Yee-Fun Lim

High entropy alloys (HEAs), composed of five or more elements, are rapidly emerging as a transformative platform for next generation electrocatalysis. The unique multielement nature of HEAs offers distinct advantages over conventional alloys, including tunable electronic structures, defect‐rich active sites, and superior stability under harsh electrochemical environments. Crucially, the vast compositional space of HEAs provides a strategic pathway to mitigate the global sustainability paradox associated with Platinum Group Metals (PGMs). By leveraging the “cocktail effect” and inherent lattice distortions, HEAs enable the design of “PGM‐thrifted” catalysts that maximize atom utilization efficiency and drastically minimize precious metal consumption without compromising performance. In this review, we provide a critical overview of recent advances in HEA electrocatalysts, emphasizing: (a) fundamental concepts and core effects governing HEA behavior; (b) criteria for strategic elemental selection to ensure phase stability and critical mineral substitution; (c) morphology engineering from 0D nanoparticles to 3D interconnected nanoporous frameworks to optimize mass transport; (d) synthesis strategies classified by their primary driving forces; (d) diverse applications including HER, OER, HOR, ORR, AOR, CO ₂ RR, and NRR; and (f) in situ characterization techniques and DFT calculation for mechanistic understanding. Finally, the review concludes with a perspective on current challenges, including the integration of machine learning and techno‐economic assessments to guide the sustainable development of high‐performance HEAs for a global carbon‐neutral energy economy. image

Metal halide perovskites are attractive for optoelectronic applications, but the existing solution-based synthetic methods rely on hazardous solvents and lack reproducibility. To overcome these challenges, solvent-free mechanochemical synthesis of perovskites can be conducted, although traditional opaque ball milling equipment hinder real-time monitoring of the reaction progress. Herein, mechanochemistry was performed with time-lapsed in situ (TLIS) measurements to elucidate the optical properties during the synthesis. The TLIS spectrometer enabled real-time observation of the impact of varying the compositions on the absorption properties of formamidinium/methylammonium lead iodide (FA_xMA_1-xPbI₃, x = 0 to 1) and facilitated optimization of the synthesis duration. Moreover, we can accelerate the evaluation and directly observe the effects of different synthetic strategies for enhancing the photoluminescence quantum yield and stability of the perovskites, providing near-infrared (NIR) emitting tin-based perovskites FASnI_zBr_3-z (where z = 0 to 3) with core-shell structures. Intriguingly, halide ion migration was deduced in lead-free double perovskites Cs₂Na_yAg_1-yBiCl₆ (y = 0 to 1.0) at room temperature during aging after mechanochemical activation. Our work provides insights into the mechanochemistry of compounds with low thermodynamic barriers.

A hard and soft acid and base theory guided “ship-in-a-bottle” strategy to incorporate bismuth nanoparticles inside a thiol-rich MOF unlocking dual catalytic activities.

We employed multi-objective Bayesian optimization to achieve Pareto optimal solutions for sustainable terpolymer synthesis, targeting a high glass transition temperature and increased incorporation of renewable monomers.

Lead‐free metal halide perovskites can potentially be air‐ and water‐stable photocatalysts for organic synthesis, but there are limited studies on them for this application. Separately, machine learning (ML), a critical subfield of artificial intelligence, has played a pivotal role in identifying correlations and formulating predictions based on extensive datasets. Herein, an iterative workflow by incorporating high‐throughput experimental data with ML to discover new lead‐free metal halide perovskite photocatalysts for the aerobic oxidation of styrene is described. Through six rounds of ML optimization guided by SHapley Additive exPlanations (SHAP) analysis, BA₂CsAg_0.95Na_0.05BiBr₇ as a photocatalyst that afforded an 80% yield of benzoic acid under the standard conditions is identified, which is a 13‐fold improvement compared to the 6% with when using Cs₂AgBiBr₆ as the initial photocatalyst benchmark that is started. BA₂CsAg_0.95Na_0.05BiBr₇ can tolerate various functional groups with 22 styrene derivatives, highlighting the generality of the photocatalytic properties demonstrated. Radical scavenging studies and density functional theory calculations revealed that the formation of the reactive oxygen species superoxide and singlet oxygen in the presence of BA₂CsAg_0.95Na_0.05BiBr₇ are critical for photocatalysis.

The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints. Here, we devise an Evolution-Guided Bayesian Optimization (EGBO) algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not only solves for the Pareto Front (PF) efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space. The algorithm is developed together with a custom self-driving lab for seed-mediated silver nanoparticle synthesis, targeting 3 objectives (1) optical properties, (2) fast reaction, and (3) minimal seed usage alongside complex constraints. We demonstrate that, with appropriate constraint handling, EGBO performance improves upon state-of-the-art qNEHVI. Furthermore, across various synthetic multi-objective problems, EGBO shows significative hypervolume improvement, revealing the synergy between selection pressure and the qNEHVI optimizer. We also demonstrate EGBO’s good coverage of the PF as well as comparatively better ability to propose feasible solutions. We thus propose EGBO as a general framework for efficiently solving constrained multi-objective problems in high-throughput experimentation platforms.

Applying electro-organic synthesis in flow configuration can potentially reduce the pharmaceutical industry's carbon footprint and simplify the reaction scale-up.

The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints. Here, we devised an Evolution-Guided Bayesian Optimization (EGBO) algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not only solves for the Pareto Front (PF) efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space. The algorithm was developed together with a custom self-driving lab for seed-mediated silver nanoparticle synthesis, targeting 3 objectives (1) optical properties, (2) fast reaction, and (3) minimal seed usage alongside complex constraints. We demonstrated that, with appropriate constraint handling, EGBO performance improves upon state-of-the-art qNEHVI. Furthermore, across various synthetic multi-objective problems, EGBO showed significative hypervolume improvement, revealing the synergy between selection pressure and the qNEHVI optimizer. We also demonstrated EGBO’s good coverage of the PF as well as comparatively better ability to propose feasible solutions. We thus propose EGBO as a general framework for efficiently solving constrained multi-objective problems in high-throughput experimentation platforms.

Copper antimony sulfides are regarded as promising catalysts for photo‐electrochemical water splitting because of their earth abundance and broad light absorption. The unique photoactivity of copper antimony sulfides is dependent on their various crystalline structures and atomic compositions. Here, a closed‐loop workflow is built, which explores Cu–Sb–S compositional space to optimize its photo‐electrocatalytic hydrogen evolution from water, by integrating a high‐throughput robotic platform, characterization techniques, and machine learning (ML) optimization workflow. The multi‐objective optimization model discovers optimum experimental conditions after only nine cycles of integrated experiments–machine learning loop. Photocurrent testing at 0 V versus reversible hydrogen electrode (RHE) confirms the expected correlation between the materials’ properties and photocurrent. An optimum photocurrent of −186 µA cm⁻² is observed on Cu–Sb–S in the ratio of 9:45:46 in the form of single‐layer coating on F‐doped SnO₂ (FTO) glass with a corresponding bandgap of 1.85 eV and 63.2% Cu¹⁺/Cu species content. The targeted intelligent search reveals a nonobvious CuSbS composition that exhibits 2.3 times greater activity than baseline results from random sampling.