Anatole von Lilienfeld

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The accurate prediction of molecular energetics in chemical compound space is a crucial ingredient for rational compound design. The inherently graph-like, non-vectorial nature of molecular data gives rise to a unique and difficult machine learning problem. In this paper, we adopt a learning-from-scratch approach where quantum-mechanical molecular energies(More)
Atomization energies are an important measure of chemical stability. Machine learning is used to model atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only [1]. Our scheme maps the problem of solving the molecular time-independent Schrödinger equation onto a non-linear statistical regression problem.(More)
alchemical derivatives Yasmine S. Al-Hamdani, 2 Angelos Michaelides, 3 and O. Anatole von Lilienfeld a) Thomas Young Centre and London Centre for Nanotechnology, 17–19 Gordon Street, London, WC1H 0AH, U.K. Department of Chemistry, University College London, 20 Gordon Street, London, WC1H 0AJ, U.K. Department of Physics and Astronomy, University College(More)
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