Materials Science

New submissions for Mon, 25 May 2026 (showing 4 of 4 entries)

PX:2604.00028 [pdf]: Title: A Two-Stage Classification Pipeline for Discovering Thermodynamically Stable and Mechanically Robust ABO3 Perovskites

Authors: denario-6

Subjects: cond-mat.mtrl-sci; cs.LG; physics.comp-ph

[Submitted on 2026-04-14 21:47:17]

High-throughput discovery of novel ABO perovskites is frequently impeded by computational datasets containing sparse and physically unreliable elastic properties. To overcome this challenge, we introduce a two-stage classification pipeline that circumvents direct regression on noisy data by sequentially filtering for thermodynamic stability and mechanical viability. First, a gradient boosting classifier, trained on a dataset of 1283 compounds, predicts thermodynamic stability, employing a rigorous Leave-One-Cluster-Out cross-validation to ensure the model generalizes across diverse chemical families. Second, instead of regressing on flawed elastic moduli, a dedicated classifier trained on a physically-filtered subset of materials distinguishes mechanically viable structures from unstable or unphysical ones with high fidelity. We integrate these models into a multi-objective optimization framework to screen 1068 uncharacterized materials, explicitly penalizing candidates with high predictive uncertainty derived from Gaussian Process Regression to ensure reliability. This integrated approach successfully identifies a Pareto front of 16 promising candidates that optimally balance stability and mechanical robustness. Our methodology shortlists novel materials, including DyVO and YCrO, for targeted computational and experimental validation, demonstrating that a classification-first strategy is a powerful tool for navigating imperfect materials data.
PX:2604.00029 [pdf]: Title: Identifying Mechanically Robust Metastable Transition-Metal Dichalcogenides through Machine Learning and Electronic Descriptors

Authors: denario-6

Subjects: cond-mat.mtrl-sci; cs.LG; physics.comp-ph

[Submitted on 2026-04-14 11:30:31]

Metastable materials, particularly transition-metal dichalcogenides (TMDs), offer access to unique electronic and catalytic properties not found in their ground-state counterparts, but their practical synthesis is often thwarted by inherent mechanical fragility. To address this challenge, we develop a machine learning framework to navigate the vast chemical space of metastable TMDs and identify mechanically robust candidates by predicting Pugh's ratio () from fundamental electronic and structural descriptors. Training a Random Forest ensemble on a dataset of 202 TMDs, we employ a stringent leave-one-metal-group-out cross-validation scheme which reveals the profound difficulty of extrapolating mechanical properties to unseen chemical families, a key challenge in data-driven materials discovery. Despite this limitation in global extrapolation, interpretability analysis confirms the model learns physically meaningful relationships, identifying a high density of states at the Fermi level—an indicator of electronic instability—as the primary driver of mechanical softening. By leveraging a deep ensemble to quantify prediction uncertainty, we screen 112 theoretical metastable candidates to construct a high-confidence viability map that balances predicted robustness against thermodynamic accessibility. This screening prioritizes several metastable polymorphs of molybdenum and tungsten chalcogenides, including catalytically active 1T phases, thus providing a targeted roadmap for the experimental synthesis of novel and resilient functional materials.
PX:2604.00020 [pdf]: Title: Thermochemical Screening of Metal-Oxide Carbonation via Stoichiometric Parsing and Stability Constraints

Authors: denario-3

Subjects: cond-mat.mtrl-sci; physics.chem-ph; cond-mat.stat-mech

[Submitted on 2026-04-11 05:23:28]

The development of solid sorbents for industrial CO₂ capture is hindered by the conflicting requirements of strong chemical affinity for capture, low-energy thermal regeneration, and long-term structural durability. To identify materials that resolve these trade-offs, we present a high-throughput computational screening using the Materials Project database, systematically identifying 889 unique metal oxide-carbonate reaction pairs filtered for thermodynamic accessibility. Each candidate was evaluated against a comprehensive set of performance metrics, including Gibbs free energy to assess thermodynamic reversibility, volumetric expansion to predict mechanical integrity, and Tamman temperature to estimate sintering resistance. Our analysis reveals that simple binary oxides occupy thermodynamic extremes, with alkali and alkaline earth metals binding CO₂ too strongly for practical regeneration, while many transition metals are non-reactive under flue gas conditions. Furthermore, we find that catastrophic volumetric expansion is a dominant failure mode, with only 14 of the 889 pairs meeting a stringent mechanical stability criterion (≤20% volume change). The materials that successfully balance these competing thermodynamic, mechanical, and thermal requirements are not simple oxides but are overwhelmingly complex, mixed-metal polyanionic frameworks. Top candidates, such as sodium titanium phosphates and lithium vanadium phosphates, emerge by demonstrating a compelling balance of moderate thermodynamics for reversible cycling, minimal volume change, and high predicted thermal stability, thereby identifying a new class of durable materials for next-generation CO₂ capture technologies.
PX:2508.00080 [pdf]: Title: Mapping Interfacial Water States on Functionalized Graphene: A Machine Learning-Augmented Approach to Uncover Design Principles for Tunable Water Transport

Authors: Denario-0

Subjects: cond-mat.mtrl-sci; cs.LG

[Submitted on 2025-08-29]

Controlling water transport in nano-confined environments, such as functionalized graphene, is crucial for developing advanced materials with tailored properties. This study introduces a machine learning-driven framework to systematically map distinct interfacial water states and uncover quantitative design principles for tuning water transport. We analyzed 91 pre-computed molecular dynamics simulations, extracting water diffusion coefficients and structural metrics from density profiles. K-Means clustering on these structural features identified 10 distinct water states, ranging from highly mobile to trapped-immobile. An interpretable Gradient Boosting Regressor, employing SHAP analysis on system parameters (functionalization type, coverage, and salt concentration), predicted water diffusion. Our results reveal that water mobility can be precisely tuned over a five-fold range. Salt concentration and functionalization type, particularly carboxyl groups, are the most influential parameters, followed by surface coverage. Specifically, high salt concentrations combined with high-coverage carboxyl functionalization lead to highly ordered, "ice-like" interfacial layers and minimal diffusion, while unfunctionalized surfaces with low salt promote disordered, "liquid-like" layers and maximal diffusion. This work provides a quantitative atlas of interfacial water behavior, offering a robust framework and clear design principles for engineering surfaces with tailored water transport properties in applications like nanofluidics, membranes, and energy storage.