arXiv subjects

Yuji Ikeda

Publications and source records attributed to Yuji Ikeda.

At least 19 recordsLinked to original sources

arXiv2026

Impact of interstitial carbon on local lattice distortions in CoCrFeMnNi high-entropy alloys

Here, we explore component-dependent local lattice distortions in polycrystalline, equiatomic, face-centered cubic CrMnFeCoNi high-entropy alloys and their modifications induced by dilute interstitial carbon. Multi-edge extended X-ray absorption fine structure spectroscopy combined with reverse Monte Carlo analysis reveals that the Cr component experiences the most substantial local distortions, independent of the temperature of prolonged annealing treatments (993 K or 1373 K) and the nominal carbon content (0 to 0.8 at.%). The static disorder around Cr atoms was found to increase markedly and monotonically upon carbon alloying, whereas Mn, Fe, Co, and Ni demonstrate weaker and non-monotonic tendencies. The carbon-induced lattice distortions extend over several coordination shells, indicating the pronounced effect of the carbon presence on the local environment around Cr absorbers. First-principles density functional theory and finite-temperature molecular dynamics simulations confirm the greater impact of carbon on the local lattice distortions around Cr than around the other $3d$ constituent elements, based on the previous finding that carbon preferentially occupies Cr-rich interstitial sites. These results provide decisive hints towards the atomistic origin of the non-monotonic diffusion behavior previously reported for carbon-doped CrMnFeCoNi alloys, and are noticeable for understanding the carbon-induced phase transitions in compositionally complex systems.

cond-mat.mtrl-sci↗

arXiv2026

An Ontology for Machine Learning Interatomic Potentials

Machine learning interatomic potentials (MLIPs) approximate quantum-mechanical energies and forces---conventionally computed by density functional theory (DFT) or wave-function methods---at a fraction of the cost. The field encompasses a growing ecosystem of algorithms, training datasets, hyperparameters, and target materials, yet the metadata needed to systematically compare, reproduce, and build upon MLIP studies remains scattered across papers, scripts, and ad-hoc file formats. We present the MLIPs ontology, an OWL 2 DL ontology that captures the concepts needed to describe MLIP methods, their hyperparameters, training datasets with DFT provenance, and published benchmarks. The ontology is organized into three modules---Method, Training Data, and Benchmark---and connects existing ontologies in materials science (MDO, CMSO/ASMO) and machine learning (ML-Schema), complementing dataset-side schemas such as Croissant. It declares 27 formal axioms enforcing data completeness and consistency, including property chains that link trained models to their methods and training data. We demonstrate the ontology through a running example based on Moment Tensor Potentials and evaluate it through competency-question execution on a 20-paper seeded knowledge graph, OWL reasoning, and comparison with existing ontologies.

cs.AI↗

arXiv2026

Machine learning via artificial neural networks coupled with density functional theory and experiments for thermodynamic optimization of high-entropy alloys for hydrogen storage at room temperature

High-entropy alloys (HEAs) have received considerable attention for hydrogen storage because of their compositional flexibility; however, designing HEAs with optimal thermodynamics is critical. This study employs machine learning via artificial neural networks (ANN) and density functional theory (DFT) to design a novel AB-type TixNb2-xVCrMnFe (x = 0.5-2.0) high-entropy system for hydrogen storage at ambient temperature (A: Ti, V and Nb, and B: Cr, Mn and Fe). Both ANN and DFT predict that the hydride formation enthalpy decreases to negative values with increasing the titanium content. Two alloys with x > 1.5 are predicted to achieve enthalpies within the -25 to -39 kJ/mol range, making them appropriate for room-temperature hydrogen storage. Experiments demonstrate good agreement with the enthalpy predictions, with the Ti-rich alloys showing reversible hydrogen storage with fast kinetics at room temperature. These results provide a framework for reliable use of data analysis and ab initio calculations to explore high-entropy hydrides as hydrogen storage materials.

cond-mat.mtrl-sci↗

arXiv2026

Assessing the Impact of Source Confusion for GREX-PLUS based on Deep JWST NIRCam Imaging

