The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
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[1] B. Thole. Molecular polarizabilities calculated with a modified dipole interaction , 1981 .
[2] Thomas A. Halgren,et al. Merck molecular force field. III. Molecular geometries and vibrational frequencies for MMFF94 , 1996, J. Comput. Chem..
[3] G. Henkelman,et al. A climbing image nudged elastic band method for finding saddle points and minimum energy paths , 2000 .
[4] J. D. Gezelter,et al. Is the Ewald summation still necessary? Pairwise alternatives to the accepted standard for long-range electrostatics. , 2006, The Journal of chemical physics.
[5] Stefan Grimme,et al. Semiempirical GGA‐type density functional constructed with a long‐range dispersion correction , 2006, J. Comput. Chem..
[6] Michele Parrinello,et al. Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.
[7] M. Head‐Gordon,et al. Long-range corrected hybrid density functionals with damped atom-atom dispersion corrections. , 2008, Physical chemistry chemical physics : PCCP.
[8] M. Parrinello,et al. Well-tempered metadynamics: a smoothly converging and tunable free-energy method. , 2008, Physical review letters.
[9] Koichi Yamashita,et al. Fitting sparse multidimensional data with low-dimensional terms , 2009, Comput. Phys. Commun..
[10] R. Komanduri,et al. Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases. , 2010, The Journal of chemical physics.
[11] R. Kondor,et al. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. , 2009, Physical review letters.
[12] P. Popelier,et al. Potential energy surfaces fitted by artificial neural networks. , 2010, The journal of physical chemistry. A.
[13] Nongnuch Artrith,et al. High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide , 2011 .
[14] Michele Parrinello,et al. Nucleation mechanism for the direct graphite-to-diamond phase transition. , 2011, Nature materials.
[15] Alán Aspuru-Guzik,et al. The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid , 2011 .
[16] J. Behler. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. , 2011, Physical chemistry chemical physics : PCCP.
[17] Alán Aspuru-Guzik,et al. Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics , 2011 .
[18] Klaus-Robert Müller,et al. Finding Density Functionals with Machine Learning , 2011, Physical review letters.
[19] A. Tkatchenko,et al. Accurate and efficient method for many-body van der Waals interactions. , 2012, Physical review letters.
[20] Jörg Behler,et al. A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges. , 2012, The Journal of chemical physics.
[21] K. Müller,et al. Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.
[22] John C. Snyder,et al. Orbital-free bond breaking via machine learning. , 2013, The Journal of chemical physics.
[23] T. Morawietz,et al. A density-functional theory-based neural network potential for water clusters including van der Waals corrections. , 2013, The journal of physical chemistry. A.
[24] Volodymyr Babin,et al. A Critical Assessment of Two-Body and Three-Body Interactions in Water. , 2012, Journal of chemical theory and computation.
[25] Sanguthevar Rajasekaran,et al. Accelerating materials property predictions using machine learning , 2013, Scientific Reports.
[26] Corey Oses,et al. Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints , 2014, 1412.4096.
[27] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[28] Kristof T. Schütt,et al. How to represent crystal structures for machine learning: Towards fast prediction of electronic properties , 2013, 1307.1266.
[29] Alán Aspuru-Guzik,et al. Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project , 2014 .
[30] Michele Ceriotti,et al. i-PI: A Python interface for ab initio path integral molecular dynamics simulations , 2014, Comput. Phys. Commun..
[31] Zhaojun Zhang,et al. Effects of reagent rotational excitation on the H + CHD₃ → H₂ + CD₃ reaction: a seven dimensional time-dependent wave packet study. , 2014, The Journal of chemical physics.
[32] Li Li,et al. Understanding Machine-learned Density Functionals , 2014, ArXiv.
[33] O. Anatole von Lilienfeld,et al. Modeling electronic quantum transport with machine learning , 2014, 1401.8277.
[34] K. Müller,et al. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space , 2015, The journal of physical chemistry letters.
[35] Jun Li,et al. A permutationally invariant full-dimensional ab initio potential energy surface for the abstraction and exchange channels of the H + CH4 system. , 2015, The Journal of chemical physics.
[36] Li Li,et al. Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals , 2015, ArXiv.
[37] Alán Aspuru-Guzik,et al. Advances in molecular quantum chemistry contained in the Q-Chem 4 program package , 2014, Molecular Physics.
[38] Joel M Bowman,et al. Permutationally Invariant Fitting of Many-Body, Non-covalent Interactions with Application to Three-Body Methane-Water-Water. , 2015, Journal of chemical theory and computation.
[39] Sergei Manzhos,et al. Neural network‐based approaches for building high dimensional and quantum dynamics‐friendly potential energy surfaces , 2015 .
[40] Luke E K Achenie,et al. Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening. , 2015, The journal of physical chemistry letters.
[41] Andreas W Götz,et al. On the representation of many-body interactions in water. , 2015, The Journal of chemical physics.
[42] Jun Chen,et al. Communication: Fitting potential energy surfaces with fundamental invariant neural network. , 2016, The Journal of chemical physics.
[43] Kun Yao,et al. Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks. , 2015, Journal of chemical theory and computation.
[44] Czech Republic,et al. Learning physical descriptors for materials science by compressed sensing , 2016, 1612.04285.
[45] Alán Aspuru-Guzik,et al. Neural Networks for the Prediction of Organic Chemistry Reactions , 2016, ACS central science.
