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The development of new battery materials, particularly novel cathode chemistries, is essential for enabling next generation energy storage technologies. In this work, we employ a multi-fidelity screening protocol combining the Energy-GNoME…
The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential, but has not yet been realized1,2. The complexity and literally infinite diversity of conjugated molecular…
Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously…
While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persist. Recently, leveraging neural networks to parameterize the…
Optimizing solution-processed organic solar cells is a complex task due to the vast parameter space in organic photovoltaics (OPV). Classical Edisonian or one-variable-at-a-time (OVAT) optimization approaches are laborious, time-consuming,…
Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven…
Solar energy is rapidly becoming a robust renewable energy source to conventional finite resources such as fossil fuels. It is harvested using interconnected photovoltaic panels, typically built with crystalline silicon cells, i.e.…
Kohn-Sham density functional theory (KS-DFT) is a powerful method to obtain key materials' properties, but the iterative solution of the KS equations is a numerically intensive task, which limits its application to complex systems. To…
Elemental selenium is a promising wide-bandgap ($E_\mathrm{G}\approx$ 1.95 eV) photovoltaic material for the next generation of thin-film solar cells. To realize high-efficiency selenium solar cells, it is crucial to optimize the…
Photocatalytic water splitting has emerged as a sustainable pathway for hydrogen production, leveraging sunlight to drive chemical reactions. This review explores the integration of density functional theory (DFT) with machine learning (ML)…
We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible…
Active learning has been increasingly applied to screening functional materials from existing materials databases with desired properties. However, the number of known materials deposited in the popular materials databases such as ICSD and…
Dark photons with masses in the 1-100 eV range can be produced in the Sun and subsequently absorbed in terrestrial detectors, offering a promising avenue for probing hidden-sector physics beyond the Standard Model. In this work, we develop…
Harnessing the recent advance in data science and materials science, it is feasible today to build predictive models for materials properties. In this study, we employ the data of high-throughput quantum mechanics calculations based on…
Materials databases built from calculations based on density functional approximations play an important role in the discovery of materials with improved properties. Most databases thus constructed rely on the generalized gradient…
With the expected rise in behind-the-meter solar penetration within the distribution networks, there is a need to develop time-series forecasting methods that can reliably predict the net-load, accurately quantifying its uncertainty and…
Metal-insulator transition (MIT) materials are a useful platform for emerging microelectronic, optoelectronic, and neuromorphic devices, but their discovery is hindered by the high computational cost of electronic structure modeling, the…
Fullerene like cages and naonotubes of carbon and other inorganic materials are currently under intense study due to their possible technological applications. First principle simulations of these materials are computationally challenging…
Adequate characterization of two-dimensional materials with low energy barriers for impurity adsorption is key for advancing applications based on catalysis, sensing, and surface functionalization. However, first-principles methods, such as…
Finding new materials with previously unknown atomic structure or materials with optimal set of properties for a specific application greatly benefits from computational modeling. Recently, such screening has been dramatically accelerated…