Alchemical Normal Modes Unify Chemical Space.
暂无分享,去创建一个
[1] E. Hückel,et al. Quantentheoretische Beiträge zum Benzolproblem , 1931 .
[2] G. W. Wheland,et al. A Quantum Mechanical Discussion of Orientation of Substituents in Aromatic Molecules , 1935 .
[3] H. C. Longuet-Higgins,et al. The electronic structure of conjugated systems I. General theory , 1947, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.
[4] H. C. Longuet-Higgins,et al. The electronic structure of conjugated systems. , 1948, Proceedings of the Royal Society of London. Series A, Mathematical and physical sciences.
[5] E. Bright Wilson,et al. Four‐Dimensional Electron Density Function , 1962 .
[6] Peter Politzer,et al. Some new energy formulas for atoms and molecules , 1974 .
[7] J. Janak,et al. Proof that ? E /? n i =e in density-functional theory , 1978 .
[8] A simple relation between nuclear charges and potential surfaces , 1985 .
[9] R. Parr. Density-functional theory of atoms and molecules , 1989 .
[10] Stefano de Gironcoli,et al. Structure and phase stability of GaxIn1-xP solid solutions from computational alchemy. , 1994, Physical review letters.
[11] Burke,et al. Generalized Gradient Approximation Made Simple. , 1996, Physical review letters.
[12] B. Baekelandt. The nuclear Fukui function and Berlin’s binding function in density functional theory , 1996 .
[13] P. Kirkpatrick,et al. Chemical space , 2004, Nature.
[14] Matthias Krack,et al. Pseudopotentials for H to Kr optimized for gradient-corrected exchange-correlation functionals , 2005 .
[15] Ursula Rothlisberger,et al. Variational particle number approach for rational compound design. , 2005, Physical review letters.
[16] Gerbrand Ceder,et al. Toward Computational Materials Design: The Impact of Density Functional Theory on Materials Research , 2006 .
[17] O. A. von Lilienfeld,et al. Molecular grand-canonical ensemble density functional theory and exploration of chemical space. , 2006, The Journal of chemical physics.
[18] Weitao Yang,et al. Designing molecules by optimizing potentials. , 2006, Journal of the American Chemical Society.
[19] R. Harrison,et al. Direct computation of general chemical energy differences: Application to ionization potentials, excitation, and bond energies. , 2006, The Journal of chemical physics.
[20] Denis Andrienko,et al. Tuning electronic eigenvalues of benzene via doping. , 2007, The Journal of chemical physics.
[21] O. A. von Lilienfeld,et al. Alchemical Variations of Intermolecular Energies According to Molecular Grand-Canonical Ensemble Density Functional Theory. , 2007, Journal of chemical theory and computation.
[22] K. Kremer,et al. Structure–charge mobility relation for hexabenzocoronene derivatives , 2008 .
[23] Kurt Kremer,et al. Towards high charge-carrier mobilities by rational design of the shape and periphery of discotics. , 2009, Nature materials.
[24] Rational design of the shape and periphery of discotics: a synthetic way towards high charge carrier mobilities , 2009 .
[25] O. Anatole von Lilienfeld. Accurate ab initio energy gradients in chemical compound space , 2009 .
[26] Graeme Henkelman,et al. Alchemical derivatives of reaction energetics. , 2010, The Journal of chemical physics.
[27] P. Ayers,et al. Computing Second-Order Functional Derivatives with Respect to the External Potential , 2010 .
[28] M. Tuckerman. Statistical Mechanics: Theory and Molecular Simulation , 2010 .
[29] Anubhav Jain,et al. A high-throughput infrastructure for density functional theory calculations , 2011 .
[30] O. A. V. Lilienfeld,et al. First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties , 2012, 1209.5033.
[31] R. Balawender,et al. Higher order alchemical derivatives from coupled perturbed self-consistent field theory. , 2012, The Journal of chemical physics.
[32] K. Müller,et al. Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.
[33] Klaus-Robert Müller,et al. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. , 2013, Journal of chemical theory and computation.
[34] Marco Buongiorno Nardelli,et al. The high-throughput highway to computational materials design. , 2013, Nature materials.
[35] Muratahan Aykol,et al. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) , 2013 .
[36] Sanguthevar Rajasekaran,et al. Accelerating materials property predictions using machine learning , 2013, Scientific Reports.
[37] Paul Geerlings,et al. Exploring Chemical Space with the Alchemical Derivatives. , 2013, Journal of chemical theory and computation.
[38] Kristof T. Schütt,et al. How to represent crystal structures for machine learning: Towards fast prediction of electronic properties , 2013, 1307.1266.
[39] Pavlo O. Dral,et al. Quantum chemistry structures and properties of 134 kilo molecules , 2014, Scientific Data.
[40] Alok Choudhary,et al. Combinatorial screening for new materials in unconstrained composition space with machine learning , 2014 .
[41] O Anatole von Lilienfeld,et al. Quantum mechanical treatment of variable molecular composition: from 'alchemical' changes of state functions to rational compound design. , 2014, Chimia.
[42] O. A. von Lilienfeld,et al. Guiding ab initio calculations by alchemical derivatives. , 2016, The Journal of chemical physics.
[43] Felix A Faber,et al. Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals. , 2015, Physical review letters.
[44] O. A. von Lilienfeld,et al. Alchemical screening of ionic crystals. , 2016, Physical chemistry chemical physics : PCCP.
[45] Stijn Fias,et al. Fast and accurate predictions of covalent bonds in chemical space. , 2015, The Journal of chemical physics.
[46] P. Ayers,et al. Chemical transferability of functional groups follows from the nearsightedness of electronic matter , 2017, Proceedings of the National Academy of Sciences.
[47] Asher Mullard,et al. The drug-maker's guide to the galaxy , 2017, Nature.
[48] O. A. von Lilienfeld,et al. Exploring dissociative water adsorption on isoelectronically BN doped graphene using alchemical derivatives. , 2017, The Journal of chemical physics.
[49] Olexandr Isayev,et al. ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules , 2017, Scientific Data.
[50] O. A. von Lilienfeld,et al. Alchemical Predictions for Computational Catalysis: Potential and Limitations. , 2017, The journal of physical chemistry letters.
[51] George E. Dahl,et al. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. , 2017, Journal of chemical theory and computation.
[52] Anders S. Christensen,et al. Alchemical and structural distribution based representation for universal quantum machine learning. , 2017, The Journal of chemical physics.
[53] O. Anatole von Lilienfeld,et al. Quantum Machine Learning in Chemical Compound Space , 2018 .
[54] P. Geerlings,et al. Exploring Chemical Space with Alchemical Derivatives: BN-Simultaneous Substitution Patterns in C60. , 2018, Journal of chemical theory and computation.
[55] Exploring water adsorption on isoelectronically doped graphene using alchemical derivatives , 2019 .