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Artificial intelligence driven laser parameter search: Inverse design of photonic surfaces using greedy surrogate-based optimization
Photonic surfaces designed with specific optical characteristics are becoming increasingly crucial for novel energy harvesting and storage systems. The design of these surfaces can be achieved by texturing materials using lasers. The optimal adjustment of laser fabrication parameters to achieve target surface optical properties is an open challenge. Thus, we develop a surrogate-based optimization approach. Our framework employs the Random Forest algorithm to model the forward relationship between the laser fabrication parameters and the resulting optical characteristics. During the optimization process, we use a greedy, prediction-based exploration strategy that iteratively selects batches of laser parameters to be used in experimentation by minimizing the predicted discrepancy between the surrogate model’s outputs and the user-defined target optical characteristics. This strategy allows for efficient identification of optimal fabrication parameters without the need to model the error landscape directly. We demonstrate the efficiency and effectiveness of our approach on two synthetic benchmarks and two specific experimental applications of photonic surface inverse design targets. By calculating the average performance of our algorithm compared to other state of the art optimization methods, we show that our algorithm performs, on average, twice as well across all benchmarks. Additionally, a warm starting inverse design technique for changed target optical characteristics enhances the performance of the introduced approach.
Artificial cathode electrolyte interphase for improving high voltage cycling stability of thick electrode with Co-free 5 V spinel oxides
Spinel-type cathode LiNi 0.5 Mn 1.5 O 4 (LNMO) has intrigued the transportation industry due to its high operating voltage and total elimination of the expensive cobalt element. However, LNMO cathode with high mass loading (> 3 mAh/cm 2 in areal capacity) has suffered from excessive capacity degradation upon long cycling. Here, a robust Al 2 O 3 surface layer is introduced to the thick LNMO electrode via atomic layer deposition (ALD). The capacity retention in full cells with the graphite anode is improved from 46.3% to 75.3% after 300 cycles with cutoff voltage up to 4.85 V, while enabling average Coulombic efficiency of 99.9% during the cycling. The post-mortem analyses reveal that the Al 2 O 3 surface layer would convert to Al-O-F /Al-F species upon cycling, offering stable interphase to protect the cathode material. Furthermore, these results demonstrate the significance of surface modification enabling high voltage cathode for next-generation LIBs.
AIBFT: Artificial Intelligence Browser Forensic Toolkit
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Geochemically induced shear slip in artificially fractured dolomite- and clay-cemented sandstone
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Computing thermodynamic properties of ammonia–water mixtures using artificial neural networks
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HYSCORE and DFT Studies of Proton-Coupled Electron Transfer in a Bioinspired Artificial Photosynthetic Reaction Center
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Comparative performance of ex situ artificial solid electrolyte interphases for Li metal batteries with liquid electrolytes
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Artificially low mild cognitive impairment to normal reversion rate in the Alzheimer's Disease Neuroimaging Initiative
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Downside in habitat restoration: Predation of artificial nests by invasive rodents in Macaronesian laurel forest
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Modeling forest ecosystem responses to elevated carbon dioxide and ozone using artificial neural networks
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