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

  • Exploring Chemical Compound space with quantum-based machine learning
    Nature Reviews Chemistry, 2020
    Co-Authors: O. Anatole Lilienfeld, Klaus-robert Müller, Alexandre Tkatchenko
    Abstract:

    Machine-learning techniques have enabled, among many other applications, the exploration of molecular properties throughout Chemical space. The specific development of quantum-based approaches in machine learning can now help us unravel new Chemical insights. Rational design of Compounds with specific properties requires understanding and fast evaluation of molecular properties throughout Chemical Compound space — the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of Chemical Compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse Compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of Chemical Compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and Chemical knowledge.

  • exploring Chemical Compound space with quantum based machine learning
    arXiv: Chemical Physics, 2019
    Co-Authors: Anatole O Von Lilienfeld, Klaus-robert Müller, Alexandre Tkatchenko
    Abstract:

    Rational design of Compounds with specific properties requires conceptual understanding and fast evaluation of molecular properties throughout Chemical Compound space (CCS) -- the huge set of all potentially stable molecules. Recent advances in combining quantum mechanical (QM) calculations with machine learning (ML) provide powerful tools for exploring wide swaths of CCS. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of QM-based ML methods to diverse Compounds and properties and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of CCS can be made through a systematic combination of rigorous physical theories, comprehensive synthetic datasets of microscopic and macroscopic properties, and modern ML methods that account for physical and Chemical knowledge.

  • machine learning of molecular electronic properties in Chemical Compound space
    New Journal of Physics, 2013
    Co-Authors: Gregoire Montavon, Klaus-robert Müller, Alexandre Tkatchenko, Matthias Rupp, Vivekanand V Gobre, Alvaro Vazquezmayagoitia, Katja Hansen, Anatole O Von Lilienfeld
    Abstract:

    The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure?property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual Compounds that are synthetically accessible. Here, we present a machine learning model, trained on a database of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity and excitation energies. The machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, i.e.?nuclear charges and Cartesian coordinates of all atoms. For small organic molecules, the accuracy of such a ?quantum machine? is similar, and sometimes superior, to modern quantum-Chemical methods?at negligible computational cost.

Klaus-robert Müller - One of the best experts on this subject based on the ideXlab platform.

  • Exploring Chemical Compound space with quantum-based machine learning
    Nature Reviews Chemistry, 2020
    Co-Authors: O. Anatole Lilienfeld, Klaus-robert Müller, Alexandre Tkatchenko
    Abstract:

    Machine-learning techniques have enabled, among many other applications, the exploration of molecular properties throughout Chemical space. The specific development of quantum-based approaches in machine learning can now help us unravel new Chemical insights. Rational design of Compounds with specific properties requires understanding and fast evaluation of molecular properties throughout Chemical Compound space — the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of Chemical Compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse Compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of Chemical Compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and Chemical knowledge.

  • exploring Chemical Compound space with quantum based machine learning
    arXiv: Chemical Physics, 2019
    Co-Authors: Anatole O Von Lilienfeld, Klaus-robert Müller, Alexandre Tkatchenko
    Abstract:

    Rational design of Compounds with specific properties requires conceptual understanding and fast evaluation of molecular properties throughout Chemical Compound space (CCS) -- the huge set of all potentially stable molecules. Recent advances in combining quantum mechanical (QM) calculations with machine learning (ML) provide powerful tools for exploring wide swaths of CCS. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of QM-based ML methods to diverse Compounds and properties and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of CCS can be made through a systematic combination of rigorous physical theories, comprehensive synthetic datasets of microscopic and macroscopic properties, and modern ML methods that account for physical and Chemical knowledge.

  • machine learning of molecular electronic properties in Chemical Compound space
    New Journal of Physics, 2013
    Co-Authors: Gregoire Montavon, Klaus-robert Müller, Alexandre Tkatchenko, Matthias Rupp, Vivekanand V Gobre, Alvaro Vazquezmayagoitia, Katja Hansen, Anatole O Von Lilienfeld
    Abstract:

    The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure?property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual Compounds that are synthetically accessible. Here, we present a machine learning model, trained on a database of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity and excitation energies. The machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, i.e.?nuclear charges and Cartesian coordinates of all atoms. For small organic molecules, the accuracy of such a ?quantum machine? is similar, and sometimes superior, to modern quantum-Chemical methods?at negligible computational cost.

