Schrödinger 在《Nature》发表锂电池电解液多锂离子聚集研究，为纯科研论文成果、无任何订单或收入增量

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Schrödinger Research Published in Nature Reveals
New Insights into Lithium-Ion Battery Chemistry
2026-10-14

New study uncovers multi-lithium ion clustering in common battery electrolytes, paving the way for improved
battery performance and design

NEW YORK--(BUSINESS WIRE)-- Schrödinger, Inc. (Nasdaq: SDGR) today announced the publication of new research
in Nature that reveals critical insights into the atomic-scale structure of lithium-ion battery (LIB) electrolytes. In a
joint study with researchers from Columbia University and the New York Structural Biology Center, the team
discovered new insights that challenge long-held assumptions of the fundamental chemistry of lithium-ion
batteries, including multi-lithium ion complexes that may play an important role in electrolyte behavior and battery
function. These new mechanistic insights into electrolyte behavior have the potential to transform battery
development from empirical trial-and-error toward predictive design to high-throughput, rational, computationally-
driven design for higher-capacity, faster-charging, and longer-lasting batteries.

The research, which leveraged Schrödinger’s physics-powered computational platform, addresses a long-standing
bottleneck in energy storage design. LIB electrolytes are crucial for battery performance, in uencing properties
such as conductivity, viscosity, salt dissociation, and electrochemical stability. LIB electrolytes also drive the
formation of the solid electrolyte interphase, which is the critical protective lm on the battery anode that dictates
battery lifespan and safety. However, a detailed atomic-level understanding of how electrolyte structure impacts
these properties had remained limited. Utilizing Schrödinger’s advanced machine learning force eld technology,
the team simulated LIB electrolytes at unprecedented accuracy and scale. These simulations showed that lithium
ions frequently form clusters coordinated by ethylene carbonate solvent molecules without the presence of anions,
a fundamental structural phenomenon previously unrecognized.

“Simulating complex battery systems has historically forced scientists to choose between quantum precision and
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large-scale simulation. Our machine learning force eld technology eliminates that trade-o ,” stated Mat Halls,
senior vice president, materials science, of Schrödinger. “Predicting these multi-lithium complexes and validating
them experimentally demonstrates that our platform can detect previously inaccessible chemical phenomena to
accelerate materials discovery.”

“Using Schrödinger’s proprietary, advanced machine learning force elds, 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 lm on the battery's surface,” stated 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 con rming 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 elds to atomic resolution.”

Key Findings:

Multi-Lithium Ion Clustering: In standard battery electrolytes, up to 40% of lithium ions group together into
multi-ion clusters, with clustering becoming even more prevalent at higher salt concentrations. This nding
upends the long-held assumption that lithium ions remain predominantly isolated in solution.
Accurate Experimental Validation: The machine learning force eld simulations demonstrate strong
agreement with a wide range of condensed-phase experimental data, including NMR spectra, electrolyte
density, viscosity, ionic conductivity, and di usion coe cients, con rming the physical reality of the predicted
multi-lithium clusters.
Electrochemical Implications: Quantum chemical calculations indicate that these multi-lithium clusters have
uniquely facile reduction potentials, suggesting they are key intermediates in the formation of the solid
electrolyte interphase, which is critical for battery longevity and safety.
Foundation for Rational Electrolyte Design: The insights gained provide a fundamental understanding that can
guide the rational design of next-generation electrolytes with enhanced performance, stability, and charge
rates.

This research is ongoing and is supported by funding from Gates Ventures.

About Schrödinger
Schrödinger is transforming molecular discovery with its computational platform, which enables the discovery of
novel, highly optimized molecules for drug development and materials design. Schrödinger’s software platform is
built on more than 30 years of R&D investment and is licensed by biotechnology, pharmaceutical and industrial
companies, and academic institutions around the world. Schrödinger also leverages the platform to advance a
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portfolio of collaborative and proprietary programs. To learn more, visit www.schrodinger.com, follow us on
LinkedIn.

Cautionary Note Regarding Forward-Looking Statements
This press release contains forward-looking statements within the meaning of The Private Securities Litigation
Reform Act of 1995 including, but not limited to those statements regarding the potential advantages of
Schrödinger’s computational platform, including the capabilities and potential advantages of its machine learning
force eld technology, and the potential for computational, physics-based simulation to enable predictive design of
batteries and accelerate materials discovery. Statements including words such as “aim,” “anticipate,” “believe,”
“contemplate,” “continue,” “could,” “estimate,” “expect,” “goal,” “intend,” “may,” “might,” “plan,” “potential,” “predict,”
“project,” “should,” “target,” “will,” “would” and statements in the future tense are forward-looking statements. These
forward-looking statements re ect Schrödinger’s current views about its plans, intentions, expectations, strategies
and prospects, which are based on the information currently available to the company and on assumptions the
company has made. Actual results may di er materially from those described in these forward-looking statements
and are subject to a variety of assumptions, uncertainties, risks and important factors that are beyond
Schrödinger’s control, including the uncertainties inherent in scienti c research, its ability to further develop its
computational platform, its reliance on third-party providers of cloud-based infrastructures to host its software
solutions, and the ability to retain and hire key personnel on its business and other risks detailed under the caption
“Risk Factors” and elsewhere in the company’s Securities and Exchange Commission lings and reports, including its
Quarterly Report on Form 10-Q for the quarter ended June 30, 2026, led with the Securities and Exchange
Commission on August 5, 2026, as well as future lings and reports by the company. Any forward-looking
statements contained in this press release speak only as of the date hereof. Except as required by law, Schrödinger
undertakes no duty or obligation to update any forward-looking statements contained in this press release as a
result of new information, future events, changes in expectations or otherwise.

Jaren Madden (Investors and Media)
Schrödinger, Inc.
jaren.madden@schrodinger.com
617-286-6264

Matthew Luchini (Investors)
Schrödinger, Inc.
matthew.luchini@schrodinger.com
917-719-0636

Theresa Masnik (Media)
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SHIFT Communications
schrodingerpr@shiftcomm.com
212-229-1037

Source: Schrödinger




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