We investigate the effects of source confusion expected in observations with GREX-PLUS, a JAXA L-class space infrared telescope mission candidate with a wide-field infrared camera covering 2-8 um with a field of view of 0.50 deg$^2$. For the deep imaging band near 4 um, we calculate the GREX-PLUS PSF and ghost based on the latest optical design, and consider two representative imaging performance cases with PSF FWHM values of 0.9 and 1.2 arcsec. We construct simulated GREX-PLUS images at different depths by convolving JWST NIRCam imaging data from JADES, GLASS, CEERS, and COSMOS-Web with the PSF+ghost kernel. Comparing the limiting magnitudes estimated from random aperture photometry using the same aperture sizes, we find that the simulated GREX-PLUS images are shallower than the original JWST images, with larger deviations for deeper original JWST images. This likely reflects unresolved faint sources and extended PSF+ghost wings from bright sources, which elevate background fluctuations in blank regions. Nevertheless, the limiting magnitudes continue to improve with increasing integration time down to ~27 mag, without a clear plateau at depths comparable to the planned GREX-PLUS deep survey, although the improvement becomes progressively less efficient toward longer integrations. Based on Monte Carlo simulations, we estimate detection completeness and correct the number counts for magnitude bias and incompleteness, finding that confusion-induced blending can reduce the completeness even at magnitudes well above the nominal 5-sigma depth. The completeness-corrected number counts agree well with the JWST-based number counts down to around the detection limit. Overall, our results suggest that statistical studies of faint galaxies remain feasible for GREX-PLUS; however, survey planning should account for less efficient depth improvement toward longer integrations due to source confusion.

astro-ph.GA↗

arXiv2026

High-Precision Near-Infrared Abundances of Solar Analogs in the YJ Bands

We present a near-infrared abundance analysis of 46 solar analogs with known ages, observed with the WINERED WIDE-mode spectrograph at a resolution of $\lambda/\Delta\lambda = 28,000$. Using an empirically calibrated, line-by-line approach in the $YJ$ bands (0.976--1.089 and 1.182--1.319~{$\mu$}m), we derive abundances for 16 elements. Despite the intrinsic weakness of near-infrared phosphorus diagnostics, the combination of five P\,{\sc i} lines yields a typical uncertainty half-width of $\sim$0.04~dex, providing an estimate of the internal precision over the solar-analog sample. For other elements, the internal precision ranges from $\sim$0.01~dex for Fe and Si to over 0.05--0.14~dex for elements with only a couple of lines available. The resulting per-object abundances for various elements are consistent with previous measurements using high-precision optical spectra with residuals of 0.03--0.2~dex depending on the element. The inferred age--[X/Fe] relations reproduce known trends for the thin disk, while extending them to elements difficult to access in the optical, including P and K. We find the slope of the age--[P/Fe] relation is steeper than that for $\alpha$ elements, which provides an empirical constraint for future modeling of Galactic phosphorus evolution. In addition, we publish a high signal-to-noise (S/N 500--1000) reference spectrum constructed by combining solar-analog spectra, together with the spectra of individual stars, and an empirically calibrated line list with per-line zero-point corrections, for future near-infrared spectroscopic studies.

astro-ph.SR↗

arXiv2026

Hydrogen uptake and hydride formation in Al$_x$CoCrFeNi high-entropy alloys: First-principles, universal-potential, and experimental study

Hydrogen uptake in complex multicomponent alloys, including high-entropy alloys (HEAs), governs both hydrogen storage capacity and resistance to hydrogen-induced degradation. We combine high-pressure experiments, density-functional theory (DFT), and a GRACE universal interatomic potential to investigate hydrogen absorption in Al$_{0.3}$CoCrFeNi and Al$_3$CoCrFeNi HEAs. In H$_2$ as a pressure-transmitting medium, the FCC Al$_{0.3}$CoCrFeNi alloy forms hydrides at ambient temperature above 3 GPa, whereas the Al-rich B2 Al$_3$CoCrFeNi alloy shows no evidence of hydride formation even upon heating at pressures up to 50 GPa. Experiments and calculations show that aluminum suppresses hydrogen uptake by increasing solution energies and destabilizing interstitial sites. The universal potential, employed in the calculations and pretrained on large DFT databases, closely reproduces DFT energetics and demonstrates transferability from the dilute limit to the hydride-forming regime. Simulations further disentangle the roles of local ordering, volume changes, composition, and crystal structure. Overall, our results indicate that hydrogen solubility in Al-containing HEAs is governed primarily by composition, with Al-driven B2 ordering as a strong secondary effect.

cond-mat.mtrl-sci↗

arXiv2026

Finely Tunable Thermal Expansion of NiTi by Stress-Induced Martensitic Transformation and Thermomechanical Training

