Mo, Yifei
Maryland Energy Innovation Institute
EDUCATION
- Postdoctoral Research Associate, Massachusetts Institute of Technology, 2010-2013
- Ph.D., University of Wisconsin-Madison, 2010
HONORS & AWARDS
- Highly Cited Researcher (Top 0.1%), Clarivate Web of Science, 2023
- World’s top 2% scientists (based on single-year citation) by the Stanford University list, 2022
- Highly Cited Researcher (Top 0.1%), Clarivate Web of Science, 2022
- Junior Faculty Outstanding Research Award, A. James Clark School of Engineering, University of Maryland, 2022
- Highly Cited Researcher (Top 0.1%), Clarivate Web of Science, 2021
- World’s top 2% scientists (based on single-year citation) by the Stanford University list, 2021
- Highly Cited Author (top 5%), Royal Society of Chemistry, 2019
- Top Peer Reviewer in Materials Science (top 1%) and Cross-Field (top 1%), Web of Science, 2019
- Outstanding Young Scientist Award, Maryland Academy of Sciences, 2019
Editorial Board
- NPJ Computational Materials
- Energy Storage Materials
- Advanced Theory and Simulations
- Energy Materials
- Journal of Materials Informatics
- Computational materials science
- Computational materials design and materials discovery
- Molecular dynamics simulations
- Large-scale atomistic modeling
- Materials for energy storage and conversion
My research aims to advance the understanding, design, and discovery of engineering materials through cutting-edge computational techniques. We target critical materials problems that impede high-impact technologies, such as energy storage, conversion, and efficiency. In our research, the computational modeling provides enhanced fundamental scientific insights, and enables the ability to rationally design new materials.
Accelerated design and discovery of novel materials through computation. Computational techniques based on first principles are capable of predicting materials properties with little or no experimental input. In our research, we leverage an array of computational techniques to design new materials with enhancement in multiple properties. With the aid of supercomputers, computational methods can significantly speed up the innovation and development of new materials. Our current efforts focus on solid-state batteries, solid oxide fuel cell, and various membrane materials.
Selected publications: Advanced Energy Materials, 9, 1902078 (2019), Joule, 2, 2016-2046 (2018); Nature communications, 8, 15893 (2017); Advanced Energy Materials, 1702998 (2018); Advanced Science, 1600517 (2017); Physical Chemistry Chemical Physics, 17, 18035-18044 (2015); Nature Materials, 14,1026–1031(2015); Energy and Environmental Science, 6, 148-156 (2013); Chemistry of Materials, 24, 15-17 (2012)
Understanding materials and interfaces in beyond Li-ion energy storage systems. The next-generation energy storage systems may be based on novel chemistries, such as all-solid-state, Li metal, Li-sulfur, and metal-oxygen, to achieve significantly higher energy density. Materials and their interfaces in these batteries are often the key limiting factors and origins of failures. For example, the degradation at the electrolyte-electrode interfaces causes poor cyclability, low coulombic efficiency, and premature failure in these new battery systems. We use state-of-the-art computation techniques to understand the limiting factors and failure mechanisms at the interfaces, and to computationally design solutions (such as novel coating materials) for these new energy technologies.
Selected publications: Angewandte Chemie Int. Ed., 59, 8039-8043 (2019), ACS Energy Letters, 4, 2444-2451 (2019), Joule, 2, 2016-2046 (2018); Journal of Materials Chemistry A, 4, 3253-3266 (2016) (Front cover); Advanced Science, 1600517 (2017); ACS Applied Materials & Interfaces, 7, 23685-23693 (2015)
Experimental collaborations: Nature Materials 16, 572-579 (2017); Advanced Energy Materials, 6, 1501590 (2016); Science Advances, 3, e1601659 (2017);Journal of the American Chemical Society, 138 (37), 12258–12262 (2016); Advanced Materials (2017); Nature Communications, 7, 11441 (2016); ACS Nano, 10, 9577–9585, (2016); Nano Letter, 15, 5755–5763 (2015)
Large-scale atomistic modeling and molecular dynamics. Large-scale atomistic modeling has the unique capability to capture complex materials phenomena, ranging from interfaces, nanostructures, to non-equilibrium dynamics. However, current large-scale modeling methods based on classical force fields have limited accuracy, transferability, and predictivity, while higher level ab initio methods are often limited in system size (hundreds of atoms) and time-scale (tens of ps). We aim to bridge the gap between ab initio methods and large-scale atomistic modeling. Integrating these techniques across different length scales enable us the unique capability to study complex processes with full atomistic details.
