arXiv subjects

Javier Sanz Rodrigo

Publications and source records attributed to Javier Sanz Rodrigo.

3 recordsLinked to original sources

arXiv2026

Rapid estimation of synthesizability windows of inorganic materials from first principles

Fast prediction of the synthesizability conditions of materials remains challenging, even assuming synthesis under thermodynamic equilibrium. We combine density functional theory (DFT) with machine-learned interatomic potentials to enable high-throughput generation of phase predominance diagrams as a function of temperature and partial pressures of the gaseous reactants. These diagrams can immediately be used by experimentalists to translate computational predictions into real synthesis parameters in the lab. Predominance diagrams are generated for a diverse set of binary compounds and for 48 more complex ternary metal phosphosulfide systems, but the method is in principle scalable to any inorganic material class. The calculated predominance diagrams generally show good agreement with the experimental synthesis literature, with a drastic reduction in computational cost compared to a full DFT approach. We find several examples of compounds that appear as metastable in a zero-temperature stability hull picture, but that become thermodynamically stable under well-defined synthesis windows.

cond-mat.mtrl-sci↗

arXiv2026

A sulfonitride transparent conductive thin film with ultra-high refractive index

With the rise of AI-assisted materials screening, extraordinary properties are now frequently predicted in experimentally uncharted material systems, highlighting the need to develop new synthesis methods for unconventional materials beyond the classic bulk powder form. Here, we establish the first thin-film growth route for any metal sulfonitride compound by realizing Zr2SN2 films with a rare and compelling combination of optical and electrical properties. Zr2SN2 is transparent across most of the visible range while exhibiting a very high average refractive index of 2.95 in the visible, exceeding expectations based on conventional refractive index-bandgap scaling. Importantly, the same Zr2SN2 film shows degenerate n-type conductivity with carrier density above 10^20 cm-3 and intragrain mobility above 8 cm2V-1s-1, approaching those of established transparent conductive oxides. Zr2SN2 thus demonstrates that strong light-matter interaction, optical transparency and electrical conductivity can be reconciled within a single material platform, revealing a new class of high-refractive-index transparent conductors.

cond-mat.mtrl-sci↗

arXiv2026

AI-enhanced discovery and accelerated synthesis of metal phosphosulfides

Metal phosphosulfides have emerged as unique multifunctional materials, but they present unique synthesis challenges compared to more established material classes such as oxides and nitrides. As a consequence, experimental development and theoretical understanding of phosphosulfides have focused on individual compounds rather than on accelerated broad-range exploration. In this work, we first evaluate the synthesizability and band gaps of 909 hypothetical ternary phosphosulfides by density functional theory. We find 19 previously unknown thermodynamically stable compounds, including the first Si- and Ge-based phosphosulfides. For rapid band gap prediction, we then develop a multi-fidelity machine learning model to translate semilocal density functional theory band gaps into experimentally calibrated band gaps. Importantly, we extend the accelerated material development workflow to the experimental domain by demonstrating a route to high-throughput synthesis and characterization of virtually any phosphosulfide material system. The method is based on thin-film combinatorial libraries and yields over 100 unique compositions in each experiment, enabling us to synthesize four distinct phosphosulfide compounds in only four combinatorial experiments without prior synthesis recipes and without compromising on material quality. Thus, we argue that accelerated materials development workflows combining theory, artificial intelligence, synthesis, and characterization can be viable even for experimentally challenging inorganic materials.

cond-mat.mtrl-sci↗