Tailoring the thermal expansion of martensitic materials by crystallographic texture and anisotropic variation of lattice parameters is a promising route to a flexible design of thermally stable systems. NiTi alloys are prototype materials in this respect, with shape-memory and superelastic properties owing to their thermoelastic martensitic transformations. Here, we propose a method to realize finely tunable coefficients of thermal expansion (CTE) for the NiTi alloy based upon a special combination of mechanical and thermal training. We achieve a near-zero in-plane CTE that is smaller in value than that of the FeNi-based Invar alloy. Atomistic simulations and theoretical calculations guide the method design and clarify the underlying mechanisms of the relationship between the processing conditions, the microstructural evolution, and the thermal expansion behavior. The directions for further, finer adjustments of the CTE without constraints on the shape of the materials are indicated.

cond-mat.mtrl-sci↗

arXiv2026

Microstructural origin of the simultaneous enhancements in strength and ductility of a nitrogen-doped high-entropy alloy

As one of the most abundant interstitial elements, nitrogen (N) is effective in improving yield strength of metallic materials, due to interstitial solid solution strengthening. Doping N can substantially enhance the yield strength but often leads to a decreased ductility, revealing a strength-ductility trade-off phenomenon. Here, we simultaneously enhance the strength and ductility in a non-equiatomic CrMnFeCoNi high-entropy alloy via N alloying and unravel the underlying microscopic mechanisms. The N-doped alloy (1 at.% N) shows an excellent combination of higher yield strength (104% increase) and larger ductility (38% increase), with a two-stage strain hardening behavior, compared to the N-free alloy. Detailed transmission electron microscopy (TEM) analysis reveals that N-doping introduces short-range order (SRO) domains within the microstructure, leads to pronounced planar slip, and promotes the formation of nano-spaced (6-15 nm) stacking faults and deformation twins. Continuous generation and interaction of the fine-spaced SFs act as a strong barrier for dislocation movement and provide ample room for dislocation storage. The interaction of SRO with dislocations and the evolution of SFs ascribe to the first strain hardening stage, and the disordering of the SRO along with the activation of deformation twins are attributed to the second strain hardening stage. Our work shows that N-doping is effective in simultaneously improving the strength-ductility synergy and provides novel insights into alloy design with slightly elevating the SFE, and manipulating the ordered structure within the HEA.

cond-mat.mtrl-sci↗

arXiv2025

Hydrogen diffusion in TiCr$_2$H$_x$ Laves phases: A combined ab initio and machine-learning-potential study

The kinetics of hydrogen diffusion in C15 cubic and C14 hexagonal TiCr$_2$H$_x$ (0 < $x$ <= 4) Laves-phase hydrogen storage alloys is investigated with density functional theory (DFT) and machine learning interatomic potentials (MLIPs). Generalized solid-state nudged elastic band calculations are conducted based on DFT for all symmetrically inequivalent paths between the first-nearest-neighbor face-sharing interstitial sites. The hydrogen migration barriers are substantially higher for the paths that require breaking a Ti-H bond than for those that require breaking a Cr-H bond. Molecular dynamics (MD) simulations with the MLIPs also demonstrate that hydrogen migration occurs more frequently within the hexagonal rings made of the A$_2$B$_2$ interstitial paths, each requiring the breaking of Cr-H bonds, than along the inter-ring paths. The diffusion coefficients of hydrogen obtained from the MD simulations reveal a non-monotonic dependence on hydrogen concentration, which is more pronounced at lower temperatures. Time-averaged radial distribution functions of hydrogen further show that hydrogen avoids face-sharing positions during diffusion and that the hydrogen occupancy at the second-nearest-neighbor edge-sharing positions increases with increasing hydrogen concentration. The diffusion coefficients of hydrogen within 400-1000 K follow an Arrhenius relationship, with activation barriers consistent with most experimental values. One-order of magnitude overestimation of diffusion coefficients compared with some experiments suggests a substantial impact of hydrogen trapping by defects such as Cr vacancies and Ti anti-sites in non-stoichiometric TiCr$_2$ in experiments.

cond-mat.mtrl-sci↗

arXiv2025

Microstructural Insights into Fast Ion Transport in Solid Electrolytes via Multiscale Modeling