Selected publications: Nature, 457, 1116-1119 (2009); Nature Materials, 12, 9-11 (2013); Journal of Physics D: Applied Physics, 44, 405401 (2011); Applied Physics Letters, 90, 181926 (2007)
- ENMA 461: Thermodynamics of Materials
- ENMA 400 / ENMA 600: Atomistic Modeling in Materials (Formerly ENMA 489A/ENMA 698A)
- ENMA 401 / ENMA 601: Continuum Modeling of Materials (Formerly ENMA 489C/ENMA 698C)
- ENMA 300 / ENME 382: Introduction to Materials Engineering
- ENMA 688: Seminar in Materials Science and Engineering
- ENMA 499: Senior Laboratory Project
- PHYS 499A: Special Problems in Physics
- ENMA 698: Special Problems in Engineering Materials
- ENMA 312: Experimental Methods in Materials Science (Guest lecturer for computational methods)
See full publication record on [Google Scholar], [Web of Science], [ORCID], [ResearchGate]
Selected Publications
- Yunsheng Liu, Yifei Mo, “Assessing the Accuracy of Machine Learning Interatomic Potentials in Predicting the Elemental Orderings: A Case Study of Li-Al Alloys”, Acta Materialia, 268, 119742 (2024)
- Shuo Wang1#, Jiamin Fu1, Yunsheng Liu#, Ramanuja Srinivasan Saravanan#, Jing Luo, Sixu Deng, Tsun-Kong Sham, Xueliang Sun*, Yifei Mo*, “Design principles for sodium superionic conductors”, Nature Communications, 14, 7615 (2023)
- Yunsheng Liu, Xingfeng He, Yifei Mo*, "The Discrepancies and Error Evaluation Metrics for Machine Learning Interatomic Potentials on Simulating Atom Dynamics", NPJ Computational Materials 9, 174 (2023)
- Menghao Yang, Yunsheng Liu, Yifei Mo*, “Lithium Crystallization at Solid Interfaces”, Nature Communications, 14, 2986 (2023)
- Shuo Wang, Yunsheng Liu, Yifei Mo*, "Frustration in Super-Ionic Conductors Unraveled by the Density of Atomistic States", Angewandte Chemie Int. Ed. e202215544 (2023) (Very Important Paper - top 5% ranked by all reviewers; Hot topic: Artificial Intelligence and Machine Learning)
- Md Shafiqul Islam, Shuo Wang, Alex T. Hall, Yifei Mo*, “First-Principles Computational Design and Discovery of Solid-Oxide Proton Conductors”, Chemistry of Materials 34, 13, 5938–5948 (2022)
- Hiram Kwak1, Shuo Wang1#, Juhyoun Park, Yunsheng Liu#, Kyu Tae Kim, Yeji Choi, Yifei Mo*, Yoon Seok Jung*, “Emerging halide superionic conductors for all-solid-state batteries: Design, synthesis, and practical applications”, ACE Energy Letters 7, 1776-1805 (2022) (ACS Editor’s choice: featured one article per day from all ACS journals. Most read article.)
