Paesani Lab

Working at the intersection of quantum chemistry, statistical mechanics, and computer/data science at @UCSanDiego. Account managed by all group members.

[Paesani Lab @PaesaniLab]

🚨 Our Perspective on single-ion hydration is out today in @JPhysChem #Letters!

👉 https://t.co/Tdjj7sBJJu

We bring together what we’ve learned with our #MBpol and #MBnrg #datadriven #manybody potentials, following ions from small water clusters 🫧 to bulk solutions and the air/water interface. 🌊

The common thread is how short-range #machinelearned n-body terms trained on CCSD(T) data work together with the physics of electrostatics, polarization, and dispersion to predict hydration structure, spectra, and thermodynamics 🖥️. All without training our #datadriven #manybody potentials to solution-phase properties! 😎

Interested in performing #datadriven #manybody simulations? Check out #MBX:
✅ https://t.co/KIcuP5nz3k
✅ https://t.co/honGcUtnHo
✅ https://t.co/90EStV7LEk

Congrats to the team, and a special shout-out to Olivia, who contributed to this work while still in high school and is now an undergrad at @Caltech! 🙌

Big thanks to @NSF for supporting this research and to @ACCESSforCI for providing computational resources! 🙏

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

[Paesani Lab @PaesaniLab]

What an exciting day at the 9th #SoCalTheoChem Symposium at @SDSU! 🎉

So proud of our undergrads and grad students who presented six posters to the SoCal theoretical chemistry community, sharing research spanning ion transport, biomolecular simulations, hydration, vibrational spectroscopy, and faster many-body potentials! 🖥️

Huge thanks to the organizers for bringing everyone together for a great day of #compchem research and lively discussion!

@UCSanDiego @UCSDPhySci @UCSDChemBiochem @HDSIUCSD @SDSC_UCSD

[Paesani Lab @PaesaniLab]

Thanks for the question! We believe the MB-nrg framework provides a robust basis for simulations in both the gas phase and solution. We already reported a proof of concept last year for alanine dipeptide in explicit MB-pol water:
👉 https://t.co/Oq04g4xrL5

The fourth paper in this series will focus on peptides in solution. This is the first iteration of the amino acid library, and we’ll continue refining and expanding the potentials as we develop additional n-body training sets. More to come!

For more on MB-pol water:
https://t.co/B3ZMqBhExm
https://t.co/1sv7UuRWY3

[Paesani Lab @PaesaniLab]

🧩 New building blocks for peptide simulations!

👉 https://t.co/S9JDltfYIl

Our latest @ChemRxiv preprint introduces an MB-nrg library of #datadriven #manybody potentials for all 20 standard amino acids, a step toward CCSD(T)-level simulations of peptides!

We combine #machinelearned n-body terms trained on DLPNO-CCSD(T) fragment data with physics-based electrostatics, polarization, and dispersion. These reusable building blocks let us assemble potentials for complete dipeptides without using whole-dipeptide energies for training. 😎

This is the first iteration of the potentials. We’ll keep improving and expanding them as we develop larger training sets of n-body interactions, including additional nonbonded and higher-body terms. 🏄‍♀️

Great work by Xinmao, Ethan, and Jiwon! 🙌

Interested in performing #datadriven #manybody simulations? Check out #MBX: ✅ https://t.co/bVIx3HHZn3 ✅ https://t.co/eY2zrSvDUF ✅https://t.co/EFiOa7qVMJ

Big thanks to @AFOSR for supporting the development of these MB-nrg potentials for amino acids and to @NSF for supporting their implementation in #MBX!

