The Experts below are selected from a list of 2694 Experts worldwide ranked by ideXlab platform

Klausrobert Muller - One of the best experts on this subject based on the ideXlab platform.

  • se 3 equivariant prediction of Molecular Wavefunctions and electronic densities
    Neural Information Processing Systems, 2021
    Co-Authors: Oliver T Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, Klausrobert Muller
    Abstract:

    Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Instead of training on a fixed set of properties, more recent approaches attempt to learn the electronic wavefunction (or density) as a central quantity of atomistic systems, from which all other observables can be derived. This is complicated by the fact that Wavefunctions transform non-trivially under Molecular rotations, which makes them a challenging prediction target. To solve this issue, we introduce general SE(3)-equivariant operations and building blocks for constructing deep learning architectures for geometric point cloud data and apply them to reconstruct Wavefunctions of atomistic systems with unprecedented accuracy. Our model reduces prediction errors by up to two orders of magnitude compared to the previous state-of-the-art and makes it possible to derive properties such as energies and forces directly from the wavefunction in an end-to-end manner. We demonstrate the potential of our approach in a transfer learning application, where a model trained on low accuracy reference Wavefunctions implicitly learns to correct for electronic many-body interactions from observables computed at a higher level of theory. Such machine-learned wavefunction surrogates pave the way towards novel semi-empirical methods, offering resolution at an electronic level while drastically decreasing computational cost. While we focus on physics applications in this contribution, the proposed equivariant framework for deep learning on point clouds is promising also beyond, say, in computer vision or graphics.

  • unifying machine learning and quantum chemistry with a deep neural network for Molecular Wavefunctions
    Nature Communications, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry. Machine learning models can accurately predict atomistic chemical properties but do not provide access to the Molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.

  • unifying machine learning and quantum chemistry with a deep neural network for Molecular Wavefunctions
    Nature Communications, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

  • unifying machine learning and quantum chemistry a deep neural network for Molecular Wavefunctions
    arXiv: Chemical Physics, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

Reinhard J Maurer - One of the best experts on this subject based on the ideXlab platform.

  • unifying machine learning and quantum chemistry with a deep neural network for Molecular Wavefunctions
    Nature Communications, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

  • unifying machine learning and quantum chemistry with a deep neural network for Molecular Wavefunctions
    Nature Communications, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry. Machine learning models can accurately predict atomistic chemical properties but do not provide access to the Molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.

  • unifying machine learning and quantum chemistry a deep neural network for Molecular Wavefunctions
    arXiv: Chemical Physics, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

Michael Gastegger - One of the best experts on this subject based on the ideXlab platform.

  • se 3 equivariant prediction of Molecular Wavefunctions and electronic densities
    Neural Information Processing Systems, 2021
    Co-Authors: Oliver T Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, Klausrobert Muller
    Abstract:

    Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Instead of training on a fixed set of properties, more recent approaches attempt to learn the electronic wavefunction (or density) as a central quantity of atomistic systems, from which all other observables can be derived. This is complicated by the fact that Wavefunctions transform non-trivially under Molecular rotations, which makes them a challenging prediction target. To solve this issue, we introduce general SE(3)-equivariant operations and building blocks for constructing deep learning architectures for geometric point cloud data and apply them to reconstruct Wavefunctions of atomistic systems with unprecedented accuracy. Our model reduces prediction errors by up to two orders of magnitude compared to the previous state-of-the-art and makes it possible to derive properties such as energies and forces directly from the wavefunction in an end-to-end manner. We demonstrate the potential of our approach in a transfer learning application, where a model trained on low accuracy reference Wavefunctions implicitly learns to correct for electronic many-body interactions from observables computed at a higher level of theory. Such machine-learned wavefunction surrogates pave the way towards novel semi-empirical methods, offering resolution at an electronic level while drastically decreasing computational cost. While we focus on physics applications in this contribution, the proposed equivariant framework for deep learning on point clouds is promising also beyond, say, in computer vision or graphics.

  • unifying machine learning and quantum chemistry with a deep neural network for Molecular Wavefunctions
    Nature Communications, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry. Machine learning models can accurately predict atomistic chemical properties but do not provide access to the Molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.

  • unifying machine learning and quantum chemistry with a deep neural network for Molecular Wavefunctions
    Nature Communications, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

  • unifying machine learning and quantum chemistry a deep neural network for Molecular Wavefunctions
    arXiv: Chemical Physics, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

Kristof T Schutt - One of the best experts on this subject based on the ideXlab platform.

  • unifying machine learning and quantum chemistry with a deep neural network for Molecular Wavefunctions
    Nature Communications, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

  • unifying machine learning and quantum chemistry with a deep neural network for Molecular Wavefunctions
    Nature Communications, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry. Machine learning models can accurately predict atomistic chemical properties but do not provide access to the Molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.

  • unifying machine learning and quantum chemistry a deep neural network for Molecular Wavefunctions
    arXiv: Chemical Physics, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

Alexandre Tkatchenko - One of the best experts on this subject based on the ideXlab platform.

  • unifying machine learning and quantum chemistry with a deep neural network for Molecular Wavefunctions
    Nature Communications, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

  • unifying machine learning and quantum chemistry with a deep neural network for Molecular Wavefunctions
    Nature Communications, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry. Machine learning models can accurately predict atomistic chemical properties but do not provide access to the Molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.

  • unifying machine learning and quantum chemistry a deep neural network for Molecular Wavefunctions
    arXiv: Chemical Physics, 2019
    Co-Authors: Kristof T Schutt, Michael Gastegger, Klausrobert Muller, Alexandre Tkatchenko, Reinhard J Maurer
    Abstract:

    Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of Molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.