- Menghao Yang, Yifei Mo*, "Interfacial Defect of Lithium Metal in Solid-State Batteries", Angewandte Chemie Int. Ed. 60, 21494, (2021) (Very Important Paper ¬– top 5% ranked by all reviewers)
- Menghao Yang, Yunsheng Liu, Adelaide M. Nolan, Yifei Mo*, "Interfacial Atomistic Mechanisms of Lithium Metal Stripping and Plating in Solid-State Batteries", Advanced Materials 33, 2008081 (2021)
- Adelaide M. Nolan, Eric D. Wachsman, Yifei Mo*, "Computation-Guided Discovery of Coating Materials to Stabilize the Interface between Lithium Garnet Solid Electrolyte and High-Energy Cathodes for All-Solid-State Lithium Batteries", Energy Storage Materials 41,571-580, (2021)
- Yunsheng Liu, Shuo Wang, Adelaide M. Nolan, Chen Ling, and Yifei Mo*, “Tailoring the Cation Lattice for Chloride Lithium-Ion Conductors“, Advanced Energy Materials 10, 2002356 (2020)
- Yizhou Zhu*, Yifei Mo*, “Materials Design Principles for Air-Stable Lithium/Sodium Solid Electrolytes”, Angewandte Chemie Int. Ed. 59, 17472 (2020)
- Chengwei Wang1, Weiwei Ping1, Qiang Bai1#, Huachen Cui1, Ryan Hensleigh1, Ruiliu Wang, Alexandra H. Brozena, Zhenpeng Xu, Jiaqi Dai, Yong Pei, Chaolun Zheng, Glenn Pastel, Jinlong Gao, Xizheng Wang, Howard Wang, Ji-Cheng Zhao, Bao Yang, Xiaoyu (Rayne) Zheng*, Jian Luo*, Yifei Mo*, Bruce Dunn, Liangbing Hu*, “A general method to synthesize and sinter bulk ceramics in seconds”, Science 368, 6490, 521-526 (2020) (Front Cover)
- Ying Zhang, Xingfeng He#, Zhiqian Chen, Qiang Bai#, Adelaide M Nolan#, Charles A. Roberts, Debasish Banerjee, Tomoya Matsunaga, Yifei Mo*, Chen Ling*, “Unsupervised Discovery of Solid-State Lithium Ion Conductors”, Nature Communications, 10, 5260 (2019) (Editor’s choice in Energy Storage Materials; Top 50 Chemistry and Materials Sciences Articles; Top 25 Most Read Article in Chemistry and Materials Science)
- Xingfeng He1, Qiang Bai1, Yunsheng Liu, Adelaide M. Nolan, Chen Ling, Yifei Mo*, “Crystal Structural Framework of Lithium Super-Ionic Conductors”, Advanced Energy Materials 9, 1902078 (2019) (Inside Front Cover)
- Adelaide M. Nolan, Yunsheng Liu, Yifei Mo*, “Solid-State Chemistries Stable with High-Energy Cathodes for Lithium-Ion Batteries”, ACS Energy Letters, 4, 2444-2451 (2019)
- Shuo Wang#, Qiang Bai#, Adelaide M. Nolan#, Yunsheng Liu#, Sheng Gong, Qiang Sun*, and Yifei Mo*, “Lithium Chlorides and Bromides as Promising Solid-State Chemistries for Fast Ion Conductors with Good Electrochemical Stability”, Angewandte Chemie Int. Ed., 59, 8039-8043 (2019)
- Adelaide Nolan, Yizhou Zhu, Xingfeng He, Qiang Bai, Yifei Mo*, “Computation-Accelerated Design of Materials and Interfaces for All-Solid-State Lithium-Ion Batteries”, Joule, 2, 2016-2046 (2018)
- Xingfeng He, Yizhou Zhu, Yifei Mo*, “Origin of Fast Ion Diffusion in Super-Ionic Conductors” Nature communications, 8, 15893 (2017) (Web of Science ESI highly cited paper; Most viewed article in 2017)
- Xingfeng He, Yizhou Zhu, Alexander Epstein, Yifei Mo*, “Statistical Variances of Diffusional Properties from Ab Initio Molecular Dynamics Simulations”, NPJ Computational Materials, 4, 18 (2018)
- Yizhou Zhu, Xingfeng He, Yifei Mo*, “Strategies Based on Nitride Materials Chemistry to Stabilize Li Metal Anode”, Advanced Science, 1600517 (2017)
- Yizhou Zhu, Xingfeng He, Yifei Mo*, “First Principles Study on Electrochemical and Chemical Stability of the Solid Electrolyte-Electrode Interfaces in All-Solid-State Li-ion Batteries”, Journal of Materials Chemistry A, 4, 3253-3266 (2016) (Featured front cover; 2016 Hot Papers; Most cited research article in 2016; Web of Science ESI highly cited paper)
- Yizhou Zhu, Xingfeng He, Yifei Mo*, “Origin of Outstanding Stability in the Lithium Solid Electrolyte Materials: Insights from Thermodynamic Analyses Based on First-Principles Calculations”, ACS Applied Materials & Interfaces, 7, 23685-23693 (2015) (The most cited article among over 50,000 published articles of the journal since 2009 launch. The Most Read article of the journal. Web of Science ESI highly cited paper)
- Yifei Mo, Kevin T. Turner, Izabela Szlufarska, “Friction laws at the nanoscale”, Nature, 457, 1116-1119 (2009)
* corresponding author; # group members in multiple-group collaboration papers; 1 co-first authors.