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We screen a large chemical space of perovskite alloys for systems with the right properties to accommodate a morphotropic phase boundary (MPB) in their composition-temperature phase diagram, a crucial feature for high piezoelectric…
Sequential generative models conditioned on uncertain rewards are central to AI-driven scientific discovery, yet the epistemic uncertainty they inherit from imperfect reward estimates remains unquantified. We propagate this uncertainty…
The discovery of synthetically accessible organic semiconductors with exceptional performance remains a critical bottleneck in materials science. While these materials offer compelling advantages - structural modularity, mechanical…
We describe a first open-access database of experimentally investigated hybrid organic-inorganic materials with two-dimensional (2D) perovskite-like crystal structure. The database includes 515 compounds, containing 180 different organic…
The significant surge in energy use and escalating environmental concerns have sparked worldwide interest towards the study and implementation of solar cell technology. Perovskite solar cells (PSCs) have garnered remarkable attention as an…
Machine learning (ML) models for predicting gas permeability through polymers have traditionally relied on experimental data. While these models exhibit robustness within familiar chemical domains, reliability wanes when applied to new…
Understanding electrochemical interfaces at a microscopic level is essential for elucidating important electrochemical processes in electrocatalysis, batteries and corrosion. While \textit{ab initio} simulations have provided valuable…
Proton exchange membrane fuel cells (PEMFCs) are a promising clean energy technology, offering high efficiency and near-zero operational emissions for stationery and automotive applications. However, their widespread adoption remains…
This paper presents a non-invasive approach to estimate the layer thicknesses of perovskite solar cells. The thicknesses are predicted by a convolutional neural network that leverages the external quantum efficiency of a perovskite solar…
Perovskite solar cells (PSCs) based on methylammonium lead iodide (MAPbI3) exhibit remarkable photovoltaic performance, where interface engineering with hole transport layers (HTLs) is crucial for optimizing charge transfer and device…
Explainable machine learning can help to discover new physical relationships for material properties. To understand the material properties that govern the activation energy for oxygen diffusion in perovskites and pyrochlores, we build a…
The significant advancements in perovskite solar cell (PSC) research and development have reignited optimism for the practicality of solar energy. A deeper comprehension of the fundamental mechanisms is essential for enhancing the current…
Machine learning enables rapid estimation of material parameters in solar cells via neural-network-based surrogate models. However, the reliability of extracted parameters depends on underlying assumptions such as the choice of…
Hybrid organic-inorganic halide perovskites have emerged as a disruptive new class of materials, exhibiting optimum properties for a broad range of optoelectronic applications, most notably for photovoltaics. The first report of highly…
Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations employing best-performing graph neural network…
Perovskite semiconductors have demonstrated outstanding external luminescence quantum yields, enabling high power conversion efficiencies (PCE). However, the precise conditions to advance to an efficiency regime above monocrystalline…
Halide perovskite light absorbers have great advantages for photovoltaics such as efficient solar energy absorption, but charge accumulation and recombination at the interface with an electron transport layer (ETL) remains a major challenge…
We propose a computational framework, Hetero-EUCLID, for segmentation and parameter identification to characterize the full hyperelastic behavior of all constituents of a heterogeneous material. In this work, we leverage the Bayesian-EUCLID…
Vacancy-ordered double perovskites (VODPs) are promising alternatives to three-dimensional lead halide perovskites for optoelectronic and photovoltaic applications. Mixing these materials creates a vast compositional space, allowing for…
The study of aerosol formation and chemistry using machine learning is limited by the lack of molecular descriptors suited to atmospheric compounds. Interpretable models are particularly affected because they often rely on dictionary-based…