Eder Giovanni Lomeli

All Publications

• Electronic Structure of the Alternating Monolayer-Trilayer Phase of La3Ni2O7 PHYSICAL REVIEW LETTERS Abadi, S., Xu, K., Lomeli, E. G., Puphal, P., Isobe, M., Zhong, Y., Fedorov, A., Mo, S., Hashimoto, M., Lu, D., Moritz, B., Keimer, B., Devereaux, T. P., Hepting, M., Shen, Z. 2025; 134 (12)

  View details for DOI 10.1103/PhysRevLett.134.126001

  View details for Web of Science ID 001458978700002

• Time-Resolved X-Ray Spectroscopy from the Atomic Orbital Ground State Up PHYSICAL REVIEW X Jost, D., Lomeli, E. G., Tang, T., Kas, J. J., Rehr, J. J., Lee, W., Jiang, H., Moritz, B., Devereaux, T. P. 2025; 15 (1)

  View details for DOI 10.1103/PhysRevX.15.011012

  View details for Web of Science ID 001413221600001

• Predicting Reactivity and Passivation of Solid-State Battery Interfaces. ACS applied materials & interfaces Lomeli, E. G., Ransom, B., Ramdas, A., Jost, D., Moritz, B., Sendek, A. D., Reed, E. J., Devereaux, T. P. 2024

  Abstract

  In this work, we build a computationally inexpensive, data-driven model that utilizes atomistic structure information to predict the reactivity of interfaces between any candidate solid-state electrolyte material and a Li metal anode. This model is trained on data from ab initio molecular dynamics (AIMD) simulations of the time evolution of the solid electrolyte-Li metal interfaces for 67 different materials. Predicting the reactivity of solid-state interfaces with ab initio techniques remains an elusive challenge in materials discovery and informatics, and previous work on predicting interfacial compatibility of solid-state Li-ion electrolytes and Li metal anodes has focused mainly on thermodynamic convex hull calculations. Our framework involves training machine learning models on AIMD data, thereby capturing information on both kinetics and thermodynamics, and then leveraging these models to predict the reactivity of thousands of new candidates in the span of seconds, avoiding the need for additional weeks-long AIMD simulations. We identify over 300 new chemically stable and over 780 passivating solid electrolytes that are predicted to be thermodynamically unfavored. Our results indicate many potential solid-state electrolyte candidates have been incorrectly labeled unstable via purely thermodynamic approaches using density functional theory (DFT) energetics, and that the pool of promising, Li-stable solid-state electrolyte materials may be much larger than previously thought from screening efforts. To showcase the value of our approach, we highlight two borate materials that were identified by our model and confirmed by further AIMD calculations to likely be highly conductive and chemically stable with Li: LiB13C2 and LiB12PC.

  View details for DOI 10.1021/acsami.4c06095

  View details for PubMedID 39277815

• Electrolyte Coatings for High Adhesion Interfaces in Solid-State Batteries from First Principles. ACS applied materials & interfaces Ransom, B., Ramdas, A., Lomeli, E., Fidawi, J., Sendek, A., Devereaux, T., Reed, E. J., Schindler, P. 2023

  Abstract

  We introduce an adhesion parameter that enables rapid screening for materials interfaces with high adhesion. This parameter is obtained by density functional theory calculations of individual single-material slabs rather than slabs consisting of combinations of two materials, eliminating the need to calculate all configurations of a prohibitively vast space of possible interface configurations. Cleavage energy calculations are used as an upper bound for electrolyte and coating energies and implemented in an adapted contact angle equation to derive the adhesion parameter. In addition to good adhesion, we impose further constraints in electrochemical stability window, abundance, bulk reactivity, and stability to screen for coating materials for next-generation solid-state batteries. Good adhesion is critical in combating delamination and resistance to lithium diffusivity in solid-state batteries. Here, we identify several promising coating candidates for the Li7La3Zr2O12 and sulfide electrolyte systems including the previously investigated electrode coating materials LiAlSiO4 and Li5AlO8, making them especially attractive for experimental optimization and commercialization.

  View details for DOI 10.1021/acsami.3c04452

  View details for PubMedID 37682811

• A Solution-Processable High-Modulus Crystalline Artificial Solid Electrolyte Interphase for Practical Lithium Metal Batteries ADVANCED ENERGY MATERIALS Yu, Z., Seo, S., Song, J., Zhang, Z., Oyakhire, S. T., Wang, Y., Xu, R., Gong, H., Zhang, S., Zheng, Y., Tsao, Y., Mondonico, L., Lomeli, E. G., Wang, X., Kim, W., Ryu, K., Bao, Z. 2022

  View details for DOI 10.1002/aenm.202201025

  View details for Web of Science ID 000817818300001

• Correlative image learning of chemo-mechanics in phase-transforming solids. Nature materials Deng, H. D., Zhao, H., Jin, N., Hughes, L., Savitzky, B. H., Ophus, C., Fraggedakis, D., Borbely, A., Yu, Y., Lomeli, E. G., Yan, R., Liu, J., Shapiro, D. A., Cai, W., Bazant, M. Z., Minor, A. M., Chueh, W. C. 2022

  Abstract

  Constitutive laws underlie most physical processes in nature. However, learning such equations in heterogeneous solids (for example, due to phase separation) is challenging. One such relationship is between composition and eigenstrain, which governs the chemo-mechanical expansion in solids. Here we developed a generalizable, physically constrained image-learning framework to algorithmically learn the chemo-mechanical constitutive law at the nanoscale from correlative four-dimensional scanning transmission electron microscopy and X-ray spectro-ptychography images. We demonstrated this approach on LiXFePO4, a technologically relevant battery positive electrode material. We uncovered the functional form of the composition-eigenstrain relation in this two-phase binary solid across the entire composition range (0≤X≤1), including inside the thermodynamically unstable miscibility gap. The learned relation directly validates Vegard's law of linear response at the nanoscale. Our physics-constrained data-driven approach directly visualizes the residual strain field (by removing the compositional and coherency strain), which is otherwise impossible to quantify. Heterogeneities in the residual strain arise from misfit dislocations and were independently verified by X-ray diffraction line profile analysis. Our work provides the means to simultaneously quantify chemical expansion, coherency strain and dislocations in battery electrodes, which has implications on rate capabilities and lifetime. Broadly, this work also highlights the potential of integrating correlative microscopy and image learning for extracting material properties and physics.

  View details for DOI 10.1038/s41563-021-01191-0

  View details for PubMedID 35177785

• Correlative analysis of structure and chemistry of LixFePO(4) platelets using 4D-STEM and X-ray ptychography MATERIALS TODAY Hughes, L. A., Savitzky, B. H., Deng, H. D., Jin, N. L., Lomeli, E. G., Yu, Y., Shapiro, D. A., Herring, P., Anapolsky, A., Chueh, W. C., Ophus, C., Minor, A. M. 2022; 52: 102-111

  View details for DOI 10.1016/j.mattod.2021.10.031

  View details for Web of Science ID 000840325900010

• Molecular design for electrolyte solvents enabling energy-dense and long-cycling lithium metal batteries NATURE ENERGY Yu, Z., Wang, H., Kong, X., Huang, W., Tsao, Y., Mackanic, D. G., Wang, K., Wang, X., Huang, W., Choudhury, S., Zheng, Y., Amanchukwu, C., Hung, S. T., Ma, Y., Lomeli, E. G., Qin, J., Cui, Y., Bao, Z. 2020

  View details for DOI 10.1038/s41560-020-0634-5

  View details for Web of Science ID 000542060100001