We are also grateful to #HPCMP and @ACCESSforCI for providing computational resources! 🙏

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

[Paesani Lab @PaesaniLab]

And here’s some of the science Richa leaves us with! 🌊🧊⚡️

🔹 Many-body physics in machine-learned water models ➡️ https://t.co/fuh9RTzdiD

🔹 A molecular view of the ice surface ➡️ https://t.co/LzqB8DyNBQ

🔹 Water next to neutral and charged graphene ➡️ https://t.co/vXOr0eqIj9

🔹 How dissolved halides affect surrounding water ➡️ https://t.co/mGOCHZdcYH

🔹 Decoding the vibrational spectrum of hydrated fluoride ➡️ https://t.co/AmLQqRG01g

🔹 Water inside polarizable carbon nanotubes ➡️ https://t.co/PJAp1yeRNo

🔹 Separating surface and bulk responses at graphene oxide interfaces ➡️ https://t.co/bDIR8JLEIH

🔹 Why free OH groups at the water surface aren’t all alike ➡️ https://t.co/iwb0FLYmju

🔹 Our review connecting many-body potentials to aqueous properties ➡️ https://t.co/IRvcNwCRiu

🔹 Following the hydration of protonated glycine ➡️ https://t.co/wAPi2xfP2T

🔹 Explaining where halides reside near the water surface ➡️ https://t.co/UTpsEfg6LN

Thank you, Richa! 🙌

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD @RichaRashm

[Paesani Lab @PaesaniLab]

Today was Richa’s last day in the group. We knew it was coming, but saying goodbye still isn’t easy! 🥹

Richa, thank you for all the wonderful science, for mentoring our undergrads, and for being so generous with your time and ideas. So much to celebrate and so many good memories over these past five years! 🫶

We’re going to miss you! Good luck with your postdoc at @Columbia! We’re excited to see what you do next! Stay in touch and come back to visit! ❤️

#OnceAPirateAlwaysAPirate 🏴‍☠️

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD @RichaRashm

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[Paesani Lab @PaesaniLab]

Our undergrads did it again! 🤯

Today, four more of our fantastic students shared their summer research on testing and improving machine-learned molecular models 💻, ion hydration 🌊, and how antifreeze proteins interact with ice. 🧊

Seven presentations over two Fridays and so much to be proud of! 🙌

A huge thank you to @NSF for supporting our undergraduate summer research program! 🙏

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

[Paesani Lab @PaesaniLab]

Our undergrads blew us away today! 🤯

Thanks to @NSF funding, we were able to support our fantastic undergrads in doing research this summer. Today, Jake, Yoyo, and Kaiyo kicked off our summer research presentations with work on #enzyme design 🧬, #MOFs as quasi-solid electrolytes 🔋, and #GPU implementation of our #datadriven #manybody potentials 🖥️.

More presentations next Friday... Stay tuned! 👀

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

[UCSD - Department of Chemistry @UCSDChem]

Huge congratulations, Yitzhak! 🎉🥳Read more: https://t.co/wSWIhGWFoh

[UCSD - Department of Chemistry @UCSDChem]

Congratulations to Roxanne!! 🎉🪩

[Paesani Lab @PaesaniLab]

Exciting news! 🎉

Our paper on #halides at the air/water interface is featured in @NatureComms Editors’ Highlights in inorganic and physical chemistry! 🌊

Congratulations to the whole team! 🙌

👉 https://t.co/1oXzYkDsVD

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

[Paesani Lab @PaesaniLab]

Our undergrads blew us away today! 🤯

Thanks to @NSF funding, we were able to support our fantastic undergrads in doing research this summer. Today, Jake, Yoyo, and Kaiyo kicked off our summer research presentations with work on #enzyme design 🧬, #MOFs as quasi-solid electrolytes 🔋, and #GPU implementation of our #datadriven #manybody potentials 🖥️.

More presentations next Friday... Stay tuned! 👀

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

[Paesani Lab @PaesaniLab]

���� Now out in its final form in @NatureComms, with volume and article number! 🏄‍♀️

👉 https://t.co/tfnUUtLmL2

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

[Paesani Lab @PaesaniLab]

🚨 New paper in @NatureComms! 🚨

Do halide ions adsorb at the air/water interface, or do they prefer to remain in bulk water? 🌊

This deceptively simple question has been debated for more than a century, with classical theories, continuum models, molecular simulations, and experiments often producing very different pictures. 🤔

