Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias von Kovács, Dávid Péter, McCorkindale, William, Lee, Alpha A.

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Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE von Kovács, Dávid Péter, Oord, Cas van der, Kucera, Jiri, Allen, Alice E. A, Cole, Daniel J, Ortner, Christoph, Csányi, Gábor

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First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects von Kapil, Venkat, Kovács, Dávid Péter, Csányi, Gábor, Michaelides, Angelos

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Hyperactive learning for data-driven interatomic potentials von van der Oord, Cas, Sachs, Matthias, Kovács, Dávid Péter, Ortner, Christoph, Csányi, Gábor

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Evaluation of the MACE force field architecture: From medicinal chemistry to materials science von Kovács, Dávid Péter, Batatia, Ilyes, Arany, Eszter Sára, Csányi, Gábor

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First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects von Kapil, Venkat, Kovács, Dávid Péter, Csányi, Gábor, Michaelides, Angelos

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First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects von Kapil, Venkat, Kovács, Dávid Péter, Csányi, Gábor, Michaelides, Angelos

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Evaluation of the MACE force field architecture: From medicinal chemistry to materials science von Kovács, Dávid Péter, Batatia, Ilyes, Arany, Eszter Sára, Csányi, Gábor

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Zero Shot Molecular Generation via Similarity Kernels von Elijošius, Rokas, Zills, Fabian, Batatia, Ilyes, Norwood, Sam Walton, Kovács, Dávid Péter, Holm, Christian, Csányi, Gábor

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The design space of E(3)-equivariant atom-centred interatomic potentials von Batatia, Ilyes, Batzner, Simon, Kovács, Dávid Péter, Musaelian, Albert, Simm, Gregor N. C, Drautz, Ralf, Ortner, Christoph, Kozinsky, Boris, Csányi, Gábor

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Hyperactive learning for data-driven interatomic potentials von van der Oord, Cas, Sachs, Matthias, Kovács, Dávid Péter, Ortner, Christoph, Csányi, Gábor

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Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias von Kovács, Dávid Péter, McCorkindale, William, Lee, Alpha A

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Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias von Kovács, Dávid Péter, McCorkindale, William, Lee, Alpha A

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Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials von van der Oord, Cas, Sachs, Matthias, Kovács, Dávid Péter, Ortner, Christoph, Csányi, Gábor

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MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields von Batatia, Ilyes, Kovács, Dávid Péter, Simm, Gregor N. C, Ortner, Christoph, Csányi, Gábor

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The design space of E(3)-equivariant atom-centred interatomic potentials von Batatia, Ilyes, Batzner, Simon, Kovács, Dávid Péter, Musaelian, Albert, Simm, Gregor N. C., Drautz, Ralf, Ortner, Christoph, Kozinsky, Boris, Csányi, Gábor

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MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules von Kovács, Dávid Péter, Moore, J. Harry, Browning, Nicholas J, Batatia, Ilyes, Horton, Joshua T, Pu, Yixuan, Kapil, Venkat, Witt, William C, Magdău, Ioan-Bogdan, Cole, Daniel J, Csányi, Gábor

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The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials von Batatia, Ilyes, Batzner, Simon, Kovács, Dávid Péter, Musaelian, Albert, Simm, Gregor N. C, Drautz, Ralf, Ortner, Christoph, Kozinsky, Boris, Csányi, Gábor

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Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE von Kovács, Dávid Péter, Oord, Cas van der, Kucera, Jiri, Allen, Alice EA, Cole, Daniel J, Ortner, Christoph, Csányi, Gábor

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Zero Shot Molecular Generation via Similarity Kernels von Rokas Elijošius, Zills, Fabian, Ilyes Batatia, Sam Walton Norwood, Dávid Péter Kovács, Holm, Christian, Csányi, Gábor

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