Publications

Unlocking Superionic Sodium Transport and Synthesis with MLIPs

All-solid-state sodium batteries are attractive for grid-scale storage, but the search for a solid electrolyte that combines high ionic conductivity, mechanical compliance, and electrochemical stability has been challenging. In this collaboration with the Meng group published in Joule, we demonstrate that a metastable orthorhombic sodium closo-hydridoborate, Na₃(B₁₂H₁₂)(BH₄) (o-NBH) achieves 4.6 mS cm⁻¹ room-temperature conductivity and enables thick-cathode Na-ASSBs with >3 mAh cm⁻² areal capacity.
Our group’s main contribution to this work is in the application of state-of-the-art machine learning interatomic potentials (MLIPs) in guiding synthesis and probing ion transport. MAVRL member, Zihan Yu, used using high-throughput r2SCAN density-functional theory (DFT) calculations to create a dataset and fine-tune a M3GNet foundation potential for the Na-B-H system. This MLIP allowed the generation of finite-temperature phase diagrams with phonon-derived vibrational entropies. These calculations revealed taht o-NBH sits ~16 meV atom⁻¹ above the 0 K hull but is entropically stabilized above ~650 K, in agreement with experiments.
MD simulations with the fine-tuned M3GNet potential shows that o-NBH exhibits 3D Na⁺ diffusion pathways with activation energies of 0.13 eV (short-range) and 0.23 eV (long-range), consistent with NMR relaxation experiments. One major innovation is the use of artificial anion masses to probe the effect of anion rotation on ionic conductivity. Unlike most computational studies that rigidly freeze atoms, our approach provides a more calibrated approach to modify anion rotation dynamics. We find that increased B₁₂H₁₂ or BH₄ cluster masses suppressed Na⁺ conductivity without significantly raising the activation barrier. This suggests that anion rotations boost the population of mobile Na⁺ rather than lowering the hopping barrier.

Check out the work here.

Publications

Unlocking Ultrafast Li-Ion Highways on Carbon Surfaces

For decades, researchers have relied on carbonaceous materials like graphite and carbon black as workhorse components in lithium-ion batteries (LIBs). These carbons usually function as hosts for lithium intercalation, where Li ions slip between graphene layers. But there’s always been a catch: lithium diffusion through bulk carbon is relatively slow, limiting performance and requiring the help of liquid or solid electrolytes. In this work, we collaborated with the group of Prof Ping Liu and researchers at the University of Maryland, and the University of Houston to identify a completely new superionic lithium transport pathway along the surface of carbon materials. We found that in carbon blacks with high surface area but limited lithium storage capacity, such as Ketjen black (KB), lithium ions move astonishingly fast across the surface once lithiated. At room temperature, lithiated KB exhibited an ionic conductivity of 18.1 mS/cm, outperforming many leading solid electrolytes. Our group’s contributions (led by Ji Qi) is in the form of DFT calculations to show that lithium at the surface encounters much lower migration barriers (as low as 0.15 eV) than lithium inside bulk graphite. In effect, the carbon surface becomes a highway for ultrafast lithium transport.
This surface-mediated conduction has several implications:
– Lithiated carbon blacks can act as protective interlayers between lithium metal and solid electrolytes, suppressing dangerous dendrite growth.
– Mixed ionic–electronic conductors like KB serve as both electron and ion conductors, enabling more stable, high-performance anodes.
– Full-cell batteries with carbon black interlayers retained ~85% capacity over 300 cycles, compared to rapid short-circuiting without them.
The findings point to a new design principle for solid-state ion conductors: look for materials with high surface area, thermodynamic stability, and weak ion–surface interactions. With these criteria, carbon blacks aren’t just inert additives anymore—they’re active enablers of next-generation solid-state batteries.

Read the full paper here: ACS Nano Letters – Superionic Surface Li-Ion Transport in Carbonaceous Materials

Publications

MatGL

Graph deep learning is transforming materials science by enabling accurate, scalable, and efficient predictions of material properties and potential energy surfaces (PES). Our article on “Materials Graph Library (MatGL) — an open-source, modular framework purpose-built for materials science and chemistry” has been published in npj Computational Materials. MatGL started as a collaboration between Intel Labs and the Materials Virtual Lab to provide a “batteries-included” environment for:

  • Implementing both invariant and equivariant graph neural networks (GNNs), including M3GNet, MEGNet, CHGNet, TensorNet, and SO3Net.
  • Leveraging pretrained foundation potentials (FPs) covering the full periodic table for out-of-the-box property predictions and atomistic simulations.
  • Seamless integration with ASE and LAMMPS for high-fidelity molecular dynamics, geometry optimization, and property calculations.
  • Training custom models with PyTorch Lightning for efficient parallelization on CPUs, GPUs, and TPUs.

We show that MatGL’s models achieving state-of-the-art accuracy on widely used datasets (QM9, Matbench, ANI-1x, MPF, MatPES) while maintaining competitive computational efficiency. The library’s design also supports fine-tuning, enabling rapid adaptation to new materials systems.

Read the full paper here and explore MatGL on GitHub.

Publications

High-Fidelity Machine Learning Interatomic Potentials with Multi-Fidelity Training

Machine learning interatomic potentials (MLIPs) are powerful tools for atomistic simulations, but training them with high-fidelity quantum mechanical data is costly. Most MLIPs rely on low-cost PBE calculations, while more accurate SCAN functionals are computationally expensive.

In this work, Tsz Wai Ko introduces a multi-fidelity M3GNet approach that can achieve SCAN-level accuracy with 10% SCAN data, reducing computational costs significantly.