Our new paper is now online:
👉 https://t.co/gcjT3Wp3gS

Vibrational sum-frequency generation (#vSFG) studies have added to the controversy, leading to opposing molecular interpretations, from extreme enrichment of the heavier halides at the surface to a stratified interface with an ion-depleted topmost layer [J. Phys. Chem. B 108, 2252 (2004); J. Phys. Chem. B 108, 5051 (2004); Nat. Commun. 5, 4083 (2014); Nat. Chem. 16, 644 (2024)]. ⚡️

Because vSFG probes atomic ions only indirectly through the response of interfacial water, translating the spectra into molecular structure depends critically on computational models whose accuracy and predictive power across different environments and properties are not always independently established.

We address this problem using MB-pol for water and a controlled hierarchy of ion–water models built on increasingly complete and physically correct approximations to the many-body expansion. 🖥️

By keeping the description of water unchanged and systematically improving only the ion–water interactions, we reconstruct the major pictures proposed over the past century and identify the physical approximations behind each one:
1️⃣ Purely pairwise-additive (2B)-MB-nrg potentials recover the generic ion exclusion predicted by the image-charge theories of Wagner [Phys. Z. 25, 474 (1924)] and Onsager–Samaras [J. Chem. Phys. 2, 528 (1934)].
2️⃣ The classical polarizable TTM-nrg potentials recover the strong adsorption of the heavier halides predicted by earlier polarizable models, including those of Jungwirth and Tobias [J. Phys. Chem. B 106, 6361 (2002)].
3️⃣ Adding an accurate short-range 2-body ion–water interaction to classical many-body polarization, as in (2B+NB)-MB-nrg, recovers the more moderate qualitative picture of Yan Levin’s extended continuum theory, which combines ionic polarizability with cavitation and interfacial solvation [Phys. Rev. Lett. 102,147803 (2009); Phys. Rev. Lett. 103, 257802 (2009)].
4️⃣ With the full (2B+3B+NB)-MB-nrg #datadriven #manybody potentials, strong adsorption disappears: F⁻, Cl⁻, and Br⁻ remain bulk-favored, while I⁻ retains only a shallow interfacial minimum of order kT.

The resulting picture supports neither extreme surface enrichment nor a completely ion-depleted topmost layer. Instead, only iodide displays a weak intrinsic surface preference in the dilute single-ion limit.

What we found is that short-range quantum-mechanical many-body effects beyond classical polarization are decisive.

Why is iodide different? Not because its direct interaction with water becomes more favorable at the surface. Instead, a localized reorganization of interfacial water offsets part of the ion–water desolvation penalty. Entropy opposes this energetic stabilization, leaving only a weak surface preference.

At a time when “physics-aware” molecular models are receiving so much attention, this paper also highlights a philosophy that has guided our work for more than a decade. Our MB-pol and MB-nrg #datadriven #manybody potentials combine physically correct long-range interactions with data-driven representations of short-range quantum mechanics trained on CCSD(T) data.

This paper also completes a major chapter in our work on single-ion hydration. After MB-pol provided a realistic and quantitative description of water from gas-phase clusters to liquid water and ice, MB-nrg extended the same framework to hydrated single ions, connecting ion–water dimers and clusters with structural, thermodynamic, dynamical, and spectroscopic properties in bulk solutions and now at aqueous interfaces. 😎

Huge congratulations to Henry, who led this project, and to Richa (@RichaRashm), Xuanyu (@philipzxy), and Saswata (@quantum_winger) for their contributions! 🎉

The next challenge is to develop a quantitative description of finite-concentration electrolyte solutions, where counterions, ion–ion correlations, ion pairing, and concentration-dependent restructuring take us beyond the Debye–Hückel theory.