Improving solid electrolytes is critical for high-performance all-solid-state batteries, yet the microstructural features that enable fast ion transport remain poorly understood. Here, we use multiscale modeling to resolve polycrystalline ion transport from atomic-scale hopping at grain boundaries to continuum-scale percolation, thereby providing insights into realistic solid-electrolyte microstructures. Accurate lightweight machine-learning potentials -- developed via closed-loop active learning for exemplar argyrodites Li$_6$PS$_5$X, X $\in$ {Cl, Br, I} -- are employed to integrate molecular dynamics with finite element simulations. We find that diffusion barriers of the anion-ordered bulk scale linearly with anion radius. Grain boundaries exert opposite effects depending on the bulk: enhancing ion diffusion in low-diffusivity phases but suppressing it in fast-diffusing ones. Li$_6$PS$_5$I exhibits non-Arrhenius transport behavior consistent with experimental observations. Our results clarify the pivotal role of grain boundaries in ion transport and guide a priori microstructural design of advanced solid electrolytes.

cond-mat.mtrl-sci↗

arXiv2025

Ultrastrong and ductile CoNiMoAl medium-entropy alloys enabled by L12 nanoprecipitate-induced multiple deformation mechanisms

L12 precipitates are known to significantly enhance the strength and ductility of single-phase face-centered cubic (FCC) medium- or high-entropy alloys (M/HEAs). However, further improvements in mechanical properties remain untapped, as alloy design has historically focused on systems with specific CrCoNi- or FeCoCrNi-based FCC matrix and Ni3Al L12 phase compositions. This study introduces novel Co-Ni-Mo-Al alloys with L12 precipitates by systematically altering Al content, aiming to bridge this research gap by revealing the strengthening mechanisms. The (CoNi)81Mo12Al7 alloy achieves yield strength of 1086 MPa, tensile strength of 1520 MPa, and ductility of 35 %, demonstrating an impressive synergy of strength, ductility, and strain-hardening capacity. Dislocation analysis via transmission electron microscopy, supported by generalized stacking fault energy (GSFE) calculations using density functional theory (DFT), demonstrates that Mo substitution for Al in the L12 phase alters dislocation behavior, promoting the formation of multiple deformation modes, including stacking faults, super-dislocation pairs, Lomer-Cottrell locks, and unusual nano-twin formation even at low strains. These behaviors are facilitated by the low stacking fault energy (SFE) of the FCC matrix, overlapping of SFs, and dislocation dissociation across anti-phase boundaries (APBs). The increased energy barrier for superlattice intrinsic stacking fault (SISF) formation compared to APBs, due to Mo substitution, further influences dislocation activity. This work demonstrates a novel strategy for designing high-performance M/HEAs by expanding the range of FCC matrix and L12 compositions through precipitation hardening.

cond-mat.mtrl-sci↗

arXiv2025

Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions

Machine-learning interatomic potentials (MLIPs) enable large-scale atomistic simulations at moderate computational cost while retaining ab initio accuracy. MLIPs trained on coupled-cluster data, particularly CCSD(T), have emerged as a promising route to achieve chemical accuracy beyond the limits of density functional theory (DFT) and to incorporate non-empirical van der Waals (vdW) interactions. Most existing approaches are, however, still not straightforwardly applicable for systems with extended covalent networks such as covalent organic frameworks (COFs) due to the limited availability of CCSD(T) for periodic systems. Here we present a methodology to train MLIPs with CCSD(T) accuracy for these systems. The approach uses the {\Delta}-learning method with a dispersion-corrected tight-binding baseline. This strategy enables training on compact molecular fragments while preserving transferability toward the periodic systems. Dispersion interactions are accounted for by adding vdW-bound multimers in the training set, and the combination with a vdW-aware tight-binding baseline allows the formally local MLIP to attain CCSD(T)-level accuracy even for systems dominated by long-range vdW forces. The resulting potential yields root-mean-square energy errors below 0.4 meV/atom on training and test sets and reproduces electronic total atomization energies, bond lengths, harmonic vibrational frequencies, and inter-molecular interaction energies for benchmark molecular systems. We apply the method to a prototypical quasi-two-dimensional COF composed of carbon and hydrogen. The COF structure, inter-layer binding energies, and hydrogen absorption are analyzed at CCSD(T) accuracy. The developed methodology opens a practical route to large-scale atomistic simulations for systems with extended covalent networks and vdW interactions with chemical accuracy.

