A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
暂无分享,去创建一个
[1] Olexandr Isayev,et al. Deep reinforcement learning for de novo drug design , 2017, Science Advances.
[2] Antonio Lavecchia,et al. Machine-learning approaches in drug discovery: methods and applications. , 2015, Drug discovery today.
[3] Frederick R. Manby,et al. Fast Hartree–Fock theory using local density fitting approximations , 2004 .
[4] Zachary W. Ulissi,et al. To address surface reaction network complexity using scaling relations machine learning and DFT calculations , 2017, Nature Communications.
[5] Volker L. Deringer,et al. Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures. , 2017, The Journal of chemical physics.
[6] A. Becke. Density-functional thermochemistry. III. The role of exact exchange , 1993 .
[7] Vijay S. Pande,et al. Molecular graph convolutions: moving beyond fingerprints , 2016, Journal of Computer-Aided Molecular Design.
[8] M. Frisch,et al. Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields , 1994 .
[9] E Weinan,et al. Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics , 2017, Physical review letters.
[10] Bing Huang,et al. Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited. , 2018, Journal of chemical theory and computation.
[11] S. H. Vosko,et al. Accurate spin-dependent electron liquid correlation energies for local spin density calculations: a critical analysis , 1980 .
[12] John D. Watts,et al. Non-iterative fifth-order triple and quadruple excitation energy corrections in correlated methods , 1990 .
[13] S. F. Boys. Construction of Some Molecular Orbitals to Be Approximately Invariant for Changes from One Molecule to Another , 1960 .
[14] Gisbert Schneider,et al. Deep Learning in Drug Discovery , 2016, Molecular informatics.
[15] Parr,et al. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. , 1988, Physical review. B, Condensed matter.
[16] Gerald Knizia,et al. Intrinsic Atomic Orbitals: An Unbiased Bridge between Quantum Theory and Chemical Concepts. , 2013, Journal of chemical theory and computation.
[17] Martin Schütz,et al. Low-order scaling local electron correlation methods. III. Linear scaling local perturbative triples correction (T) , 2000 .
[18] R. Nesbet. Brueckner's Theory and the Method of Superposition of Configurations , 1958 .
[19] Michele Parrinello,et al. Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.
[20] Klaus Obermayer,et al. Gaussian process regression: active data selection and test point rejection , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[21] J. Behler. Perspective: Machine learning potentials for atomistic simulations. , 2016, The Journal of chemical physics.
[22] Anders S. Christensen,et al. Operators in quantum machine learning: Response properties in chemical space. , 2018, The Journal of chemical physics.
[23] Justin S. Smith,et al. Hierarchical modeling of molecular energies using a deep neural network. , 2017, The Journal of chemical physics.
[24] R. Kondor,et al. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. , 2009, Physical review letters.
[25] B. Kramer,et al. Localization: theory and experiment , 1993 .
[26] P. C. Hariharan,et al. The influence of polarization functions on molecular orbital hydrogenation energies , 1973 .
[27] Alán Aspuru-Guzik,et al. Inverse molecular design using machine learning: Generative models for matter engineering , 2018, Science.
[28] Alán Aspuru-Guzik,et al. Advances in molecular quantum chemistry contained in the Q-Chem 4 program package , 2014, Molecular Physics.
[29] Francesco Paesani,et al. Getting the Right Answers for the Right Reasons: Toward Predictive Molecular Simulations of Water with Many-Body Potential Energy Functions. , 2016, Accounts of chemical research.
[30] Geoffrey J. Gordon,et al. A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians. , 2018, Journal of chemical theory and computation.
[31] A. Szabo,et al. Modern quantum chemistry , 1982 .
[32] K. Pearson. Mathematical Contributions to the Theory of Evolution. III. Regression, Heredity, and Panmixia , 1896 .
[33] J. Cizek. On the Correlation Problem in Atomic and Molecular Systems. Calculation of Wavefunction Components in Ursell-Type Expansion Using Quantum-Field Theoretical Methods , 1966 .
[34] Michele Ceriotti,et al. Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond. , 2014, The Journal of chemical physics.
[35] Lawrence D. Jackel,et al. Learning Curves: Asymptotic Values and Rate of Convergence , 1993, NIPS.
[36] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[37] Jörg Behler,et al. Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions. , 2018, The Journal of chemical physics.
[38] Alberto Fabrizio,et al. Transferable Machine-Learning Model of the Electron Density , 2018, ACS central science.
[39] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .
[40] Marwin H. S. Segler,et al. Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction. , 2017, Chemistry.
[41] Mike Preuss,et al. Planning chemical syntheses with deep neural networks and symbolic AI , 2017, Nature.
[42] Paul Raccuglia,et al. Machine-learning-assisted materials discovery using failed experiments , 2016, Nature.
[43] Li Li,et al. Bypassing the Kohn-Sham equations with machine learning , 2016, Nature Communications.
[44] David Li,et al. Deep Learning in Drug Discovery and Medicine; Scratching the Surface , 2018, Molecules.
[45] Alán Aspuru-Guzik,et al. Neural Networks for the Prediction of Organic Chemistry Reactions , 2016, ACS central science.
[46] K. Müller,et al. Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.
[47] Matthias Rupp,et al. Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. , 2015, Journal of chemical theory and computation.
[48] Joseph Gomes,et al. MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a , 2017, Chemical science.
[49] David W Toth,et al. The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics , 2017, Chemical science.
[50] Daniel W. Davies,et al. Machine learning for molecular and materials science , 2018, Nature.
[51] Hans-Joachim Werner,et al. Local treatment of electron correlation in coupled cluster theory , 1996 .
[52] Vijay S. Pande,et al. MoleculeNet: a benchmark for molecular machine learning , 2017, Chemical science.
[53] J S Smith,et al. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost , 2016, Chemical science.
[54] Anders S. Christensen,et al. Alchemical and structural distribution based representation for universal quantum machine learning. , 2017, The Journal of chemical physics.
[55] M. Parrinello,et al. Accurate sampling using Langevin dynamics. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.
[56] U. Kaldor. Localized Orbitals for NH3, C2H4, and C2H2 , 1967 .
[57] Stefanie Jegelka,et al. Virtual screening of inorganic materials synthesis parameters with deep learning , 2017, npj Computational Materials.
[58] M. Rupp,et al. Machine learning of molecular electronic properties in chemical compound space , 2013, 1305.7074.
[59] T. H. Dunning. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen , 1989 .
[60] Alexandre Tkatchenko,et al. Quantum-chemical insights from deep tensor neural networks , 2016, Nature Communications.
[61] Andrew G. Taube,et al. Improving the accuracy of Møller-Plesset perturbation theory with neural networks. , 2017, The Journal of chemical physics.
[62] M. Plesset,et al. Note on an Approximation Treatment for Many-Electron Systems , 1934 .
[63] Thomas F. Miller,et al. Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis. , 2018, Journal of chemical theory and computation.
[64] Klaus-Robert Müller,et al. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. , 2013, Journal of chemical theory and computation.
[65] Christopher Wolverton,et al. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments , 2018, Science Advances.
[66] Lorenz C. Blum,et al. 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. , 2009, Journal of the American Chemical Society.
[67] Ilias Bilionis,et al. Gaussian processes with built-in dimensionality reduction: Applications in high-dimensional uncertainty propagation , 2016, 1602.04550.
[68] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[69] Yang Yang,et al. Accurate molecular polarizabilities with coupled cluster theory and machine learning , 2018, Proceedings of the National Academy of Sciences.