Browsing by Author "Ma, S"

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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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    Metal-organic frameworks with exceptionally high methane uptake: where and how is methane stored?
    (Wiley-VCH Verlag Berlin, 2010-05-03) Wu, H; Simmons, JM; Liu, Y; Brown, CM; Wang, XS; Ma, S; Peterson, VK; Southon, PD; Kepert, CJ; Zhou, HC; Yildirim, T; Zhou, W
    Metal–organic frameworks (MOFs) are a novel family of physisorptive materials that have exhibited great promise for methane storage. So far, a detailed understanding of their methane adsorption mechanism is still scarce. Herein, we report a comprehensive mechanistic study of methane storage in three milestone MOF compounds (HKUST-1, PCN-11, and PCN-14) the CH4 storage capacities of which are among the highest reported so far among all porous materials. The three MOFs consist of the same dicopper paddlewheel secondary building units, but contain different organic linkers, leading to cagelike pores with various sizes and geometries. From neutron powder diffraction experiments and accurate data analysis, assisted by grand canonical Monte Carlo (GCMC) simulations and DFT calculations, we anambiguously revealed the exact locations of the stored methane molecules in these MOF materials. We found that methane uptake takes place primarily at two types of strong adsorption site: 1) the open Cu coordination sites, which exhibit enhanced Coulomb attraction toward methane, and 2) the van der Waals potential pocket sites, in which the total dispersive interactions are enhanced due to the molecule being in contact with multiple “surfaces”. Interestingly, the enhanced van der Waals sites are present exclusively in small cages and at the windows to these cages, whereas large cages with relatively flat pore surfaces bind very little methane. Our results suggest that further, rational development of new MOF compounds for methane storage applications should focus on enriching open metal sites, increasing the volume percentage of accessible small cages and channels, and minimizing the fraction of large pores. © 2010, Wiley-VCH Verlag Berlin