相关论文: Open Catalyst Experiments 2024 (OCx24): Bridging E…
Solid-state hydrogen storage materials are promising candidates for safe and compact hydrogen storage; however, data-driven discovery in this field remains limited by the availability of large-scale, well-curated datasets. Here, we present…
The environmental impact of Artificial Intelligence (AI) is emerging as a significant global concern, particularly regarding model training. In this paper, we introduce GREEN (Guided Recommendations of Energy-Efficient Networks), a novel,…
A comprehensive understanding of the electrode-electrolyte interface in energy conversion systems remains challenging due to the complex and multifaceted nature of interfacial processes. This complexity hinders the development of more…
Electrocatalysts for the oxygen evolution reaction (OER) are an important component for the transition from fossil to sustainable energy. Commercialization of cost-effective earth-abundant electrocatalysts is in large parts hindered by…
With the rise of AI in recent years and the increase in complexity of the models, the growing demand in computational resources is starting to pose a significant challenge. The need for higher compute power is being met with increasingly…
The conversion of $\mathrm{CO_2}$ to value-added compounds is an important part of the effort to store and reuse atmospheric $\mathrm{CO_2}$ emissions. Here we focus on $\mathrm{CO_2}$ hydrogenation over so-called inverse catalysts:…
Accurate predictions of reactive mixing are critical for many Earth and environmental science problems. To investigate mixing dynamics over time under different scenarios, a high-fidelity, finite-element-based numerical model is built to…
This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to…
The discovery and design of new materials are paramount in the development of green technologies. High entropy oxides represent one such group that has only been tentatively explored, mainly due to the inherent problem of navigating vast…
Water splitting reactions through photocatalysis is an efficient and sustainable technique for the generation of green energy. The photocatalyst's ability to effect simultaneous generation of hydrogen and oxygen, along with efficiency in…
The key challenge in advancing multivalent-ion batteries lies in finding suitable intercalation hosts. Open-tunnel oxides, featuring one-dimensional channels or nanopores, show promise for enabling effective ion transport. However, the vast…
Objective. Hypoxic tumor subvolumes resist radiation through elevated oxygen enhancement ratios (OER), yet no computational OER model is simultaneously particle-specific, mechanistically grounded, and fast enough for voxel-scale treatment…
Janus type Water-Splitting Catalysts have attracted highest attention as a tool of choice for solar to fuel conversion. AISI Ni 42 steel was upon harsh anodization converted in a bifunctional electrocatalyst. Oxygen evolution reaction-…
The use of transition group metals in electric batteries requires extensive usage of critical elements like lithium, cobalt and nickel, which poses significant environmental challenges. Replacing these metals with redox-active organic…
We are merging a large participatory science effort with machine learning to enhance the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). Our overall goal is to remove false positives, allowing us to use lower signal-to-noise data…
ATLAS, a general-purpose experiment at the Large Hadron Collider (LHC), makes use of a large internationally-distributed computing infrastructure, including over $10^6$ TB of managed data on disk and tape and almost one million…
The rapid expansion of exoplanet survey missions such as Kepler, TESS, and the upcoming PLATO mission has generated massive light-curve datasets that challenge traditional vetting pipelines. We introduce a hybrid deep-learning framework…
Artificial Intelligence (AI) compilers are critical for efficiently deploying AI models across diverse hardware platforms. However, they remain prone to bugs that can compromise both compiler reliability and model correctness. Thus,…
The substantial increase in AI model training has considerable environmental implications, mandating more energy-efficient and sustainable AI practices. On the one hand, data-centric approaches show great potential towards training…
Accurate thermochemical data with sub-chemical accuracy (within 1 kcal mol$^{-1}$ of the empirical ground truth) are essential for advancing computational chemistry methods. However, existing datasets that reach this level of accuracy…