arXiv subjects

Dennis Boakye

Publications and source records attributed to Dennis Boakye.

7 recordsLinked to original sources

arXiv2026

Domain-wall energy governs coercivity in ordered and disordered additively manufactured Fe--49Co--2V

Additively manufactured soft-magnetic Fe--Co is consistently harder than wrought material of the same composition, and controlling that excess is the central obstacle to printing electrical machines from these alloys. The literature attributes it to B2 chemical ordering, untested because the anisotropy of the ordered state had not been measured for this alloy and because printed material had never been compared with wrought material at matched grain size. In this study we supply both. Calibrating on wrought Fe--49Co--2V at a known order parameter fixes the ordered-state domain-wall energy and caps every pinning channel independent of grain size. Ordering then falls orders of magnitude short of the measured excess, and a tenfold change in antiphase-domain size leaves coercivity unchanged. What ordering controls is the wall energy, and that effect is small and bounded. The excess is retained defect content, which moves process optimization from post-build annealing to in-build defect control.

cond-mat.mtrl-sci↗

arXiv2026

High-throughput thermodynamic screening of oxide-scale adhesion across the CoCrFeMnNiAl high-entropy alloys

One significant benefit of reactive element (RE) additions is the colossal improvement in oxide-scale retention during high-temperature oxidation. Selecting optimal RE dopants in high-entropy alloys remains empirical because the relevant thermodynamic landscape is inaccessible to first-principles at the required compositional resolution. Here we apply the macroscopic atom model, coupled with McLean isotherm and Guttmann models, to screen adhesion across nine CoCrFeMnNiAl sub-families at \ce{Cr2O3} and \ce{Al2O3} interfaces, ranking five REs (Hf, Y, Zr, La, Ti) for segregation, adhesion enhancement, and sulfur displacement. The screening reveals an oxide-dependent ranking inversion, with Hf dominating at \ce{Cr2O3} and La dominating at \ce{Al2O3}, driven by the interplay between RE--O and RE--matrix interaction enthalpies. Mn-containing alloys exhibit intrinsic sulfur resistance, consistent with their experimentally observed oxidation characteristics. A sulfur immunity phase diagram identifies compositions with Al~+~Mn~$\gtrsim$~25~at\% as thermodynamically immune to S-induced adhesion loss. All crossover concentrations collapse onto a universal exponential governed by the segregation enthalpy difference, providing a transferable design rule. Inverse design identifies \ce{Co16Cr16Fe16Ni16Al35} as the optimal S-immune composition with $W_\text{sep} = 5.95$~J/m$^2$ without RE doping.

cond-mat.mtrl-sci↗

arXiv2026

Implicit size dependence of the valence electron concentration criterion in high-entropy alloys

The valence electron concentration (VEC) is the most widely used predictor of FCC against BCC stability in high-entropy alloys (HEAs), yet it is a compositional average carrying no information about atomic size. Using the macroscopic atom model, we show that the mixing enthalpy is almost size-blind, shifting by less than 6\% even when constituent volumes differ by a factor of 2, so that size can act only on the electron count. That action equals exactly the covariance of the atomic surface $V^{2/3}$ with the valence electron count, divided by its mean. This covariance is not free. Volume and valence are strongly anti-correlated across the elements used to build HEAs, so the size-corrected count is an affine rescaling of VEC over 265 characterized alloys and improves no prediction. VEC already encodes atomic size, which explains its success and locates its failure among large, electron-rich elements. Chemistry sets the enthalpy through one switch element.

cond-mat.mtrl-sci↗

arXiv2026

Solidification-cell confinement of domain-wall pinning in additively manufactured ferromagnets

As-built printed ferromagnets typically exhibit higher coercivity than optimized wrought materials, yet existing explanations rely on empirical fits or costly simulations. Herein, we provide a missing analytical theory that links print parameters directly to cooling rates, cellular spacing, dislocation density, and domain-wall pinning coercivity. Informed by metallographic grain data and using a single fitted constant, our model predicts six experimental datasets for pure Fe, Fe-6.9Si, and a multicomponent alloy within a factor of 1.9. We demonstrate that configurational lattice distortion is negligible, implying that single-phase printed alloys follow dilute-pinning laws. Critically, we introduce a confinement factor, $E=\sqrt{λ_{c}/2δ_{w}}$, proving that solidification-induced dislocation packing makes cellular microstructures harder than conventionally cold-worked metals. The framework enables an alloy-sensitivity map to screen and rank compositions before manufacturing.

