248th ECS Meeting
Date & Time - October 12th-16th, 2025
Location - Chicago, Illinois
Schrödinger is excited to be participating in the 248th ECS Meeting taking place on October 12th – 16th in Chicago, Illinois. Join us for presentations by Schrödinger scientists.
OCT 14 | 11:00AM Schrödinger’s Atomistic Simulation Workflow to Model Solid Electrolyte Interphase in Lithium-Ion Batteries
Speaker:
Manav Bhati, Senior Scientist II, Materials Science Modeling Services, Schrödinger
Abstract:
Lithium-ion batteries (LiBs) are ubiquitous, powering applications from portable electronics to electric vehicles. Atomic-level computational simulations play a critical role in exploring and optimizing battery materials. This work expands the application of our physics-based simulation workflow to model the solid electrolyte interphase (SEI), which is a crucial yet poorly understood component of batteries. Our approach utilizes a reaction-template-based method with the OPLS4 force field and a high-speed GPU-based molecular dynamics engine (Desmond) within Schrödinger’s Materials Science suite to simulate SEI nucleation and growth. The SEI simulator provides detailed atomistic insights into SEI morphology and product distribution. In particular, we investigate how changing the chemistry of electrolytes affects SEI composition and properties.
Lithium hexafluorophosphate in ethylene carbonate (LiPF6/EC) is a widely used Li-ion battery electrolyte. Our atomistic simulations of 1 M LiPF6/EC on a graphite electrode closely match experiments, revealing a thin inorganic layer (Li2CO3, LiF) near the electrode, a porous organic layer (Li2EDC, Li2BDC), and gaseous species (C2H4, PF3) diffusing away. Comparing different electrolyte chemistries, we find that 1 M LiPF6/EC forms a denser, more compact SEI than 1 M LiPF6/PC, suggesting superior mechanical stability and explaining EC’s dominance in commercial batteries. Adding ethyl methyl carbonate (EMC, a common linear cosolvent) to EC further enhances SEI density, particularly in the inorganic layer, leading to reduced electrolyte degradation, lower irreversible losses, and improved mechanical stability, ultimately boosting battery performance.
Schrödinger’s SEI simulation workflow enables modeling across diverse electrolyte chemistries (from cyclic to linear electrolyte solvents and mixtures), offering comprehensive atomistic insights that accelerate the development of optimized materials for next-generation batteries.
OCT 15 | 12:20PM Scalable and Generalizable Machine Learning Force Fields for Modeling Complex Battery Materials
Speaker:
Garvit Agarwal, Principal Scientist I, Materials Science Applications Science
Abstract:
The rapid advancements in rechargeable Li-ion battery (LIB) technology has revolutionized several key industries such as automotive and consumer electronics. However, new battery chemistries are needed to improve the power density, safety, reliability, and lifetime of LIBs. Existing classical force fields are not accurate enough to predict bulk properties of LIB materials without time-consuming and customized parametrization. To move towards accurate and reliable modeling of battery chemistries, we developed a machine-learned force field (MLFF) using a charge recursive neural network (QRNN) architecture, which includes both long-range interactions and global charge redistribution. The MLFF is trained to model a large chemical space of industrially relevant liquid electrolyte chemistries and enables large scale molecular dynamics (MD) simulations of realistic electrolyte formulations. In this presentation, I will demonstrate our generalized active learning framework and sampling workflow used to generate accurate training data for liquid and amorphous systems. I will discuss large scale benchmarks carried out to evaluate the performance of MLFF against experimental data for key physical and transport properties of liquid electrolytes including density, viscosity and ionic conductivity. Our results indicate that MLFF outperforms the classical force fields in terms of quantitative agreement with experimental data across a broad range of electrolyte chemistries. I will also discuss the novel molecular level insights into the unique Li+ cation solvation structures predicted by MLFF and their validation using experimental nuclear magnetic resonance (NMR) spectroscopy. Finally, I will briefly discuss our recent work to develop a message passing neural network architecture to train universal MLFF for inorganic materials covering up to 94 elements from the periodic table allowing for efficient design of materials for cathodes, coatings and and solid-state electrolytes for applications in next-generation batteries.