Unlocking the Potential of Open-Tunnel Oxides: DFT-Guided Design and Machine Learning-Enhanced Discovery for Next- Generation Industry-Scale Battery Technologies
Unlocking the Potential of Open-Tunnel Oxides: DFT-Guided Design and Machine Learning-Enhanced Discovery for Next- Generation Industry-Scale Battery Technologies
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释放开放式隧道氧化物的潜力：DFT 引导设计和机器学习增强发现下一代工业规模电池技术
DOI：
10.1039/d4ya00014e
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Datta, Dibakar
中科院分区：
文献类型：
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作者：
Datta, Joy;Koratkar, Nikhil;Datta, Dibakar
锂离子电池（LIB）在日常应用中无处不在。然而，锂（Li）是地球上的有限资源，因此不可持续。作为锂的替代物，在电池系统中已经积极地研究了地球上丰富且更便宜的多价金属，例如铝（Al）和钙（Ca）。然而，迫切需要找到合适的多价离子电池的插层主体。开放隧道氧化物代表了一种特殊类别的微粒，其特征在于存在集成的一维通道或纳米孔。本论文主要研究了两种有前景的开放隧道氧化物：铌钨氧化物（NTO）和钼钒氧化物（MoVO）。MoVO结构由于其更大的表面积和不同的形状，可以容纳比NTO更多数量的多价离子。具体地，MoVO结构可以吸附Ca、Li和Al离子，吸附电势范围为约4至5eV。然而，由于有限的通道面积，六方通道的Al离子的吸附势下降到1.73 eV。NTO结构对于一种Li、Ca和Al分别表现出4.4eV、3.4eV和0.9eV的插入/吸附电势。通常，在MoVO和NTO结构中，Ca离子比Al离子更容易被吸附。Bader电荷分析和电荷密度图揭示了电荷转移和离子尺寸在将Ca和Al等多价离子插入MoVO和NTO系统中的作用。探索用于电池应用的开放隧道氧化物材料受到巨大的成分可能性的阻碍。执行实验性试验和基于量子的模拟对于解决在大量复杂的可能性中定位特定项目的挑战是不可行的。因此，必须进行结构稳定性测试，以确定具有足够孔隙拓扑结构的可行组合。采用数据挖掘和机器学习技术来发现创新的过渡金属氧化物材料。这项研究比较了两种机器学习算法，一种使用描述符，另一种使用图形来预测实验室环境中新材料的可合成性。这项研究的结果提供了有价值的见解，探索替代天然存在的多尺度颗粒，表现出很有前途的潜力，利用多价离子在电池相关的情况下。
Lithium–ion batteries (LIBs) are ubiquitous in everyday applications. However, lithium (Li) is a limited resource on the planet and, therefore, not sustainable. As an alternative to lithium, earth-abundant and cheaper multivalent metals such as aluminum (Al) and calcium (Ca) have been actively researched in battery systems. However, finding suitable intercalation hosts for multivalent-ion batteries is urgently needed. Open-tunneled oxides represent a specific category of microparticles distinguished by the presence of integrated one-dimensional channels or nanopores. This work focuses on two promising open-tunnel oxides: niobium tungsten oxide (NTO) and molybdenum vanadium oxide (MoVO). The MoVO structure can accommodate a larger number of multivalent ions than NTO due to its larger surface area and different shapes. Specifically, the MoVO structure can adsorb Ca, Li, and Al ions with adsorption potentials ranging from around 4 to 5 eV. However, the adsorption potential for hexagonal channels of Al ions drops to 1.73 eV due to the limited channel area. The NTO structure exhibits an insertion/adsorption potential of 4.4 eV, 3.4 eV, and 0.9 eV for one Li, Ca, and Al, respectively. Generally, Ca ions are more readily adsorbed than Al ions in both MoVO and NTO structures. Bader charge analysis and charge density plots reveal the role of charge transfer and ion size in the insertion of multivalent ions such as Ca and Al into MoVO and NTO systems. Exploring open-tunnel oxide materials for battery applications is hindered by vast compositional possibilities. The execution of experimental trials and quantum-based simulations is not viable for addressing the challenge of locating a specific item within a large and complex set of possibilities. Therefore, it is imperative to conduct structural stability testing to identify viable combinations with sufficient pore topologies. Data mining and machine learning techniques are employed to discover innovative transition metal oxide materials. This study compares two machine learning algorithms, one utilizing descriptors and the other employing graphs to predict the synthesizability of new materials inside a laboratory setting. The outcomes of this study offer valuable insights into the exploration of alternative naturally occurring multiscale particles that exhibit promising potential for the utilization of multivalent ions in battery-related contexts.