cond-mat.mtrl-sci↗

arXiv2026

Dominant-pair free energies predict phase selection in high-entropy alloys

Phase selection in multicomponent alloys is governed by the competition between entropic stabilization of disordered solutions and enthalpic driving forces for chemical ordering. However, widely used parametric criteria reduce it to a single scalar, carrying no explicit free energy for any competing ordered phase. Herein, we develop a thermodynamic framework based on the semi-empirical macroscopic atom model and the Dinsdale lattice stability database to fill this gap. We show that a dominant-pair mechanism, in which the Al-transition-metal interaction family dominates the ordering enthalpy, enables the complex multicomponent B2-ordering problem to be reduced to an effective pseudo-binary system with an analytically evaluated Bragg-Williams free energy. Combined with a minimum-free-energy classifier, the framework predicts the lowest-energy phase as a function of composition and temperature. This provides continuous phase stability maps rather than the single-value predictions of conventional descriptors. Demonstrated on high-entropy alloys using a dataset of 269 experimentally characterized samples, the model outperforms widely used phase-selection criteria in the class-balanced macro-F1 metric and achieves 77.9% on the well-posed three-class task, outperforming the valence electron concentration criterion. The model is general by construction and computationally efficient for predicting phase stability in multicomponent alloys over a broad range of compositions and temperatures.

cond-mat.mtrl-sci↗

arXiv2026

Rapid modeling of segregation-driven metal-oxide adhesion in high-entropy alloys using macroscopic atom model

Accurate prediction of metal-oxide adhesion in high-entropy alloys (HEAs) is challenging because interfacial segregation, atomic environments, and macroscopic thermodynamic quantities are strongly correlated. Relying solely on first-principles approaches is too expensive for exploring composition, solute concentration, and co-segregation effects. To address this, we extend the macroscopic atom model (MAM) for multicomponent alloys using composition-consistent surface fractions and an interfacial pair-probability formalism that captures deviations from random contact statistics. Applied to CoCrFeNi (AlCoCrFeNi) HEA in contact with Cr2O3 (Al2O3), the model predicts segregation energies and work of separation as continuous functions of composition, reproducing the correct segregation hierarchy of Hf, Y, Zr, and S. The stronger segregation tendency at Al2O3 interfaces, and the non-linear dependence of surface energy and adhesion on solute content and co-segregation is also captured. The results are benchmarked with DFT calculations, which shows consistent trends, particularly the strengthening of adhesion by Hf and Zr through strong metal-oxygen bonding and the weakening effect of S. These results demonstrate that the extended MAM provides a physically interpretable, computationally efficient, and quantitatively predictive framework for screening segregation-controlled adhesion beyond the limits of DFT.

cond-mat.mtrl-sci↗

arXiv2025

Machine-learned accelerated discovery of oxidation-resistant NiCoCrAl high-entropy alloys

The development of oxidation-resistant high-entropy alloy (HEA) bond coats is restricted by the limited understanding of how multi-principal element interactions govern scale formation across temperatures. This study uncovers new oxidation trends in NiCoCrAl HEAs using a data-driven analysis of high-fidelity experimental oxidation data. The results reveal a clear temperature-dependent transition between alumina- and chromia-dominated protection, identifying the compositional regimes where alloys rich in Al dominate at $\ge1150$ °C, mixed Al-Cr chemistries are optimal at intermediate temperatures, and, unexpectedly, Cr-rich low-Al alloys perform best at 850 °C-challenging the assumption that high Al is universally required. The effects of Hf and Y are shown to be strongly composition-dependent with Hf producing the largest global reduction in oxidation rate, while Y becomes effective primarily in NiCo-lean alloys. Y-Hf co-doping offers consistent improvement but exhibits site-saturation behavior. These insights identify new high-performing HEA bond-coat families, including $\mathrm{Ni_{17}Co_{23}Cr_{30}Al_{30}}$ as a substitute for conventional mutlilayer thermal barrier coatings.

cond-mat.mtrl-sci↗