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    Reference Work Entry In depth

    Machine Learning of Atomic-Scale Properties Based on Physical Principles

    We briefly summarize the kernel regression approach, as used recently in materials modeling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be ...

    Michele Ceriotti, Michael J. Willatt, Gábor Csányi in Handbook of Materials Modeling (2020)

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    Chapter

    Machine-Learning of Atomic-Scale Properties Based on Physical Principles

    We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be...

    Gábor Csányi, Michael J. Willatt… in Machine Learning Meets Quantum Physics (2020)

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    Living Reference Work Entry In depth

    Machine Learning of Atomic-Scale Properties Based on Physical Principles

    We briefly summarize the kernel regression approach, as used recently in materials modeling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be ...

    Michele Ceriotti, Michael J. Willatt, Gábor Csányi in Handbook of Materials Modeling

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    Article

    Machines learn to recognize glasses

    The dynamics of a viscous liquid undergo a dramatic slowdown when it is cooled to form a solid glass. Recognizing the structural changes across such a transition remains a major challenge. Machine-learning met...

    Michele Ceriotti, Vincenzo Vitelli in Nature Physics (2016)