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RESEARCH PAPER · Physical Review Letters · 2025

Augmentation of Universal Potentials for Broad Applications

Joe Pitfield, Florian Brix, Zeyuan Tang, Andreas Møller Slavensky, Nikolaj Rønne, Mads-Peter Verner Christiansen, Bjørk Hammer

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16 direct citing papers in this saved network. Explore the next layer with second-order citations.

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Augmentation of Universal Potentials for Broad ApplicationsOriginalΔ-model correction of foundation model based on the model’s own understanding8Instability of A B O 3 perovskite surfaces induced by vacancy formation ( A = Ca , Sr , Ba ; B = Ti , Zr , Sn )4Toward data-driven predictive modeling of electrocatalyst stability and surface reconstruction24RAFFLE: active learning accelerated interface structure prediction3Cross-functional transferability in foundation machine learning interatomic potentials12Active Δ-learning with universal potentials for global structure optimization2Simulating H$^+$, O$^{2-}$ and Sr$^{2+}$ transport in SrTiO$_3$ with a machine-learning potential1An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials8Explicit atomistic modelling of solid–solid interfaces: from construction to design0Nanoscale doping and mixing of binary oxide thin films: concepts, properties and applications2Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models30Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models2Iterative learning scheme for crystal structure prediction with anharmonic lattice dynamics1Mechanistic study of mixed lithium halides solid-state electrolytes1Theoretical insights into intermetallic compound surfaces in hydrogen-involving catalytic reactions0Automatic Generation of Inorganic Solid‐State Electrolytes via Unified Multi‐Modal Network0

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  1. Δ-model correction of foundation model based on the model’s own understanding

    The Journal of Chemical Physics · 2025-05-08

    Mads-Peter Verner Christiansen, Bjørk Hammer

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  2. Instability of A B O 3 perovskite surfaces induced by vacancy formation ( A = Ca , Sr , Ba ; B = Ti , Zr , Sn )

    Physical review. B./Physical review. B · 2025-06-04

    Ned Thaddeus Taylor, Michael Thomas Morgan, Steven Paul Hepplestone

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  3. Toward data-driven predictive modeling of electrocatalyst stability and surface reconstruction

    The Journal of Chemical Physics · 2025-07-28

    Jiayu Peng

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  4. RAFFLE: active learning accelerated interface structure prediction

    npj Computational Materials · 2025-08-08

    Ned Thaddeus Taylor, Joe Pitfield, Francis Huw Davies, Steven Paul Hepplestone

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  5. Cross-functional transferability in foundation machine learning interatomic potentials

    npj Computational Materials · 2025-10-21

    Xu Huang, Bowen Deng, Peichen Zhong, Aaron D. Kaplan, Kristin Aslaug Persson, Gerbrand Ceder

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  6. Active Δ-learning with universal potentials for global structure optimization

    Physical Chemistry Chemical Physics · 2025-12-01

    Joe Pitfield, Mads-Peter Verner Christiansen, Bjørk Hammer

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  7. Simulating H$$^+$$, O$$^{2-}$$ and Sr$$^{2+}$$ transport in SrTiO$$_3$$ with a machine-learning potential

    Journal of Materials Science Materials in Energy · 2025-12-16

    Caitlin Perkampus, Stefan N. Adams, Roger A. De Souza

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  8. An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials

    npj Computational Materials · 2025-12-17

    Jisu Kim, Jiho Lee, Sangmin Oh, Yutack Park, Seungwoo Hwang, Seungwu Han, Sungwoo Kang, Youngho Kang

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  9. Explicit atomistic modelling of solid–solid interfaces: from construction to design

    Chemical Communications · 2026-01-01

    Anastasia K. Lavrinenko, James A. Quirk, James Alexander Dawson, Marnix Wagemaker, Alexandros Vasileiadis

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  10. Nanoscale doping and mixing of binary oxide thin films: concepts, properties and applications

    Journal of Physics Condensed Matter · 2026-01-23

    Claudine Noguera, Niklas Nilius, Jacek Goniakowski

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  11. Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models

    Angewandte Chemie International Edition · 2026-02-17

    Di Zhang, Yuanzheng Chen, Chuanyu Liu, Y. H. Liu, Hongliang Xin, Jiayu Peng, Pengfei Ou, H. C. Li

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  12. Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models

    Angewandte Chemie · 2026-02-24

    Di Zhang, Yuanzheng Chen, Chuanyu Liu, Yan Liu, Hongliang Xin, Jiayu Peng, Pengfei Ou, H. C. Li

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  13. Iterative learning scheme for crystal structure prediction with anharmonic lattice dynamics

    Physical review. B./Physical review. B · 2026-03-30

    Hao Gao, Yue‐Wen Fang, Ion Errea

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  14. Mechanistic study of mixed lithium halides solid-state electrolytes

    Physical Review Materials · 2026-08-26

    Davide Tisi, Sergey Pozdnyakov, Michele Ceriotti

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  15. Theoretical insights into intermetallic compound surfaces in hydrogen-involving catalytic reactions

    Surface Science Reports · 2026-09-01

    Arshak A. Tsaturyan, Florian Brix, Dmytro Kandaskalov, Émilie Gaudry

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  16. Automatic Generation of Inorganic Solid‐State Electrolytes via Unified Multi‐Modal Network

    Small · 2026-10-08

    Yihong Liu, Jinjiang Liang, Zheng Wang, Mohamed Ait Tamerd, Jielun Huang, Renyuan Zhang, Wei Liu, Menghao Yang

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