Schrödinger Research Published in Nature Reveals New Insights into Lithium-Ion Battery Chemistry

Mat Halls, Senior Vice President, Materials Science, Schrödinger

“Simulating complex battery systems has historically forced scientists to choose between quantum precision and large-scale simulation. Our machine learning force field technology eliminates that trade-off”

Mat Halls, Senior Vice President, Materials Science, Schrödinger

“Predicting these multi-lithium complexes and validating them experimentally demonstrates that our platform can detect previously inaccessible chemical phenomena to accelerate materials discovery.”

Richard Friesner, Co-founder and Member of the Board of Directors at Schrödinger, and William P. Schweitzer Professor of Chemistry at Columbia University

“Using Schrödinger’s proprietary, advanced machine learning force fields, we discovered lithium ions in batteries actually group together into clusters in the absence of anions, and that these clusters are what drive the creation of the protective film on the battery's surface”

Richard Friesner, Co-founder and Member of the Board of Directors at Schrödinger, and William P. Schweitzer Professor of Chemistry at Columbia University

“Predicting this unexpected chemistry computationally and confirming it experimentally demonstrates that Schrödinger’s physics-powered platform can accurately model the hidden atomic details of batteries. Furthermore, these results suggest that many, if not most, complex chemical systems are now amenable to predictive physics-based simulation using machine learning force fields to atomic resolution.”