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Sanaa Ismail

Publications and source records attributed to Sanaa Ismail.

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Machine-learning screening and first-principles lattice dynamics resolve the most stable structure of NaCa(BH$_4$)$_3$ from a data-driven candidate search

Equimolar NaCa(BH$_4$)$_3$ offers a theoretical hydrogen capacity of 11.24 wt.% and a decomposition enthalpy expected to fall between those of NaBH$_4$ and Ca(BH$_4$)$_2$, but it has never been prepared and no crystal structure has been reported. Predictions for it have so far been built within the perovskite family that its heavier homologues adopt. Here that assumption is removed. Six candidate frameworks were assembled from three independent sources --- experimentally determined ABX$_3$ borohydrides, a distorted perovskite from data-driven structure prediction, and an unbiased search over a structural database in which all 2238 generated candidates were relaxed with none excluded --- and at fixed charge-neutral composition the cation arrangement of every framework was enumerated exhaustively, 420 decorations reducing to 118 symmetry-inequivalent configurations, screened with a machine-learning potential and settled from first principles. The most stable structure is not a perovskite. It is a monoclinic framework of space group Cm, reached only by the unrestricted search, at $-$4.185640 eV/atom, 7.67 meV/atom below the best framework available beforehand; two chemically unrelated donor prototypes converge on it to 0.076 meV/atom, and its ordered cation arrangement is the ground state of its own series. Its phonon spectrum carries no imaginary mode at any wavevector the supercell resolves exactly and its relaxed-ion elastic tensor is positive definite, whereas the orthorhombic perovskite candidate is unstable to $-$2.401 THz. That contrast supplies a physical reason for the reported failure to obtain this composition in perovskite form, and the simulated diffraction pattern reported here identifies the predicted framework by seven reflections that neither parent phase nor the competing framework produces.

cond-mat.mtrl-sci

Lead-free piezoelectric perovskites for arterial-pulse e-skin: from configurational complexity to equivariant machine-learning potentials

Continuous, non-invasive monitoring of the arterial pulse is a clinical priority for cardiovascular disease, the leading cause of global mortality. Flexible piezoelectric electronic skins can transduce the 1-10 kPa pressure wave into a self-powered voltage, but the best-performing piezoceramics are lead-based, and their toxicity is incompatible with skin contact and with tightening RoHS/REACH regulation. Among lead-free alternatives, the BaTiO$3$-based solid solution BZT-BCT reaches $d{33} \approx 620$ pC/N near its tricritical morphotropic phase boundary, rivalling soft PZT while remaining biocompatible. Exploiting this in a wearable confronts a sensitivity-flexibility paradox and three computational walls: the combinatorial explosion of atomic configurations in a disordered solid solution, the band-gap error of affordable density-functional approximations, which corrupts leakage and insulation estimates, and the 0 K nature of standard calculations against a 310 K operating temperature. We review lead-free piezoelectrics, morphotropic-boundary physics and fabricated flexible devices, then argue that equivariant machine-learning interatomic potentials --- coupled to a tiered functional hierarchy and finite-temperature lattice dynamics --- can survey the full configurational ensemble at body temperature and close the gap to a clinically viable lead-free pulse sensor.

cond-mat.mtrl-sci