A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians.
暂无分享,去创建一个
Geoffrey J. Gordon | Haichen Li | Christopher R. Collins | D. Yaron | Haichen Li | Matteus Tanha | Christopher Collins | Matteus Tanha | Geoffrey J Gordon | David J Yaron
[1] Alán Aspuru-Guzik,et al. Inverse molecular design using machine learning: Generative models for matter engineering , 2018, Science.
[2] Joost VandeVondele,et al. Machine Learning Adaptive Basis Sets for Efficient Large Scale Density Functional Theory Simulation , 2018, Journal of chemical theory and computation.
[3] Thomas F. Miller,et al. Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis. , 2018, Journal of chemical theory and computation.
[4] Bálint Aradi,et al. Efficient Automatized Density-Functional Tight-Binding Parametrizations: Application to Group IV Elements. , 2018, Journal of chemical theory and computation.
[5] Y. Qi,et al. Transferable Self-Consistent Charge Density Functional Tight-Binding Parameters for Li–Metal and Li-Ions in Inorganic Compounds and Organic Solvents , 2018 .
[6] Geoffrey J. Gordon,et al. Constant size descriptors for accurate machine learning models of molecular properties. , 2018, The Journal of chemical physics.
[7] Marcus Elstner,et al. Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning. , 2018, Journal of chemical theory and computation.
[8] Olexandr Isayev,et al. ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules , 2017, Scientific Data.
[9] A. Niklasson,et al. Numerical Optimization of Density Functional Tight Binding Models: Application to Molecules Containing Carbon, Hydrogen, Nitrogen, and Oxygen. , 2017, Journal of chemical theory and computation.
[10] John E. Herr,et al. Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network. , 2017, The journal of physical chemistry letters.
[11] O. Anatole von Lilienfeld,et al. Machine Learning, Quantum Chemistry, and Chemical Space , 2017 .
[12] K. Hermansson,et al. Self-Consistent-Charge Density-Functional Tight-Binding (SCC-DFTB) Parameters for Ceria in 0D to 3D , 2017 .
[13] J S Smith,et al. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost , 2016, Chemical science.
[14] Alexandre Tkatchenko,et al. Quantum-chemical insights from deep tensor neural networks , 2016, Nature Communications.
[15] Maicon Pierre Lourenço,et al. FASP: a framework for automation of Slater–Koster file parameterization , 2016, Theoretical Chemistry Accounts.
[16] Weitao Yang,et al. Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks. , 2016, Journal of chemical theory and computation.
[17] Masato Kobayashi,et al. Three pillars for achieving quantum mechanical molecular dynamics simulations of huge systems: Divide‐and‐conquer, density‐functional tight‐binding, and massively parallel computation , 2016, J. Comput. Chem..
[18] John R. Kitchin,et al. Neural network and ReaxFF comparison for Au properties , 2016 .
[19] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[20] A. Ilie,et al. Self-consistent charge and dipole density functional tight binding method and application to carbon-based systems , 2016 .
[21] Nongnuch Artrith,et al. An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 , 2016 .
[22] Yoshifumi Nishimura,et al. Automatized Parameterization of DFTB Using Particle Swarm Optimization. , 2016, Journal of chemical theory and computation.
[23] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Thomas Heine,et al. DFTB Parameters for the Periodic Table, Part 2: Energies and Energy Gradients from Hydrogen to Calcium. , 2015, Journal of chemical theory and computation.
[25] Jörg Behler,et al. Constructing high‐dimensional neural network potentials: A tutorial review , 2015 .
[26] Ralph Roskies,et al. Bridges: a uniquely flexible HPC resource for new communities and data analytics , 2015, XSEDE.
[27] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[29] O. A. von Lilienfeld,et al. Electronic spectra from TDDFT and machine learning in chemical space. , 2015, The Journal of chemical physics.
[30] Walter Thiel,et al. Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations , 2015, Journal of chemical theory and computation.
[31] Arun Mannodi-Kanakkithodi,et al. Accelerated materials property predictions and design using motif-based fingerprints , 2015, 1503.07503.
[32] Matthias Rupp,et al. Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. , 2015, Journal of chemical theory and computation.
[33] J. Vybíral,et al. Big data of materials science: critical role of the descriptor. , 2014, Physical review letters.
[34] Lihong Hu,et al. Alternative approach to chemical accuracy: a neural networks-based first-principles method for heat of formation of molecules made of H, C, N, O, F, S, and Cl. , 2014, The journal of physical chemistry. A.
[35] Q. Cui,et al. Density functional tight binding: values of semi-empirical methods in an ab initio era. , 2014, Physical chemistry chemical physics : PCCP.
