arXiv subjects

Muhammad Harussani Moklis

Publications and source records attributed to Muhammad Harussani Moklis.

3 recordsLinked to original sources

arXiv2026

Genome-Guided Interpretable Screening of Phase-Stable, Lead-Free Double Perovskite Absorbers for All-Inorganic Semiconductors, Sensors, and Photovoltaics with DFT-Validated Design Rules

The discovery of stable, lead-free halide perovskites for optoelectronic applications is constrained by vast compositional space and limited interpretability of conventional screening approaches. We present a genome-guided, physics-informed framework that decodes thermodynamic stability and optoelectronic behavior through four physically interpretable descriptor families: packing, bonding, polarization, and electronic identity. Trained on 1,221 DFT-calculated A2BB'X6 compounds, machine-learning surrogates achieve robust predictive performance, with a recall-optimized stability classifier (ROC-AUC = 0.92) and an XGBoost regressor for band-gap prediction (R2 = 0.93 on held-out data). Applying a staged inverse-design constraint stack to 13,088 charge-balanced, lead-free compositions reduces the search space to five DFT-validated, phase-stable semiconductors: Rb2SnMnBr6, Cs2CdSnBr6, Cs2CdSnI6, Cs2KGaI6, and Cs2AgAlBr6. These candidates lie on the convex hull (E_hull <= 0 meV/atom), preserve ordered double-perovskite structures, and exhibit strong optical absorption (alpha peak ~1e5 cm^-1). Genotype-phenotype coupling analysis reveals a hierarchical control mechanism: packing genes define structural formability, bonding genes govern near-edge optical transitions and conductivity, and optoelectronic response genes regulate dielectric response and exciton screening (epsilon0 = 4.6-8.2). This work establishes a generalizable paradigm for interpretable inverse design, linking descriptor-level genomics to experimentally relevant optoelectronic phenotypes and providing design rules for discovering stable, lead-free double perovskites for photovoltaics, sensing, and transparent electronic applications.

cond-mat.mtrl-sci↗

arXiv2026

Backward Mapping from Device Targets to Chemical Genomes for Interpretable Discovery of Phase-Stable Lead-Free Double Perovskites with DFT-Validated Design Rules

Lead-free halide double perovskites are promising alternatives to Pb-based semiconductors, but their discovery is challenging because structural formability, thermodynamic stability, band-gap placement, optical-transition strength, dielectric screening, and carrier transport must all be satisfied within the vast A2BB'X6 space. We present a backward-mapping, genome-guided framework linking device-level targets to chemically interpretable descriptor families for Pb-free double-perovskite discovery. From 13,088 charge-balanced compositions, we apply a halide-aware workflow integrating geometric formability filtering, six-family chemical-genome descriptor encoding, evolutionary-optimized machine learning surrogates, SHAP-based interpretation, and DFT phenotype closure. Stability is modeled using Ehull-derived labels, while a band-gap surrogate predicts scalar-relativistic PBE Eg for target-driven selection. The funnel reduces the search space to seven DFT-validated candidates: K2BePdF6, K2MnCdCl6, Rb2TeCuBr6, Cs2SnGeBr6, Cs2GeSrBr6, Cs2NiBaI6, and Cs2AgInCl6, all verified for structural assignability, band-edge character, effective masses, dielectric response, optical absorption, conductivity, reflectivity, energy-loss spectra, and XRD fingerprints. Functional rules emerge from stability-function coupling rather than band-gap optimization alone, providing an interpretable inverse-design paradigm to accelerate Pb-free double-perovskite discovery.

cond-mat.mtrl-sci↗

arXiv2025

Hydrogen permeability prediction in palladium alloys and virtual screening of B2-phase stabilized Pd(100-x-y)CuxMy ternary alloys using machine learning

We present a forward prediction material screening framework designed to discover Pd-Cu alloys with improved B2 phase stability, thereby unlocking simultaneous $H_2$ generation and utilization. First, we trained CatBoost models with literature-derived Pd alloy data to predict $H_2$ permeability from composition and testing conditions. We evaluated fractional, composition-based, and physics-informed descriptors, individually and in combination, and showed that sequential Pearson filtering and fold-wise SHAP-based recursive feature elimination with cross-fold aggregation reduced errors while controlling complexity. Guided by the one-SE rule, a narrower domain-informed set of 13 features provided the best accuracy parsimony trade-off ($R^2=0.81$), only 0.01 below the max. $R^2$ achievable with 3x the number of features. SHAP analysis indicated that high permeability is promoted by elevated temperature, lattice expansion relative to Pd, atomic size mismatch, and favorable mixing tendencies. Second, the selected model was applied to screen $Pd_{(100-x-y)}Cu_{x}M_{y}$ spanning 16 co-dopants M for B2 stabilization. For each M system, we obtained the Pareto set of compositions that minimize Pd content and Miedema heat of formation and maximize the permeability, then picked three compounds, including that with the highest predicted permeability, the lowest Miedema heat of formation, and the lowest Pd content. With a final filter considering M concentration for single-phase Pd-M solution formation, we recommend Pd48.48Cu43.00Y8.52, Pd49.08Cu42.45Sc8.47, Pd56.09Cu33.70La10.21, and Pd52.68Cu40.44Mg6.88 for experimental validation. We predict those alloys to exhibit permeabilities 1.7 to 1.9 higher than B2 Pd60Cu40. Our framework provides plausible experimental targets and a scalable pathway for designing stable, high-temperature, H2-selective Pd-alloy membranes.

cond-mat.mtrl-sci↗