Anatole O Von Lilienfeld - One of the best experts on this subject based on the ideXlab platform.

  • machine learning of molecular electronic properties in Chemical Compound space
    New Journal of Physics, 2013
    Co-Authors: Gregoire Montavon, Klaus-robert Müller, Alexandre Tkatchenko, Matthias Rupp, Vivekanand V Gobre, Alvaro Vazquezmayagoitia, Katja Hansen, Anatole O Von Lilienfeld
    Abstract:

    The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure?property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual Compounds that are synthetically accessible. Here, we present a machine learning model, trained on a database of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity and excitation energies. The machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, i.e.?nuclear charges and Cartesian coordinates of all atoms. For small organic molecules, the accuracy of such a ?quantum machine? is similar, and sometimes superior, to modern quantum-Chemical methods?at negligible computational cost.

Anatole O Von Lilienfeld - One of the best experts on this subject based on the ideXlab platform.

  • exploring Chemical Compound space with quantum based machine learning
    arXiv: Chemical Physics, 2019
    Co-Authors: Anatole O Von Lilienfeld, Klaus-robert Müller, Alexandre Tkatchenko
    Abstract:

    Rational design of Compounds with specific properties requires conceptual understanding and fast evaluation of molecular properties throughout Chemical Compound space (CCS) -- the huge set of all potentially stable molecules. Recent advances in combining quantum mechanical (QM) calculations with machine learning (ML) provide powerful tools for exploring wide swaths of CCS. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of QM-based ML methods to diverse Compounds and properties and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of CCS can be made through a systematic combination of rigorous physical theories, comprehensive synthetic datasets of microscopic and macroscopic properties, and modern ML methods that account for physical and Chemical knowledge.

  • quantum machine learning in Chemical Compound space
    Angewandte Chemie, 2018
    Co-Authors: Anatole O Von Lilienfeld
    Abstract:

    Rather than numerically solving the computationally demanding equations of quantum or statistical mechanics, machine learning methods can infer approximate solutions, interpolating previously acquired property data sets of molecules and materials. The case is made for quantum machine learning: An inductive molecular modeling approach which can be applied to quantum chemistry problems.

  • first principles view on Chemical Compound space gaining rigorous atomistic control of molecular properties
    International Journal of Quantum Chemistry, 2013
    Co-Authors: Anatole O Von Lilienfeld
    Abstract:

    A well-defined notion of Chemical Compound space (CCS) is essential for gaining rigorous control of properties through variation of elemental composition and atomic configurations. Here, we give an introduction to an atomistic first principles perspective on CCS. First, CCS is discussed in terms of variational nuclear charges in the context of conceptual density functional and molecular grand-canonical ensemble theory. Thereafter, we revisit the notion of Compound pairs, related to each other via “alChemical” interpolations involving fractional nuclear charges in the electronic Hamiltonian. We address Taylor expansions in CCS, property nonlinearity, improved predictions using reference Compound pairs, and the ounce-of-gold prize challenge to linearize CCS. Finally, we turn to machine learning of analytical structure property relationships in CCS. These relationships correspond to inferred, rather than derived through variational principle, solutions of the electronic Schrodinger equation. © 2013 Wiley Periodicals, Inc.

Stefan Grimme - One of the best experts on this subject based on the ideXlab platform.

  • exploration of Chemical Compound conformer and reaction space with meta dynamics simulations based on tight binding quantum Chemical calculations
    Journal of Chemical Theory and Computation, 2019
    Co-Authors: Stefan Grimme
    Abstract:

    The semiempirical tight-binding based quantum chemistry method GFN2-xTB is used in the framework of meta-dynamics (MTD) to globally explore Chemical Compound, conformer, and reaction space. The biasing potential given as a sum of Gaussian functions is expressed with the root-mean-square-deviation (RMSD) in Cartesian space as a metric for the collective variables. This choice makes the approach robust and generally applicable to three common problems (i.e., conformer search, Chemical reaction space exploration in a virtual nanoreactor, and for guessing reaction paths). Because of the inherent locality of the atomic RMSD, functional group or fragment selective treatments are possible facilitating the investigation of catalytic processes where, for example, only the substrate is thermally activated. Due to the approximate character of the GFN2-xTB method, the resulting structure ensembles require further refinement with more sophisticated, for example, density functional or wave function theory methods. Howe...