Aron Walsh’s research while affiliated with Imperial College London and other places

Low lasing threshold and long‐term operational stability are essential in advancing cost‐effective, efficient lead‐free (tin) halide perovskite lasers. However, the rapid crystallization of tin perovskites and oxidation of Sn²⁺ lead to substantial amounts of lattice defects, detrimental to laser performance enhancement. Herein, a dual oxidation suppression strategy is developed to suppress the oxidation of Sn²⁺ 2D tin halide perovskites, i.e., adopting an oxygen‐free two‐step growth to enhance the crystal quality and incorporating electron‐donating biuret molecules to coordinate with Sn²⁺ during the crystal growth, which led to the substantial reduction of lasing threshold to <1 µJ cm⁻² in (PEA)2MASn2I7. This represents the lowest value in lead‐free perovskite nanolasers and approximately one order of magnitude lower than those previously reported for tin‐based nanolasers. Investigations into the spontaneous photoluminescence (PL) and stimulated lasing emission revealed that 2D tin perovskites exhibited superior photostability and lasing stability compared to their lead counterparts. Specifically, the lasing intensity of (PEA)2MA2Sn3I10 constantly increased by >300% under optical pumping and the lasing threshold decreased by ≈17%, which is not observed in their lead counterparts. The findings highlight the prospect of 2D tin halide perovskites as lead‐free gain materials and cavities for solution‐processed nanolasers with low lasing thresholds and exceptional stability.

The rules that govern structure and bonding, established for elemental solids and simple compounds, are challenging to apply to more complex crystals formed of polyatomic building blocks, such as layered or framework materials. Whether these modular building blocks are electrically neutral or charged influences the physical properties of the resulting crystal. Despite the prevalence of alternating charged units, their effects on the electronic structure remain unclear. We demonstrate how the distribution of charged building blocks, driven by differences in the electrostatic potential, governs the electronic band energies formed in layered crystals. This coarse-grained model predicts the spatially separated valence and conduction band edges observed in the metal-oxyhalide Ba2Bi3Nb2O11Cl and explains observed property trends in the Sillén–Aurivillius crystal system. Moreover, the general nature of the model allows for extension to other modular structure types, illustrated for Sillén and Ruddlesden–Popper layered compounds, and can support the rational design of electronic properties in diverse materials.

Direct air capture (DAC) of CO₂ is necessary for climate change mitigation, but it faces challenges from low atmospheric CO₂ concentrations and competition from water vapor. Metal-organic frameworks (MOFs) are attractive candidates for DAC owing to their exceptionally high surface area, tunable porosity, and potential for adsorption-based capture processes with relatively low regeneration cost. Identifying optimal MOFs is hindered by their structural complexity, the vastness of their chemical space, and the expense of accurate simulations. Here, we present a machine learning force field (MACE-DAC) tailored for CO2 and H2O interactions in MOFs by finetuning the foundation model MACE-MP-0. To address smoothing issues and catastrophic forgetting, we curated the diverse GoldDAC dataset and introduced a continual learning loss function. To efficiently sample gas configurations, we developed the DAC-SIM package that uses MLFFs to achieve ab initio quality thermodynamics based on Widom insertion at computational speeds comparable to classical force fields. High-throughput screening on more than 8,000 synthesized MOF structures was performed to identify optimal MOFs and extract important chemical features. This approach overcomes prior limitations in describing CO2/MOF and H₂O/MOF interactions, providing a scalable and accurate framework for accelerating DAC research for porous materials.

The structures and properties of organic–inorganic perovskites are influenced by the hydrogen bonding between the organic cations and the inorganic octahedral networks. This study explores the dynamics of hydrogen bonds in CH3NH3PbBr3 across a temperature range from 70 to 350 K, using molecular dynamics simulations with machine-learning force fields. The results indicate that the lifetime of hydrogen bonds decreases with increasing temperature from 7.6 ps (70 K) to 0.16 ps (350 K), exhibiting Arrhenius-type behavior. The geometric conditions for hydrogen bonding, which include bond lengths and angles, maintain consistency across the full temperature range. The relevance of hydrogen bonds for the vibrational states of the material is also evidenced through a detailed analysis of the vibrational power spectra, demonstrating their significant effect on the physical properties for this class of perovskites.

