Materials Science & Engineering

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Author: search.filters.author.He, Xingfeng

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Now showing 1 - 5 of 5
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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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    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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    First Principle Computational Study of Fast Ionic Conductors
    (2018) He, Xingfeng; Mo, Yifei; Material Science and Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Fast ionic conductors have great potential to enable novel technologies in energy storage and conversion. However, it is not yet understood why only a few materials can deliver exceptionally higher ionic conductivity than typical solids or how on can design fast ion conductors following simple principles. In this dissertation, I applied first principles computational method to understanding the fast ionic diffusion within fast ionic conductors and I demonstrated a conceptually simple framework for guiding the design of super-ionic conductor materials. I studied Na0.5Bi0.5TiO3 (NBT) as the model material for oxygen ionic conductor. The structure-property relationship of the NBT materials is established. Based on the newly gained materials understanding, our first principles computation predicted that Na and K were promising dopants to increase oxygen ionic conductivity. The newly designed NBT materials with A-site Na and K substituted A sites exhibited a many-fold increase in the ionic conductivity at 900K comparing to that in the experimental compound. We demonstrated that the concerted migration mechanism with low energy barrier is the universal mechanism of fast ionic diffusion in a broad range of ionic conducting materials. Our theory provides a conceptually simple framework for guiding the design of super-ionic conductor materials, that is, inserting mobile ions into high-energy sites to activate concerted ion conduction with lower migration barriers. We demonstrated this strategy by designing a number of novel fast Li-ion conducting materials to activate concerted migration with reduced diffusion barrier. We identified the common features of crystal structural framework for lithium SICs. Based on the determined attributes, we performed a high-throughput screening of all lithium-containing oxide and sulfide compounds. The screening revealed several crystal structures that are potential to be fast ion conductors. Through aliovalent doping, we modified the Li content of these structures which resulting in different Li sublattice within the structure and we found a number of lithium super- ionic conductors that are predicted to have Li+ conductivities greater than 0.1 mS/cm at 300K.
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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.