cond-mat.mtrl-sci↗

arXiv2025

Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs

We propose a free-energy-perturbation approach accelerated by machine-learning potentials to efficiently compute transition temperatures and entropies for all rungs of Jacob's ladder. We apply the approach to the dynamically stabilized phases of SiO$_2$, which are characterized by challengingly small transition entropies. All investigated functionals from rungs 1-4 fail to predict an accurate transition temperature by 25-200%. Only by ascending to the fifth rung, within the random phase approximation, an accurate prediction is possible, giving a relative error of 5%. We provide a clear-cut procedure and relevant data to the community for, e.g., developing and evaluating new functionals.

cond-mat.mtrl-sci↗

arXiv2025

Machine Learning Potentials for Hydrogen Absorption in TiCr$_2$ Laves Phases

The energetics of hydrogen absorption in C15 cubic and C14 hexagonal TiCr$_2$H$_x$ Laves phases is investigated for $0 < x \le 6$ with density functional theory (DFT) and machine learning interatomic potentials (MLIPs). The MLIPs are trained with configurations generated through a series of active-learning schemes. Basin-hopping Monte Carlo (BHMC) simulations based on the MLIPs predict minimum-energy hydrogen configurations, along with enthalpies of formation and hydrogen orderings. The obtained phase transformations at 0 K agree well with the experiments at low temperatures. The hydrogen solubility limits in the low-concentration $\alpha$ phases at 0 K are predicted to be $x = 1.0$ and $x = 1.5$ for the C15 and the C14 phases, respectively. At these concentrations, C15 TiCr$_2$H shows the $Cc$ monoclinic symmetry, while C14 TiCr$_2$H$_{1.5}$ shows the $Ama2$ orthorhombic symmetry, both of which have not been reported for this system. The first and the second hydride phases, i.e., $\beta$ and $\beta'$, at 0 K are found around $x = 3$ and $x = 4$, respectively, for both the C15 and the C14 phases. In the second-hydride $\beta'$ phases, C15 TiCr$_2$H$_4$ shows the $I4_1/a$ tetragonal symmetry, while C14 TiCr$_2$H$_4$ shows the $R\bar3c$ rhombohedral symmetry. Hydrogen repulsion are found to extend to edge-sharing interstices, affecting the hydrogen ordering. Furthermore, the $6h_2$ A$_2$B$_2$ interstices are found to be energetically substantially more preferable for C14 TiCr$_2$H$_x$ than the other A$_2$B$_2$ interstices at low hydrogen concentrations, influencing the hydrogen-occupation trend.

cond-mat.mtrl-sci↗

arXiv2025

MAGIS (Measuring Abundances of red super Giants with Infrared Spectroscopy) project I. Establishment of an abundance analysis procedure for red supergiants and its evaluation with nearby stars

[Abbreviated] Context. Given their high luminosities (L>~10^4Lsun), red supergiants (RSGs) are good tracers of the chemical abundances of the young stellar population in the Milky Way and nearby galaxies. However, previous abundance analyses tailored to RSGs suffer some systematic uncertainties originating in, most notably, the synthesized molecular spectral lines for RSGs. Aims. We establish a new abundance analysis procedure for RSGs that circumvents difficulties faced in previous works, and test the procedure with ten nearby RSGs observed with the near-infrared high-resolution spectrograph WINERED (0.97--1.32 micron, R=28,000). Results. We determined the [X/Fe] of ten elements (Na I, Mg I, Al I, Si I, K I, Ca I, Ti I, Cr I, Ni I, and Y II). We estimated the relative precision in the derived abundances to be 0.04--0.12 dex for elements with more than two lines analyzed (e.g., Fe I and Mg I) and up to 0.18 dex for the other elements (e.g., Y II). We compared the resultant abundances of RSGs with the well-established abundances of another type of young star, namely the Cepheids, in order to evaluate the potential systematic bias in our abundance measurements, assuming that the young stars (i.e., both RSGs and Cepheids) in the solar neighborhood have common chemical abundances. We find that the determined RSG abundances are highly consistent with those of Cepheids within <~0.1 dex for some elements (notably [Fe/H] and [Mg/Fe]), which means the bias in the abundance determination for these elements is likely to be small. In contrast, the consistency is worse for some other elements (e.g., [Si/Fe] and [Y/Fe]). Nevertheless, the dispersion of the chemical abundances among our target RSGs is comparable with the individual statistical errors on the abundances. Hence, the procedure is likely to be useful to evaluate the relative difference in chemical abundances among RSGs.

