How to validate machine-learned interatomic potentials von Morrow, Joe D., Gardner, John L. A., Deringer, Volker L.

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Using spectroscopy to probe relaxation, decoherence, and localization of photoexcited states in π-conjugated polymers von Barford, William, Gardner, John L. A, Mannouch, Jonathan R

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Synthetic pre-training for neural-network interatomic potentials von Gardner, John L A, Baker, Kathryn T, Deringer, Volker L

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Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks von Faure Beaulieu, Zoé, Nicholas, Thomas C., Gardner, John L. A., Goodwin, Andrew L., Deringer, Volker L.

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Synthetic data enable experiments in atomistic machine learning von Gardner, John L. A, Faure Beaulieu, Zoé, Deringer, Volker L

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Data as the next challenge in atomistic machine learning von Ben Mahmoud, Chiheb, Gardner, John L A, Deringer, Volker L

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Coarse-grained fully atomistic machine learning for zeolitic imidazolate frameworks von Faure Beaulieu, Zoé, Nicholas, Thomas C, Gardner, John L. A, Goodwin, Andrew L, Deringer, Volker L

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How to validate machine-learned interatomic potentials von Morrow, Joe D, Gardner, John L A, Deringer, Volker L

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Synthetic pre-training for neural-network interatomic potentials von Gardner, John L. A, Baker, Kathryn T, Deringer, Volker L

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Synthetic data enable experiments in atomistic machine learning von Gardner, John L. A, Beaulieu, Zoé Faure, Deringer, Volker L

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Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks von Beaulieu, Zoé Faure, Nicholas, Thomas C, Gardner, John L. A, Goodwin, Andrew L, Deringer, Volker L

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An automated framework for exploring and learning potential-energy surfaces von Liu, Yuanbin, Morrow, Joe D, Ertural, Christina, Fragapane, Natascia L, Gardner, John L. A, Naik, Aakash A, Zhou, Yuxing, George, Janine, Deringer, Volker L

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Synthetic pre-training for neural-network interatomic potentials von Gardner, John L A, Baker, Kathryn T, Deringer, Volker L

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C_Gardner_2022 von Gardner, John L. A., Beaulieu, Zoé Faure, Deringer, Volker L.

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Synthetic data enable experiments in atomistic machine learning von Gardner, John L A, Zoé Faure Beaulieu, Deringer, Volker L

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Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks von Zoé Faure Beaulieu, Nicholas, Thomas C, Gardner, John L A, Goodwin, Andrew L, Deringer, Volker L

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An automated framework for exploring and learning potential-energy surfaces von Liu, Yuanbin, Morrow, Joe D, Ertural, Christina, Fragapane, Natascia L, Gardner, John L A, Naik, Aakash A, Zhou, Yuxing, George, Janine, Deringer, Volker L

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Guest Editors' Introduction von Gardner, John L., Manchester, Paul B.

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Guest Editors' Introduction von Gardner, John L., Manchester, Paul B., Wachter, Susan M.

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The Urban Growth Question von Tolley, George S., Graves, Philip E., Gardner, John L.

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