Key Findings

Silicon: Captures phase transitions & structural properties with high accuracy.
Water: Predicts liquid & ice structures better than single-fidelity models.
Efficiency: Cuts high-fidelity data requirements by up to 90% while improving accuracy.

Why It Matters

✔️ Faster, cheaper MLPs for materials simulations.
✔️ Better generalization to unseen materials.
✔️ A pathway to universal, high-fidelity interatomic potentials.

Read the full paper here.

Publications

Enhancing Energy Density of Li3V2O5 anode

In 2020, Prof Ping Liu’s group and our group proposed a highly promising anode material – disordered rock salt (DRS) Li3V2O5 (LVO) – which had a near-ideal voltage of 0.6V vs Li/Li+ and fast Li diffusion. In this work, we show that we can further increase the energy density of this anode by Mg doping. Due to the increase in site energy due to Mg doping, the voltage of the LVO anode is reduced by a further 10%, while still retaining low Li migration barriers. Mg-doped LVO retains over 95% of its capacity over 1000 cycles at a rate of 5 C. Full cells with a LiNi0.8Co0.1Mn0.1O2 cathode demonstrate the expected increase in cell voltage and energy density while retaining 91% of their capacity over 250 cycles at 5 C.

Check out the work here.

Publications

Proton-exchange induced reactivity in NCM cathodes

Xingyu’s collaborative research with the Chen group, titled “Proton-Exchange Induced Reactivity in Layered Oxides for Lithium-Ion Batteries,” has been published in Nature Communications!

This study addresses a critical challenge in the manufacturing, storage, transportation, processing, and recycling of LiNixCoyMn1-x-yO2 (NCM, 0 < x, y < 1), the dominant cathode material for state-of-the-art lithium-ion batteries. Through an integration of experimental and computational approaches, the team reveals how protons intercalate into the layered NCM structure, triggering Li⁺ leaching and the formation of protonated NCM. This protonation process significantly disrupts the structural integrity of the material, promoting cation rearrangement and the development of impurity phases. These effects are particularly severe in NCMs with higher nickel content.

The study also demonstrates a solution-based approach to mitigate Li deficiencies in NCM materials by leveraging controlled proton concentrations and the presence of Li⁺ ions. The underlying relithiation mechanism is further elucidated through detailed materials characterization and kinetics modeling. This work provides essential insights into managing structural and compositional defects in NCM cathodes, paving the way for improved performance and stability in next-generation lithium-ion batteries.

Check out this work here.

Publications

Cation Ordering in P2 Na-Ion Cathodes

Congratulations to Zishen for his co-authored paper on “Influence of Interlayer Cation Ordering on Na Transport in P2-Type Na0.67–xLiy Ni0.33–zMn0.67+zO2 for Sodium-Ion Batteries” published in JACS together with the group of Prof Claire Xiong at Boise State University! In this work, we studied the P2-type Na2/3Ni1/3Mn2/3O2 (PNNMO) cathode for Na-ion batteries. Zishen’s contribution is showing via DFT calculations that Li doping (Na2/3Li0.05Ni1/3Mn2/3O2, LFN5) promotes ABC-type interplanar Ni/ Mn ordering without disrupting the Na+/vacancy ordering and creates low-energy Li−Mn-coordinated diffusion pathways. These result are in line with those from neutron/X-ray diffraction. Quasielastic neutron scattering reveals that the Na+ diffusivity in LFN5 is enhanced by an order of magnitude over PNNMO, increasing its capacity at a high current. These results suggest that the interlayer ordering can be tuned through the control of composition, which has an equal or greater impact on Na+ diffusion than the Na+/vacancy ordering.

Check out the work here.

Publications

Congrats to Ji Qi on the successful defense of his thesis!

Congratulations to Ji Qi for successfully defending his PhD thesis on Apr 12 2024. During his time in the Materials Virtual Lab, Ji has made extremely valuable contributions in the development and application of machine learning interatomic potentials (MLPs). He has applied MLPs to solid electrolytes, pushing the envelope of their application to extremely complex chemistries (7 element oxides!!!). He also developed an innovative DIRECT sampling method that enables the fitting of MLPs with much fewer / zero active learning steps. We wish him all the best in his new job at CATL.

Check out the recording of his PhD thesis defense below.

Publications

Healable Sulfur Cathode for Solid-State Li-S Batteries

Repaired Sulfur Cathode Interface

Manas’ final work on “Healable and conductive sulfur iodide for solid-state Li–S batteries” is now out in Nature! This work is a collaboration between Prof Ping Liu’s group and our group. Solid-state Li–S batteries (SSLSBs) are made of low-cost and abundant materials free of supply chain concerns. In this work, we report an S9.3I molecular crystal, which shows a semiconductor-level electrical conductivity. Our group’s main contribution is showing that iodine disrupts the molecular bonding in sulfur to lower its melting point, as well as introduce new states into the band gap of sulfur. This lowered melting point enables periodical remelting of the cathode to repair interfaces.

Check out this work here as well as the UCSD press release on this discovery.

Publications

DIRECT Sampling for Robust MLPs

Ji’s work on “Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling” is now out in npj Computational Materials! Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond that accessible by ab initio methods. In this work, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolates more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with the M3GNet universal potential can be used instead of expensive ab initio MD to rapidly create a large configuration space for target systems. We combined this scheme with DIRECT sampling to develop a reliable moment tensor potential for titanium hydrides without the need for iterative augmentation of training structures.

Check out this work here. If you want to use DIRECT sampling for your work, please check out our implementation available on our MAML repository on Github.