Machine Learning Interatomic Potentials as Emerging Tools for Materials Science von Deringer, Volker L., Caro, Miguel A., Csányi, Gábor

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Machine learning based interatomic potential for amorphous carbon von Deringer, Volker L., Csányi, Gábor

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LOBSTER: Local orbital projections, atomic charges, and chemical‐bonding analysis from projector‐augmented‐wave‐based density‐functional theory von Nelson, Ryky, Ertural, Christina, George, Janine, Deringer, Volker L., Hautier, Geoffroy, Dronskowski, Richard

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LOBSTER: A tool to extract chemical bonding from plane-wave based DFT von Maintz, Stefan, Deringer, Volker L., Tchougréeff, Andrei L., Dronskowski, Richard

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Data-Driven Learning of Total and Local Energies in Elemental Boron von Deringer, Volker L, Pickard, Chris J, Csányi, Gábor

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A general-purpose machine-learning force field for bulk and nanostructured phosphorus von Deringer, Volker L., Caro, Miguel A., Csányi, Gábor

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Crystal Orbital Hamilton Population (COHP) Analysis As Projected from Plane-Wave Basis Sets von Deringer, Volker L, Tchougréeff, Andrei L, Dronskowski, Richard

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A Quantum‐Mechanical Map for Bonding and Properties in Solids von Raty, Jean‐Yves, Schumacher, Mathias, Golub, Pavlo, Deringer, Volker L., Gatti, Carlo, Wuttig, Matthias

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Origins of structural and electronic transitions in disordered silicon von Deringer, Volker L., Bernstein, Noam, Csányi, Gábor, Ben Mahmoud, Chiheb, Ceriotti, Michele, Wilson, Mark, Drabold, David A., Elliott, Stephen R.

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Analytic projection from plane-wave and PAW wavefunctions and application to chemical-bonding analysis in solids von Maintz, Stefan, Deringer, Volker L., Tchougréeff, Andrei L., Dronskowski, Richard

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De novo exploration and self-guided learning of potential-energy surfaces von Bernstein, Noam, Csányi, Gábor, Deringer, Volker L.

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Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning von Caro, Miguel A, Aarva, Anja, Deringer, Volker L, Csányi, Gábor, Laurila, Tomi

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Understanding X‑ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra von Aarva, Anja, Deringer, Volker L, Sainio, Sami, Laurila, Tomi, Caro, Miguel A

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Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures von Fujikake, So, Deringer, Volker L., Lee, Tae Hoon, Krynski, Marcin, Elliott, Stephen R., Csányi, Gábor

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Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics von Deringer, Volker L, Bernstein, Noam, Bartók, Albert P, Cliffe, Matthew J, Kerber, Rachel N, Marbella, Lauren E, Grey, Clare P, Elliott, Stephen R, Csányi, Gábor

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Understanding X‑ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra von Aarva, Anja, Deringer, Volker L, Sainio, Sami, Laurila, Tomi, Caro, Miguel A

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Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning von Erhard, Linus C., Rohrer, Jochen, Albe, Karsten, Deringer, Volker L.

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Microscopic Complexity in Phase-Change Materials and its Role for Applications von Deringer, Volker L., Dronskowski, Richard, Wuttig, Matthias

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Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon von Bernstein, Noam, Bhattarai, Bishal, Csányi, Gábor, Drabold, David A., Elliott, Stephen R., Deringer, Volker L.

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Extracting Crystal Chemistry from Amorphous Carbon Structures von Deringer, Volker L., Csányi, Gábor, Proserpio, Davide M.

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