[36] Alok Choudhary,et al. Combinatorial screening for new materials in unconstrained composition space with machine learning , 2014 .
[37] Walter Thiel,et al. Semiempirical quantum–chemical methods , 2014 .
[38] Alán Aspuru-Guzik,et al. Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project , 2014 .
[39] Lyuben Zhechkov,et al. DFTB Parameters for the Periodic Table: Part 1, Electronic Structure. , 2013, Journal of chemical theory and computation.
[40] Klaus-Robert Müller,et al. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. , 2013, Journal of chemical theory and computation.
[41] Xiaohui Qu,et al. A big data approach to the ultra-fast prediction of DFT-calculated bond energies , 2013, Journal of Cheminformatics.
[42] James J. P. Stewart,et al. Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters , 2012, Journal of Molecular Modeling.
[43] Jean-Louis Reymond,et al. Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17 , 2012, J. Chem. Inf. Model..
[44] K. Müller,et al. Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.
[45] Bálint Aradi,et al. Automated Repulsive Parametrization for the DFTB Method. , 2011, Journal of chemical theory and computation.
[46] Noel M. O'Boyle,et al. Computational Design and Selection of Optimal Organic Photovoltaic Materials , 2011 .
[47] Jason Weston,et al. Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..
[48] Bálint Aradi,et al. An Improved Self-Consistent-Charge Density-Functional Tight-Binding (SCC-DFTB) Set of Parameters for Simulation of Bulk and Molecular Systems Involving Titanium. , 2010, Journal of chemical theory and computation.
[49] D. Yaron,et al. Using Molecular Similarity to Develop Reliable Models of Chemical Reactions in Complex Environments. , 2009, Journal of chemical theory and computation.
[50] Pekka Koskinen,et al. Density-functional tight-binding for beginners , 2009, 0910.5861.
[51] M. Elstner,et al. Automatized parametrization of SCC-DFTB repulsive potentials: application to hydrocarbons. , 2009, The journal of physical chemistry. A.
[52] D. York,et al. Description of phosphate hydrolysis reactions with the Self-Consistent-Charge Density-Functional-Tight-Binding (SCC-DFTB) theory. 1. Parameterization. , 2008, Journal of chemical theory and computation.
[53] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[54] T. Frauenheim,et al. Initial steps toward automating the fitting of DFTB Erep(r). , 2007, The journal of physical chemistry. A.
[55] T. Frauenheim,et al. DFTB+, a sparse matrix-based implementation of the DFTB method. , 2007, The journal of physical chemistry. A.
[56] Thomas Frauenheim,et al. Parameter Calibration of Transition-Metal Elements for the Spin-Polarized Self-Consistent-Charge Density-Functional Tight-Binding (DFTB) Method: Sc, Ti, Fe, Co, and Ni. , 2007, Journal of chemical theory and computation.
[57] Michele Parrinello,et al. Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.
[58] Hao Hu,et al. Fitting Molecular Electrostatic Potentials from Quantum Mechanical Calculations. , 2007, Journal of chemical theory and computation.
[59] Julian Tirado-Rives,et al. Comparison of SCC-DFTB and NDDO-based semiempirical molecular orbital methods for organic molecules. , 2006, The journal of physical chemistry. A.
[60] M. Elstner,et al. Validation of the density-functional based tight-binding approximation method for the calculation of reaction energies and other data. , 2005, The Journal of chemical physics.
[61] GuanHua Chen,et al. Improving the Accuracy of Density-Functional Theory Calculation: The Statistical Correction Approach , 2004 .
[62] C Z Wang,et al. Molecule intrinsic minimal basis sets. I. Exact resolution of ab initio optimized molecular orbitals in terms of deformed atomic minimal-basis orbitals. , 2004, The Journal of chemical physics.
[63] William L. Jorgensen,et al. PDDG/PM3 and PDDG/MNDO: Improved semiempirical methods , 2002, J. Comput. Chem..
[64] S. Suhai,et al. Application of an approximate density-functional method to sulfur containing compounds , 2001 .
[65] Walter Thiel,et al. Orthogonalization corrections for semiempirical methods , 2000 .
[66] Sándor Suhai,et al. Self-consistent-charge density-functional tight-binding method for simulations of complex materials properties , 1998 .
[67] Eamonn F. Healy,et al. Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model , 1985 .
[68] P. Pulay. Improved SCF convergence acceleration , 1982 .
[69] C. R. Deboor,et al. A practical guide to splines , 1978 .
[70] Carl de Boor,et al. A Practical Guide to Splines , 1978, Applied Mathematical Sciences.