astro-ph.GA↗

arXiv2024

A search for water vapor plumes on Europa by spatially resolved spectroscopic observation using Subaru/IRCS

We present near-infrared high-dispersion spectroscopic observations of Europa using the Infrared Camera and Spectrograph (IRCS) onboard the Subaru Telescope, seeking direct evidence of water plumes on Europa and exploring spatial variations in plume activity. Using high spectral/spatial resolution and sensitivity of Subaru/IRCS, our observations have enabled a spatially resolved search for water plumes on Europa. Within our detection limits and time of observation, we found no evidence for the presence of water emission. For a rotation temperature of 50 K, we derived an upper limit on the H$_{2}$O abundance of 9.46$\times$10$^{19}$ - 5.92$\times$10$^{20}$ m$^{-2}$ in each divided slit area and 4.61$\times$10$^{19}$ m$^{-2}$ in the entire area covered by the slit. This upper limit lies below the inferred water abundance from previous UV observations by the Hubble Space Telescope (HST), while being less sensitive by a factor of three compared to the Keck telescope and by one order of magnitude or more than the James Webb Space Telescope (JWST) observations. Our results align with previous studies and demonstrate that using Subaru/IRCS is an effective strategy for searching for water plumes on Europa with high spatial resolution. Continued observations across different surface areas and orbital phases are essential to fully characterize Europa's plume activity and complement upcoming space missions.

astro-ph.EP↗

arXiv2024

Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li$_6$PS$_5$Cl using machine-learning interatomic potentials

Li$_6$PS$_5$Cl is a promising candidate for the solid electrolyte in all-solid-state Li-ion batteries. In applications, this material is in a polycrystalline state with grain boundaries (GBs) that can affect ionic conductivity. While atomistic modeling provides valuable information on the impact of GBs on Li diffusion, such studies face either high computational cost (\textit{ab initio} methods) or accuracy limitations (classical potentials) as challenges. Here, we develop a quality-level-based active learning scheme for efficient and systematic development of \textit{ab initio}-based machine-learning interatomic potentials, specifically moment tensor potentials (MTPs), for large-scale, long-time, and high-accuracy simulations of complex atomic structures and diffusion mechanisms as encountered in solid electrolytes. Based on this scheme, we obtain MTPs for Li$_6$PS$_5$Cl and investigate two tilt GBs, $\Sigma3(1\bar{1}2)[110]$, $\Sigma3(\bar{1}11)[110]$, and one twist GB, $\Sigma5(001)[001]$. All three GBs exhibit low formation energies of less than \SI{20}{meV/\angstrom\textsuperscript{2}}, indicating their high stability in polycrystalline Li$_6$PS$_5$Cl. Using the MTPs, diffusion coefficients of the anion-ordered and anion-disordered bulk, as well as the three GBs, are obtained from molecular dynamics simulations of atomistic models. At \SI{300}{\kelvin}, the GB diffusion coefficients fall between the ones of the anion-ordered bulk structure (\SI{0.012e-7}{cm^2/s}, corresponding ionic conductivity about \SI{0.2}{mS/cm}) and the anion-disordered bulk structure (\SI{50}{\percent} Cl/S-anion disorder; \SI{2.203e-7}{cm^2/s}, about \SI{29.8}{mS/cm}) of Li$_6$PS$_5$Cl. Experimental data fall between the Arrhenius-extrapolated diffusion coefficients of the investigated atomic structures.

cond-mat.mtrl-sci↗

arXiv2024

Mechanism of anatase-to-columbite TiO2 phase transformation via sheared phases: first-principles calculations and high-pressure torsion experiments

High-pressure torsion (HPT) can facilitate phase transformations in titanium dioxide (TiO2) and stabilize its high-pressure columbite phase, as an active photocatalyst, by shear straining under high pressure. This study aims to understand the mechanism underlying the acceleration of the anatase-to-columbite phase transformation by shear strain. A mechanism by considering sheared crystal structures as intermediate phases was proposed and examined using quantum mechanics in the framework of density functional theory (DFT) and HPT experiments. DFT energy and phonon calculations demonstrated the viability of the sheared structures as intermediate phases. Furthermore, the sheared structures were observed experimentally as new metastable phases using high-resolution transmission electron microscopy. These findings can explain the significant effect of shear strain on pressure-induced phase transitions, reported during severe plastic deformation of various metals and ceramics.

cond-mat.mtrl-sci↗