methods. Specifically, Fe/NG CO binding energy changes dramatically with the percentage of exact exchange in the functional; Co/NG does less so, while Ni/NG nearly has no change. Such varying behaviors is a direct result of different local spin configurations, similar to the performance of DFT methods for metal porphyrin complexes. Therefore, caution should be exercised when using DFT binding energies for quantitative predictions in TM/NG single atom catalysis.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Integrating density functional theory into reductive dechlorination research

Abstract Chlorinated organics have been frequently detected in groundwaters, threatening the quality of drinking water supplies worldwide. A promising method for groundwater remediation involves reductive dechlorination (RD), in which chlorine atoms are sequentially removed and substituted by hydrogen, producing less harmful byproducts. In this paper, for the first time, RD research is reviewed in light of the growing incorporation of density functional theory (DFT) as a research tool. DFT has been used to uncover a variety of reaction properties for a range of relevant groundwater pollutants, including 1,2,3‐trichloropropane, hexachlorobenzene, and various dioxins. DFT models have revealed the role of surface interactions in driving the kinetics of catalytically driven RD. Mechanisms involved with biologically mediated RD have also been elucidated with insights gleaned from DFT. Issues and challenges for future research are also discussed.

Das Schober, Jaya↗
DOE OSTI2017

First-principles study of crystal structure and stability of T 1 precipitates in Al-Li-Cu alloys

Aluminum-lithium-copper alloys have a low density, high elastic modulus and high specific strength.Due to this combination of properties, alloys strengthened with the ternary (Al-Li-Cu) T 1 phase have attracted a great deal of interest especially in aerospace applications. Determining the atomic structuralinformation of the precipitate is a fundamental step in developing a basis for advanced alloy design; however, even though many experimental studies have addressed the T 1 crystal structure, it remains the subject of some controversy. Here, we use density functional theory (DFT) calculations to investigate the structure and composition of the T 1 phase by comparing the energetic stability of five previously-proposed models of the crystal structure of T 1 . The DFT formation energy of these proposed T 1 crystal structures was calculated using a special quasi-random structure (SQS) approach to describe a disordered Al-Cu sub-lattice. In conflict with the experimental phase diagram, DFT calculations of all five proposed models result in an energetically unstable T 1 phase. Here, we search for a new, lower-energy structure of T 1 using a cluster expansion approach, and find a new structural model with DFT energy that is stable (at T = 0 K), i.e., on the calculated convex hull of the Al-Li-Cu ternary system. However, this new predicted phase does not have a tie-line with Al, but the formation energy of the phase is very close to the energy required to make a tie-line with Al (Δ E = 0.013 eV/atom), which could be affected by finite temperature entropic effects (i.e., vibrational entropic stabilization).

36 MATERIALS SCIENCE↗
DOE OSTI2019

The partial reduction of clean and doped alpha-Fe2O3(0001) from first principles

Fe surfaces have a wide range of applications due to the ability of Fe to be present in a number of different oxidation states, which can be further influenced by the presence of dopants that change the energetic and electronic surface properties. Thus, having an understanding of the elementary reduction processes of clean Fe oxide surfaces, and how dopants affect the energetics and electronics of such processes, is crucial to the science-guided design and optimization of said surfaces. In this work, we use density functional theory (DFT) to investigate the partial reduction of a-Fe2O3(0001) in the absence and presence of Pd and Pt dopants. In order to account for the strong on-site Coulombic interactions present in transition metal oxides, we used both the DFT?+?U method and 2006 Heyd-Scuseria-Ernzerhof functional and compare the results obtained from the different methods. We find that oxygen vacancies created in the clean a-Fe2O3(0001) surface prefer to diffuse into a subsurface layer, with the charge left behind from the first surface oxygen vacancy distributing between all of the near surface Fe atoms. The addition of Pd or Pt to the a-Fe2O3(0001) surface significantly decreases the energy required to create the first surface oxygen vacancy, suggesting the dopants intrinsically promote the reduction of a-Fe2O3(0001). Additionally, the presence of the dopants caused the charge left behind by the creation of the first surface oxygen vacancy to be localized on the dopant itself as well as the highest Fe atom. Overall, this work provides insight into the elementary energetic and electronic properties of the fully oxidized and partially reduced a-Fe2O3(0001) surface in the absence and presence of Pd and Pt dopants, which has potential implications for the design and optimization of doped Fe oxide surfaces.

Hensley, Alyssa↗
DOE OSTI2022

Accelerated computation of lattice thermal conductivity using neural network interatomic potentials

We report with the development of the density functional theory (DFT) and ever-increasing computational capacity, an accurate prediction of lattice thermal conductivity based on the Boltzmann transport theory becomes computationally feasible, contributing to a fundamental understanding of thermal conductivity as well as a choice of the optimal materials for specific applications. However, steep costs in evaluating third-order force constants limit the theoretical investigation to crystals with high symmetry and few atoms in the unit cell. Currently, machine learning potentials are garnering attention as a computationally efficient high-fidelity model of DFT, and several studies have demonstrated that the lattice thermal conductivity could be computed accurately via the machine learning potentials. However, test materials were mostly crystals with high symmetries, and the applicability of machine learning potentials to a wide range of materials has yet to be demonstrated. Furthermore, establishing a standard training set that provides consistent accuracy and computational efficiencies across a wide range of materials would be useful. To address these issues, herein we compute lattice thermal conductivities at 300 K using neural network interatomic potentials. As test materials, we select 25 materials with diverse symmetries and a wide range of lattice thermal conductivities between 10 -1 and 10 3 Wm -1 K -1 . Among various choices of training sets, we find that molecular dynamics trajectories at 50–700 K consistently provide results at par with DFT for the test materials. In contrast to pure DFT approaches, the computational cost in the present approach is uniform over the test materials, yielding a speed gain of 2–10 folds. When a smaller reduced training set is used, the relative efficiency increases by up to ~50 folds without sacrificing accuracy significantly. The current work will broaden the application scope of machine learning potentials by establishing a robust framework for accurately computing lattice thermal conductivity with machine learning potentials.

36 MATERIALS SCIENCE↗