Faculty Directory

Editorial Board

• NPJ Computational Materials
• Energy Storage Materials
• Advanced Theory and Simulations

• Computational materials science
• Computational materials design and materials discovery
• Molecular dynamics simulations
• Large-scale atomistic modeling
• Materials for energy storage and conversion

My research advances the understanding, design, and discovery of materials through computation and AI, targeting critical materials problems that limit high-impact technologies such as energy storage, conversion, and computing. Our computational modeling delivers fundamental physical insight and the ability to rationally design new materials. 

Watch videos of our research presentations here. 

Accelerated materials design and discovery through computation and AI/ML. We combine high-throughput first-principles computation with machine learning to design and discover materials, evaluating the full set of properties — phase stability, electrochemical stability, interface compatibility, synthesizability, thermal and moisture stability — that determine whether a material actually works in application. This approach has led to the discovery of new solid-electrolyte chemistries, including mixed-anion oxyhalide Li-ion conductors and Na-ion-conducting chloride families, several of which have since been confirmed experimentally and, in one case, patented industrially. 

Selected publications: Science, 390, 199-204 (2025); Nature Chemistry, 16, 1584-1591 (2024); Nature Communications, 14, 7615 (2023); Advanced Materials, 36, 2308012 (2024); Journal of the American Chemical Society, 145, 2183-2194 (2023); Advanced Energy Materials, 9, 1902078 (2019); Nature Communications, 10, 5260 (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)

Understanding and designing materials interfaces in energy storage systems. Interfaces are often the limiting factor in battery performance and processing. We use first-principles thermodynamics and large-scale atomistic simulation to reveal how solid-electrolyte/electrode interfaces degrade, how Li stripping and plating proceed and fail at the atomic scale, and how protective coatings can stabilize these interfaces — with several predictions (interfacial amorphous Li layers, amorphous-to-BCC Li deposition pathways) since confirmed by experiment.

Selected publications:  Nature Communications, 14, 2986 (2023); Angewandte Chemie Int. Ed., 60, 21494 (2021); Advanced Materials, 33, 2008081 (2021);  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)

Large-scale atomistic modeling of complex materials phenomena. Large-scale atomistic simulation captures materials behavior governed by long length and time scales that are inaccessible to first-principles methods alone. We apply this to Li/Na/Mg/Zn metal-anode failure and crystallization mechanisms, disorder-to-order transitions in high-entropy nanoparticles, and mechanical deformation and friction at interfaces. 







Selected publications: Nature Communications, 14, 2986 (2023); Angewandte Chemie Int. Ed., 60, 21494 (2021); Advanced Materials, 33, 2008081 (2021);   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)

Trustworthy, interpretable AI/ML for materials science. Rather than treating machine learning as a black box, we develop AI/ML methods that are rigorously tested, physically interpretable, and capable of revealing new mechanisms — including application-oriented benchmarks for machine-learning interatomic potentials and our Density of Atomistic States (DOAS) framework for quantifying disorder and frustration in ion transport.

Selected publications: npj Computational Materials, 9, 174 (2023); npj Computational Materials, 10, 159 (2024); Acta Materialia, 268, 119742 (2024); Angewandte Chemie Int. Ed., 62, e202215544 (2023)

• 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

• Kiyan Amirian, Ramanuja Srinivasan Saravanan, Felix Adams, Charles E Schwarz, Yifei Mo*, FPBench: Application-Oriented Error Decomposition for Foundation Potentials, arxiv: 2609.05714
• 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 Wang^1#, 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*, "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 Wang^1#, 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 Bai^1#, 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.