The vastness of chemical space presents a long-standing challenge for the exploration of new compounds with pre-determined properties. In materials science, crystal structure prediction has become a mature tool for mapping from composition to structure based on global optimisation techniques. Generative artificial intelligence (AI) now offers the means to efficiently navigate larger regions of crystal chemical space informed by structure-property datasets of materials. We introduce a model, named Chemeleon, designed to generate chemical compositions and crystal structures by learning from both textual descriptions and three-dimensional structural data. The model employs denoising diffusion techniques for compound generation using textual inputs aligned with structural data via cross-modal contrastive learning. The potential of this approach is demonstrated for multi-component compound generation, including the prediction of stable phases in the Li-P-S-Cl quaternary space of relevance to solid-state batteries. Our work highlights the potential of bridging geometric and linguistic data to unlock approaches to materials design.

Photovoltaics (PVs) are a critical technology for curbing growing levels of anthropogenic greenhouse gas emissions, and meeting increases in future demand for low-carbon electricity. In order to fulfill ambitions for net-zero carbon dioxide equivalent (CO2eq) emissions worldwide, the global cumulative capacity of solar PVs must increase by an order of magnitude from 0.9 TWp in 2021 to 8.5 TWp by 2050 according to the International Renewable Energy Agency, which is considered to be a highly conservative estimate. In 2020, the Henry Royce Institute brought together the UK PV community to discuss the critical technological and infrastructure challenges that need to be overcome to address the vast challenges in accelerating PV deployment. Herein, we examine the key developments in the global community, especially the progress made in the field since this earlier roadmap, bringing together experts primarily from the UK across the breadth of the PVs community. The focus is both on the challenges in improving the efficiency, stability and levelized cost of electricity of current technologies for utility-scale PVs, as well as the fundamental questions in novel technologies that can have a significant impact on emerging markets, such as indoor PVs, space PVs, and agrivoltaics. We discuss challenges in advanced metrology and computational tools, as well as the growing synergies between PVs and solar fuels, and offer a perspective on the environmental sustainability of the PV industry. Through this roadmap, we emphasize promising pathways forward in both the short- and long-term, and for communities working on technologies across a range of maturity levels to learn from each other.

Interface engineering is the core of device optimization, and this is particularly true for perovskite photovoltaics (PVs). The steady improvement in their performance has been largely driven by careful manipulation of interface chemistry to reduce unwanted recombination. Despite that, PVs devices still suffer from unavoidable open circuit voltage (VOC) losses. Here, we propose a different approach by creating a photo-ferroelectric perovskite interface. By engineering an ultrathin ferroelectric two-dimensional perovskite (2D) which sandwiches a perovskite bulk, we exploit the electric field generated by external polarization in the 2D layer to enhance charge separation and minimize interfacial recombination. As a result, we observe a net gain in the device VOC reaching 1.21 V, the highest value reported to date for highly efficient perovskite PVs, leading to a champion efficiency of 24%. Modeling depicts a coherent matching of the crystal and electronic structure at the interface, robust to defect states and molecular reorientation. The interface physics is finely tuned by the photoferroelectric field, representing a new tool for advanced perovskite device design.

Energy-efficient electrochemical reduction of nitrogen to ammonia could help in mitigating climate change. Today, only Li- and recently Ca-mediated systems can perform the reaction. These materials have a large intrinsic energy loss due to the need to electroplate the metal. In this work, we present a series of calculated energetics, formation energies, and binding energies as fundamental features to calculate the energetic distance between Li and Ca and potential new electrochemical nitrogen reduction systems. The featured energetic distance increases with the standard potential. However, dimensionality reduction using principal component analysis provides an encouraging picture; Li and Ca are not exceptional in this feature space, and other materials should be able to carry out the reaction. However, it becomes more challenging the more positive the plating potential is.

The combination of elements from the Periodic Table defines a vast chemical space. Only a small fraction of these combinations yields materials that occur naturally or are accessible synthetically. Here,...

High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials. However, the characteristics of elements change when they adopt a given oxidation state, with distinct structural preferences and physical properties. We explore several methods for developing embedding vectors of elements decorated with oxidation states. Graphs generated from 110 160 crystals are used to train representations of 84 elements that form 336 species. Clustering these learned representations of ionic species in low-dimensional space reproduces expected chemical heuristics, particularly the separation of cations from anions. We show that these representations have enhanced expressive power for property prediction tasks involving inorganic compounds. We expect that ionic representations, necessary for the description of mixed valence and complex magnetic systems, will support more powerful machine learning models for materials.