Materials Science & Engineering

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Author: search.filters.author.Mo, Yifei

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    Dataset for "Resistance of Boron Nitride Nanotubes to Radiation-Induced Oxidation" as published in The Journal of Physical Chemistry C
    (2024) Chao, Hsin-Yun (Joy); Nolan, Adelaide M.; Hall, Alex T.; Golberg, Dmitri; Park, Cheol; Yang, Wei-Chang David; Mo, Yifei; Sharma, Renu; Cumings, John
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    Design principles for sodium superionic conductors
    (Nature Portfolio, 2023-11-22) Wang, Shuo; Fu, Jiamin; Liu, Yunsheng; Saravanan, Ramanuja; Luo, Jing; Deng, Sixu; Sham, Tsun-Kong; Sun, Xueliang; Mo, Yifei
    Motivated by the high-performance solid-state lithium batteries enabled by lithium superionic conductors, sodium superionic conductor materials have great potential to empower sodium batteries with high energy, low cost, and sustainability. A critical challenge lies in designing and discovering sodium superionic conductors with high ionic conductivities to enable the development of solid-state sodium batteries. Here, by studying the structures and diffusion mechanisms of Li-ion versus Na-ion conducting solids, we reveal the structural feature of face-sharing high-coordination sites for fast sodium-ion conductors. By applying this feature as a design principle, we discover a number of Na-ion conductors in oxides, sulfides, and halides. Notably, we discover a chloride-based family of Na-ion conductors NaxMyCl6 (M = La–Sm) with UCl3-type structure and experimentally validate with the highest reported ionic conductivity. Our findings not only pave the way for the future development of sodium-ion conductors for sodium batteries, but also consolidate design principles of fast ion-conducting materials for a variety of energy applications.
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    Discrepancies and error evaluation metrics for machine learning interatomic potentials
    (Springer Nature, 2023-09-26) Liu, Yunsheng; He, Xingfeng; Mo, Yifei
    Machine learning interatomic potentials (MLIPs) are a promising technique for atomic modeling. While small errors are widely reported for MLIPs, an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properties in molecular dynamics (MD) simulations. In this study, we examine the state-of-the-art MLIPs and uncover several discrepancies related to atom dynamics, defects, and rare events (REs), compared to ab initio methods. We find that low averaged errors by current MLIP testing are insufficient, and develop quantitative metrics that better indicate the accurate prediction of atomic dynamics by MLIPs. The MLIPs optimized by the RE-based evaluation metrics are demonstrated to have improved prediction in multiple properties. The identified errors, the evaluation metrics, and the proposed process of developing such metrics are general to MLIPs, thus providing valuable guidance for future testing and improvements of accurate and reliable MLIPs for atomistic modeling.
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    Frustration in Super-Ionic Conductors Unraveled by the Density of Atomistic States
    (Wiley, 2023-02-07) Wang, Shuo; Liu, Yunsheng; Mo, Yifei
    The frustration in super-ionic conductors enables their exceptionally high ionic conductivities, which are desired for many technological applications including batteries and fuel cells. A key challenge in the study of frustration is the difficulties in analyzing a large number of disordered atomistic configurations. Using lithium super-ionic conductors as model systems, we propose and demonstrate the density of atomistic states (DOAS) analytics to quantitatively characterize the onset and degree of disordering, reveal the energetics of local disorder, and elucidate how the frustration enhances diffusion through the broadening and overlapping of the energy levels of atomistic states. Furthermore, material design strategies aided by the DOAS are devised and demonstrated for new super-ionic conductors. The DOAS is generally applicable analytics for unraveling fundamental mechanisms in complex atomistic systems and guiding material design.
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    Unsupervised discovery of solid-state lithium ion conductors
    (Springer Nature, 2019-11-20) Zhang, Ying; He, Xingfeng; Chen, Zhiqian; Bai, Qiang; Nolan, Adelaide M.; Roberts, Charles A.; Banerjee, Debasish; Matsunaga, Tomoya; Mo, Yifei; Ling, Chen
    Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10−4–10−1 S cm−1 predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data.
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    Statistical variances of diffusional properties from ab initio molecular dynamics simulations
    (Nature Publishing Group, 2018-04-03) He, Xingfeng; Zhu, Yizhou; Epstein, Alexander; Mo, Yifei
    Ab initio molecular dynamics (AIMD) simulation is widely employed in studying diffusion mechanisms and in quantifying diffusional properties of materials. However, AIMD simulations are often limited to a few hundred atoms and a short, sub-nanosecond physical timescale, which leads to models that include only a limited number of diffusion events. As a result, the diffusional properties obtained from AIMD simulations are often plagued by poor statistics. In this paper, we re-examine the process to estimate diffusivity and ionic conductivity from the AIMD simulations and establish the procedure to minimize the fitting errors. In addition, we propose methods for quantifying the statistical variance of the diffusivity and ionic conductivity from the number of diffusion events observed during the AIMD simulation. Since an adequate number of diffusion events must be sampled, AIMD simulations should be sufficiently long and can only be performed on materials with reasonably fast diffusion. We chart the ranges of materials and physical conditions that can be accessible by AIMD simulations in studying diffusional properties. Our work provides the foundation for quantifying the statistical confidence levels of diffusion results from AIMD simulations and for correctly employing this powerful technique.
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    Strategies Based on Nitride Materials Chemistry to Stabilize Li Metal Anode
    (John Wiley & Sons Ltd., 2017-03-03) Zhu, Yizhou; He, Xingfeng; Mo, Yifei
    Lithium metal battery is a promising candidate for high-energy-density energy storage. Unfortunately, the strongly reducing nature of lithium metal has been an outstanding challenge causing poor stability and low coulombic efficiency in lithium batteries. For decades, there are significant research efforts to stabilize lithium metal anode. However, such efforts are greatly impeded by the lack of knowledge about lithium-stable materials chemistry. So far, only a few materials are known to be stable against Li metal. To resolve this outstanding challenge, lithium-stable materials have been uncovered out of chemistry across the periodic table using first-principles calculations based on large materials database. It is found that most oxides, sulfides, and halides, commonly studied as protection materials, are reduced by lithium metal due to the reduction of metal cations. It is discovered that nitride anion chemistry exhibits unique stability against Li metal, which is either thermodynamically intrinsic or a result of stable passivation. The results here establish essential guidelines for selecting, designing, and discovering materials for lithium metal protection, and propose multiple novel strategies of using nitride materials and high nitrogen doping to form stable solid-electrolyte-interphase for lithium metal anode, paving the way for high-energy rechargeable lithium batteries.