Rebecca A. Durr | Searcharxiv

arXiv2026

AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution

Catalyzing acidic oxygen evolution at the proton-exchange-membrane water electrolysis (PEMWE) anode relies almost entirely on iridium or ruthenium, drawn from concentrated supply chains that constrain gigawatt-scale deployment. We report an artificial intelligence (AI)-guided, human-supervised closed-loop platform (>90% automation) integrating combinatorial sputter synthesis, high-throughput screening, machine-learning composition-property models, adaptive multi-objective optimization, and context-aware large-language-model reasoning, where lead catalysts advanced to long-term validation in 1 M H2SO4 at 10 mA cm-2. Navigating a combinatorial metal oxide space, the platform iteratively evaluated the activity-stability trade-off of 2,942 catalysts across 53 material systems and 26 elements, surfacing Ir- and Ru-free complex oxides such as InMnPdOx and NiTaPdOx that conventional design logic, and off-the-shelf language models, would not predict. In retrospective benchmarking, our sequential learning agent advanced the activity-stability frontier faster than fixed-policy Bayesian optimization or in-context language-model selection. During long-term testing, NiTaPdOx operated at lower overpotential than PdOx, but both eventually exceeded 0.5 V: PdOx at ~200 h and NiTaPdOx at ~470 h. InMnPdOx showed a similar overpotential improvement in addition to a dramatic increase in operational stability, retaining overpotential below 0.5 V over 1,000 h of operation. The additive elements promote the formation of a nanostructure that is associated with catalytic activity while stabilizing Pd against corrosion. The results highlight the power of AI-driven science in addressing long-standing challenges in materials chemistry, and the greater availability of Pd relative to incumbent Ir and Ru offers a near-term option to ease supply constraints on scaled electrochemical H2 generation.

cond-mat.mtrl-sci↗

arXiv2017

Hierarchical On-Surface Synthesis of Deterministic Graphene Nanoribbon Heterojunctions

Bottom-up graphene nanoribbon (GNR) heterojunctions are nanoscale strips of graphene whose electronic structure abruptly changes across a covalently bonded interface. Their rational design offers opportunities for profound technological advancements enabled by their extraordinary structural and electronic properties. Thus far the most critical aspect of their synthesis, the control over sequence and position of heterojunctions along the length of a ribbon, has been plagued by randomness in monomer sequences emerging from step-growth copolymerization of distinct monomers. All bottom-up GNR heterojunction structures created so far have exhibited random sequences of heterojunctions and, while useful for fundamental scientific studies, are difficult to incorporate into functional nanodevices as a result. Here we describe a new hierarchical fabrication strategy that allows deterministic growth of bottom-up GNRs that preferentially exhibit a single heterojunction interface rather than a random statistical sequence of junctions along the ribbon. Such heterojunctions provide a viable new platform that could be directly used in functional GNR-based device applications at the molecular scale. Our hierarchical GNR fabrication strategy is based on differences in the dissociation energies of C-Br and C-I bonds that allow control over the growth sequence of the block-copolymers from which GNRs are formed, and consequently yields a significantly higher proportion of single-junction GNR heterostructures. Scanning tunnelling spectroscopy and density functional theory calculations confirm that hierarchically-grown heterojunctions between chevron GNR (cGNR) and binaphthyl-cGNR segments exhibit straddling Type I band alignment in structures that are only one atomic layer thick and 3 nm in width.

cond-mat.mtrl-sci↗