[46] Andrew A Peterson,et al. Acceleration of saddle-point searches with machine learning. , 2016, The Journal of chemical physics.
[47] Andreas W Götz,et al. On the accuracy of the MB-pol many-body potential for water: Interaction energies, vibrational frequencies, and classical thermodynamic and dynamical properties from clusters to liquid water and ice. , 2016, The Journal of chemical physics.
[48] Kieron Burke,et al. Pure density functional for strong correlation and the thermodynamic limit from machine learning , 2016, 1609.03705.
[49] Alireza Khorshidi,et al. Amp: A modular approach to machine learning in atomistic simulations , 2016, Comput. Phys. Commun..
[50] Noam Bernstein,et al. Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression. , 2016, Journal of chemical theory and computation.
[51] D. Lu,et al. Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles. , 2017, The journal of physical chemistry letters.
[52] Andreas Verras,et al. Is Multitask Deep Learning Practical for Pharma? , 2017, J. Chem. Inf. Model..
[53] Gábor Csányi,et al. Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials. , 2016, The journal of physical chemistry. B.
[54] Heather J Kulik,et al. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships. , 2017, The journal of physical chemistry. A.
[55] Chunjie Luo,et al. pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning. , 2017, Analytical chemistry.
[56] Alexandre Tkatchenko,et al. Quantum-chemical insights from deep tensor neural networks , 2016, Nature Communications.
[57] Ryosuke Jinnouchi,et al. Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm. , 2017, The journal of physical chemistry letters.
[58] Li Li,et al. Bypassing the Kohn-Sham equations with machine learning , 2016, Nature Communications.
[59] Klaus-Robert Müller,et al. Machine learning of accurate energy-conserving molecular force fields , 2016, Science Advances.
[60] E. Weinan,et al. Deep Potential: a general representation of a many-body potential energy surface , 2017, 1707.01478.
[61] Mariano Sigman,et al. The language of geometry: Fast comprehension of geometrical primitives and rules in human adults and preschoolers , 2017, PLoS Comput. Biol..
[62] Bernard R Brooks,et al. Machine Learning Force Field Parameters from Ab Initio Data. , 2017, Journal of chemical theory and computation.
[63] John E Herr,et al. The many-body expansion combined with neural networks. , 2016, The Journal of chemical physics.
[64] Cormac Toher,et al. Universal fragment descriptors for predicting properties of inorganic crystals , 2016, Nature Communications.
[65] K. Jordan,et al. Preface: Special Topic: From Quantum Mechanics to Force Fields. , 2017, The Journal of chemical physics.
[66] Heather J Kulik,et al. Predicting electronic structure properties of transition metal complexes with neural networks† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc01247k , 2017, Chemical science.
[67] Olexandr Isayev,et al. ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules , 2017, Scientific Data.
[68] Jörg Behler,et al. Accurate Neural Network Description of Surface Phonons in Reactive Gas–Surface Dynamics: N2 + Ru(0001) , 2017, The journal of physical chemistry letters.
[69] J. Behler. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems. , 2017, Angewandte Chemie.
[70] Christoph Kreisbeck,et al. Machine learning for quantum dynamics: deep learning of excitation energy transfer properties† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc03542j , 2017, Chemical science.
[71] Artem R Oganov,et al. Energy-free machine learning force field for aluminum , 2017, Scientific Reports.
[72] Andrew G. Taube,et al. Improving the accuracy of Møller-Plesset perturbation theory with neural networks. , 2017, The Journal of chemical physics.
[73] John E. Herr,et al. Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network. , 2017, The journal of physical chemistry letters.
[74] Volker L. Deringer,et al. Machine learning based interatomic potential for amorphous carbon , 2016, 1611.03277.
[75] Vijay S. Pande,et al. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics , 2016, bioRxiv.
[76] A. W. Götz,et al. Toward chemical accuracy in the description of ion-water interactions through many-body representations. Alkali-water dimer potential energy surfaces. , 2017, The Journal of chemical physics.
[77] Ekin D. Cubuk,et al. Representations in neural network based empirical potentials. , 2017, The Journal of chemical physics.
[78] Nathan S. Lewis,et al. Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction , 2017 .
[79] George E. Dahl,et al. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. , 2017, Journal of chemical theory and computation.
[80] Francesco Paesani,et al. Molecular Origin of the Vibrational Structure of Ice Ih. , 2017, The journal of physical chemistry letters.
[81] Kyle Mills,et al. Deep learning and the Schrödinger equation , 2017, ArXiv.
[82] Michael Gastegger,et al. Machine learning molecular dynamics for the simulation of infrared spectra† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02267k , 2017, Chemical science.
[83] Emma Strubell,et al. Machine-learned and codified synthesis parameters of oxide materials , 2017, Scientific Data.
[84] J S Smith,et al. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost , 2016, Chemical science.
[85] Weitao Yang,et al. Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations. , 2017, The Journal of chemical physics.
[86] Alexie M. Kolpak,et al. Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods , 2017, Scientific Reports.
[87] W. Wang,et al. Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability. , 2017, The journal of physical chemistry letters.
[88] V. Barone,et al. Force Field Parametrization of Metal Ions from Statistical Learning Techniques , 2017, Journal of chemical theory and computation.
[89] Gregory S. Ezra,et al. Empirical Classification of Trajectory Data: An Opportunity for the Use of Machine Learning in Molecular Dynamics. , 2017, The journal of physical chemistry. B.