We believe our #datadriven #manybody potentials can get us there. Stay tuned! 🏄‍♀️

Interested in performing #datadriven #manybody simulations? Check out #MBX:
✅ https://t.co/bVIx3HIxcB
✅ https://t.co/eY2zrSwbKd
✅https://t.co/EFiOa7rtCh

Big thanks also to @NSF for supporting this research and to @ACCESSforCI for providing computational resources! 🙏

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

[Paesani Lab @PaesaniLab]

Huge congratulations to the newly minted Dr. Zhu, who successfully defended his #PhD thesis today! 🎓

Xuanyu first joined our lab as a physics undergraduate and stayed for his #PhD, working on several theoretical aspects of our #datadriven #manybody potentials. In particular, he has focused on understanding how to make #machinelearning models accurate, robust, and transferable, all at once. 😎

A few highlights from his work:
��� MB-pol(2023): improving accuracy from water clusters to liquid water
https://t.co/jMx8Fbkzwg
🔹 Fourier series for accurate and robust many-body potentials
https://t.co/CS4dnXNg6I
🔹 Exploring plastic ice VII under high pressure
https://t.co/kdewfUGoIE

We are so proud of him and look forward to seeing where his creativity and intuition will take him! 🚀

#OnceAPirateAlwaysAPirate 🏴‍☠️

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

[Paesani Lab @PaesaniLab]

🚨 Our mini review on single-ion hydration is now on @ChemRxiv!

👉 https://t.co/cNQZoZN00C

We summarize what we have learned with our #datadriven #manybody potentials, following ions from small water clusters to bulk solutions and interfaces. 🌊

A central theme is how these predictions emerge from the interplay between short-range #machinelearned n-body terms trained on CCSD(T) data and #physics-based representations of electrostatics, polarization, and dispersion. 🏄‍♀️

Interested in performing #datadriven #manybody simulations? Check out #MBX:
✅ https://t.co/bVIx3HIxcB
✅ https://t.co/eY2zrSwbKd
✅ https://t.co/EFiOa7rtCh

Big thanks to @NSF for supporting the development of #datadriven #manybody potentials and #MBX and to
@ACCESSforCI for providing computational resources! 🙏

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

[Paesani Lab @PaesaniLab]

Greetings from the 2026 @NSF CSSI/CyberTraining/SCIPE PI Meeting in Rockville, Maryland! 👋

Francesco presented our latest work on #MBX, the open-source software behind our #datadriven #manybody simulations.

By combining #machinelearned low-order n-body terms trained on CCSD(T) data with #physics-based representations of many-body effects, our #datadriven #manybody potentials transfer chemical accuracy across phases. 🏄‍♀️

Interested in running your own #datadriven #manybody simulations? Check out #MBX:
✅ https://t.co/bVIx3HIxcB
✅ https://t.co/eY2zrSwbKd
✅ https://t.co/EFiOa7rtCh

Big thanks to @NSF for supporting our research on #MBX and #datadriven #manybody simulations, and to the organizers, program officers, and all the presenters for a great meeting! 🙌

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

[Paesani Lab @PaesaniLab]

Reproducing experimental measurements is rewarding. Seeing experiments confirm something we predicted a year earlier is even more exciting! 🎯

A recent hyper-Raman study in @JPhysChem Letters found that fluoride shifts #water’s librational band to higher frequencies, reflecting more strongly hindered rotations of nearby #water molecules. Chloride and bromide shift it in the opposite direction. ⚡️
👉 https://t.co/RlKxdbd7HW

The study confirms our 2025 predictions! 😎
👉 https://t.co/AmLQqRG01g

These predictions emerged from our #datadriven #manybody description of molecular interactions, without fitting to these experimental spectra. 🖥️

By combining #machinelearned n-body terms trained on high-level quantum chemistry data with #physics-based representations of many-body effects, our #datadriven #manybody potentials transfer chemical accuracy from molecular clusters to condensed phases, enabling robust predictions that experiments can test. 🔬

Interested in performing #datadriven #manybody simulations? Check out #MBX:
✅ https://t.co/bVIx3HIxcB
✅ https://t.co/eY2zrSwbKd
✅ https://t.co/EFiOa7rtCh

Big thanks also to @NSF for supporting our research on #datadriven #manybody potentials and to @ACCESSforCI for providing computational resources! 🙏

Stay tuned for more predictions! 🏄‍♀️

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD