Browsing by Author "Liu, D"

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    Data quantity governance for machine learning in materials science
    (Oxford University Press (OUP), 2023-05-31) Liu, Y; Yang, ZW; Zou, XX; Ma, S; Liu, D; Avdeev, M; Shi, S
    Data-driven machine learning (ML) is widely employed in the analysis of materials structure–activity relationships, performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction. However, because of the laborious process of materials data acquisition, ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size (for traditional ML models) or the mismatch between model parameters and sample size (for deep-learning models), usually resulting in terrible performance. Here, we review the efforts for tackling this issue via feature reduction, sample augmentation and specific ML approaches, and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity governance. Following this, we propose a synergistic data quantity governance flow with the incorporation of materials domain knowledge. After summarizing the approaches to incorporating materials domain knowledge into the process of ML, we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and applications. The work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML. © The Author(s) 2023. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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    Generative artificial intelligence and its applications in materials science: current situation and future perspectives
    (Elsevier, 2023-07) Liu, Y; Yang, ZW; Yu, ZY; Liu, Z; Liu, D; Lin, H; Li, MQ; Ma, S; Avdeev, M; Shi, SQ
    Generative Artificial Intelligence (GAI) is attracting the increasing attention of materials community for its excellent capability of generating required contents. With the introduction of Prompt paradigm and reinforcement learning from human feedback (RLHF), GAI shifts from the task-specific to general pattern gradually, enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships. Here, we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology. The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected. Taking ChatGPT as an example, we explore the potential applications of general GAI in generating multiple materials content, solving differential equation as well as querying materials FAQs. Furthermore, we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions. This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development. © 2023 The Authors. Published by Elsevier B.V. on behalf of The Chinese Ceramic Society. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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    Signature of modern meteorological and glacial lake outburst floods in fjord sediments (Baker River, Chilean Patagonia)
    (American Geophysical Union (AGU), 2021-12) Bertrand, S; Vandekerkhove, E; Liu, D; Renson, V; Kylander, ME; Saunders, KM; Reid, B; Torrejón, F
    Floods are among the most destructive natural hazards on Earth. In paleohydrology, sediments are generally considered as one of the best archives to extend flood records to pre-historical timescales. Doing so requires being able to identify flood deposits from sediment archives and decipher between flood types. The latter is particularly important in glacierized regions, where meteorological floods frequently co-occur with Glacial Lake Outburst Floods (GLOFs). In Patagonia, GLOFs are particularly pronounced in the Baker River watershed (48°S), where 23 events occurred between 2008 and 2020. Since 1976, the same region experienced three intense rain-on-snow events. To identify the sedimentary signature of these flood events, ten sediment cores collected in the fjord immediately downstream of the Baker River (Martínez Channel) were investigated and compared to the recent flood history of the river. Results show that sediments accumulate on the fjord head delta at 2.0 to 3.4 cm yr-1 and that GLOF deposits can be distinguished from background sediments by their finer grain size (5.98 ± 0.82 μm) and lower organic carbon content (0.31 ± 0.06%), reflecting the release and transport in suspension of high amounts of glacial rock flour. Our results also show that the rain-on-snow events that occur in summer, and therefore primarily affect the glacierized part of the watershed, have the same sedimentary signature as GLOFs. In contrast, rain-on-snow events occurring in winter have a distinct coarse and organic-rich signature, reflecting sediment input from the non-glacierized part of the watershed. In summary, this study shows that (a) GLOF deposits in fjord sediments are distinct from typical flood turbidites and are best identified by their low grain size and total organic carbon content, and (b) the sedimentary signature of rain-on-snow floods in partially glacierized watersheds depends on the season during which they occur. We anticipate that our findings will contribute to a better interpretation of flood records from partially glacierized watersheds.
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    Silver nanoparticles prepared by gamma irradiation across metal organic framework templates
    (Royal Society of Chemistry, 2015-01-07) He, L; Dumée, LF; Liu, D; Velleman, L; She, FH; Banos, C; Davies, JB; Kong, LX
    In this study, we demonstrate for the first time the successful fabrication of well-dispersed ultrafine silver nanoparticles inside metal–organic frameworks through a single step gamma irradiation at room temperature. HKUST-1 crystals are soaked in silver nitrate aqueous solution and irradiated with a Cobalt 60 source across a range of irradiation doses to synthesize highly uniformly distributed silver nano-particles. The average size of the silver nanoparticles across the Ag@HKUST-1 materials is found to vary between 1.4 and 3 nm for dose exposures between 1 and 200 kGy, respectively. The Ag@HKUST-1 hybrid crystals exhibit strong surface plasmon resonance and are highly durable and efficient catalytic materials for the reduction of 4-nitrophenol to 4-aminophenol (up to 14.46 × 10−3 s−1 for 1 kGy Ag@HKUST-1). The crystals can be easily recycled for at least five successive cycles of reaction with a conversion efficiency higher than 99.9%. The gamma irradiation is demonstrated to be an effective and environmental friendly process for the synthesis of nano-particles across confined metal–organic frameworks at room temperature with potential applications in environmental science. © 2015 The Royal Society of Chemistry