Statistical phase evaluation approach for defect phase diagrams

Physical review. B./Physical review. B, Год журнала: 2025, Номер 111(5)

Опубликована: Фев. 27, 2025

We propose an analytical thermodynamic model for describing defect phase transformations, which we term the statistical evaluation approach (SPEA). The SPEA assumes a Boltzmann distribution of finite-size fractions and calculates their average. To benchmark performance model, apply it to construct binary surface diagrams metal alloys. Two alloy systems are considered: Mg with Ca substitutions Ni Nb substitutions. firm basis against can be leveled, first perform Monte Carlo (MC) simulations coupled cluster expansion density functional theory dataset. then demonstrate that reproduces MC results accurately. Specifically, correctly predicts order-disorder transitions as well coexistence 1/3 ordered disordered phase. Finally, compare method sublattice commonly used in CALPHAD describe random solution phases transitions. proposed provides highly efficient modeling transformations. Published by American Physical Society 2025

Язык: Английский

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Accurate prediction of thermoresponsive phase behavior of disordered proteins

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 6, 2025

Protein responses to environmental stress, particularly temperature fluctuations, have long been a subject of investigation, with focus on how proteins maintain homeostasis and exhibit thermoresponsive properties. While UCST-type (upper critical solution temperature) phase behavior has studied extensively can now be predicted reliably using computational models, LCST-type (lower transitions remain less explored, lack models capable accurate prediction. This gap limits our ability probe fully undergo in response changes. Here, we introduce Mpipi-T, residue-level coarse-grained model designed predict proteins. Parametrized both atomistic simulations experimental data, Mpipi-T accounts for entropically driven protein separation that occurs upon heating. Accordingly, predicts temperature-driven quantitatively single- multi-chain systems. Beyond its predictive capabilities, demonstrate provides framework uncovering the molecular mechanisms underlying heat stress responses, offering new insights into sense adapt thermal changes biological

Язык: Английский

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Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data

Polymers, Год журнала: 2025, Номер 17(5), С. 694 - 694

Опубликована: Март 5, 2025

The growing demand for high-performance and cost-effective composite materials necessitates advanced computational approaches optimizing their composition properties. This study aimed at the application of machine learning prediction optimization functional properties composites based on a thermoplastic matrix with various fillers (two types fibrous, four dispersed, two nano-dispersed fillers). experimental methods involved material production through powder metallurgy, further microstructural analysis, mechanical tribological testing. analysis revealed distinct structural modifications interfacial interactions influencing key findings indicate that optimal filler selection can significantly enhance wear resistance while maintaining adequate strength. Carbon fibers 20 wt. % improved (by 17–25 times) reducing tensile strength elongation. Basalt 10 provided an effective balance between reinforcement 11–16 times). Kaolin 2 greatly enhanced 45–57 moderate reduction. Coke maximized 9−15 acceptable Graphite ensured wear, as higher concentrations drastically decreased Sodium chloride 5 offered improvement 3–4 minimal impact Titanium dioxide 3 11–12.5 slightly Ultra-dispersed PTFE 1 optimized both work analyzed in detail effect content learning-driven prediction. Regression models demonstrated high R-squared values (0.74 density, 0.67 strength, 0.80 relative elongation, 0.79 intensity), explaining up to 80% variability Despite its efficiency, limitations include potential multicollinearity, lack consideration external factors, need validation under real-world conditions. Thus, approach reduces extensive testing, minimizing waste costs, contributing SDG 9. highlights use polymer design, offering data-driven framework rational choice fillers, thereby sustainable industrial practices.

Язык: Английский

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Artificial Intelligence and Multiscale Modeling for Sustainable Biopolymers and Bioinspired Materials

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Март 10, 2025

Biopolymers and bioinspired materials contribute to the construction of intricate hierarchical structures that exhibit advanced properties. The remarkable toughness damage tolerance such multilevel are conferred through assembly their multiscale (i.e., atomistic macroscale) components architectures. Here, functionality mechanisms biopolymers bio-inspired at multilength scales explored summarized, focusing on biopolymer nanofibril configurations, biocompatible synthetic biopolymers, composites. Their modeling methods with theoretical basis multiple lengths time reviewed for applications. Additionally, exploration artificial intelligence-powered methodologies is emphasized realize improvements in these from functionality, biodegradability, sustainability characterization, fabrication process, superior designs. Ultimately, a promising future versatile manufacturing across wider applications greater lifecycle impacts foreseen.

Язык: Английский

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A Triple Point on the Phase Diagram of a One-component System in the Van der Waals Approximation

Vestnik Moskovskogo Universiteta Seriya 3 Fizika Astronomiya, Год журнала: 2025, Номер 80(№1, 2025)

Опубликована: Янв. 1, 2025

In this work, a phase diagram of the neighborhood triple point one-component system in van der Waals approximation is constructed. It shown that makes it possible to describe corresponding coexistence three aggregate states matter – solid, liquid and gaseous. The possibility using for points other types discussed.

Язык: Английский

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Quantum Support Vector Classifier for Phase Diagram Prediction in Quinary Systems

Materials Horizons, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

This study represents a novel methodology utilizing quantum support vector classifier to predict phase diagrams in quinary systems which enhances predictive accuracy beyond classical methods.

Язык: Английский

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Ab initio phase diagrams of binary alloys in the low solute concentration limit

The Journal of Chemical Physics, Год журнала: 2025, Номер 162(18)

Опубликована: Май 8, 2025

Phase diagrams are crucial to the design of new materials, understand their phase stability and metastability under different thermodynamic conditions, such as composition, temperature, pressure. Here, we use an ab initio approach study diagram a binary alloy within low concentration limit solute. Using molecular dynamics calculations based on density functional theory, estimate solute partitioning ratios in solid–liquid equilibria. The chemical potential difference between solvent atoms both solid liquid phases is calculated using integration. As illustration techniques, have applied this method reproduce Al–Mg at zero We also compute coexistence curve pure Al by applying phase-coexistence with free energy correction technique. results close agreement experiment, demonstrating reliability models.

Язык: Английский

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Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Май 10, 2025

Abstract Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, demonstrated remarkable accuracy generalizability. However, the computational cost MLIPs limits their applicability to chemically disordered systems requiring simulation cells or sample-intensive statistical methods. Here, we report use continuous differentiable alchemical degrees freedom in materials exploiting fact that graph neural network represent discrete elements as real-valued tensors. The proposed method introduces atoms with corresponding weights into input graph, alongside modifications message-passing readout mechanisms allows smooth interpolation between compositional states materials. end-to-end differentiability enables efficient calculation gradient energy respect weights. With this modification, propose methodologies for optimizing composition solid solutions towards target macroscopic properties, characterizing order disorder multicomponent oxides, conducting free simulations quantify vacancy formation changes.

Язык: Английский

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A Triple Point on the Phase Diagram of a One-Component System in the van der Waals Approximation

Moscow University Physics Bulletin, Год журнала: 2025, Номер 80(1), С. 60 - 65

Опубликована: Фев. 1, 2025

Язык: Английский

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Theory of generalized Hertzian hyperspheres

Physical review. E, Год журнала: 2025, Номер 111(5)

Опубликована: Май 23, 2025

Язык: Английский

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