Amp: A modular approach to machine learning in atomistic simulations
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[1] Donald L. Thompson,et al. Interpolating moving least-squares methods for fitting potential energy surfaces: Detailed analysis of one-dimensional applications , 2003 .
[2] Roland T. Chin,et al. On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..
[3] J. Board,et al. Ewald summation techniques in perspective: a survey , 1996 .
[4] A. Gross,et al. Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks , 2004 .
[5] J. Nørskov,et al. Improved adsorption energetics within density-functional theory using revised Perdew-Burke-Ernzerhof functionals , 1999 .
[6] Karsten W. Jacobsen,et al. An object-oriented scripting interface to a legacy electronic structure code , 2002, Comput. Sci. Eng..
[7] W. Heisenberg,et al. Zur Quantentheorie der Molekeln , 1924 .
[8] Michele Parrinello,et al. Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.
[9] Mauricio Cafiero,et al. Using Density Functional Theory Methods for Modeling Induction and Dispersion Interactions in Ligand–Protein Complexes , 2010 .
[10] Ning Lu,et al. Mechanisms for Soil-Water Retention and Hysteresis at High Suction Range , 2015 .
[11] Tony C. Scott,et al. New approach for the electronic energies of the hydrogen molecular ion , 2006, physics/0607081.
[12] 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.
[13] Karsten Wedel Jacobsen,et al. A semi-empirical effective medium theory for metals and alloys , 1996 .
[14] Tjerk P. Straatsma,et al. NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations , 2010, Comput. Phys. Commun..
[15] C. Horowitz,et al. Functional representation of Liu and Siegbahn’s accurate ab initio potential energy calculations for H+H2 , 1978 .
[16] Nongnuch Artrith,et al. High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide , 2011 .
[17] Reinhard Klein,et al. Shape retrieval using 3D Zernike descriptors , 2004, Comput. Aided Des..
[18] George Z. Voyiadjis,et al. Nanoindentation Study of Yielding and Plasticity of Poly(methyl methacrylate) , 2015 .
[19] R. Kondor,et al. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. , 2009, Physical review letters.
[20] S. J. Moss,et al. Potential-energy surfaces and transition-state theory , 1983 .
[21] John R. Kitchin,et al. Neural network and ReaxFF comparison for Au properties , 2016 .
[22] Stefano de Gironcoli,et al. QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials , 2009, Journal of physics. Condensed matter : an Institute of Physics journal.
[23] I. Bahar,et al. Normal mode analysis : theory and applications to biological and chemical systems , 2005 .
[24] A. V. Duin,et al. ReaxFF: A Reactive Force Field for Hydrocarbons , 2001 .
[25] R. O. Jones,et al. Density functional theory: Its origins, rise to prominence, and future , 2015 .
[26] T Hollebeek,et al. Constructing multidimensional molecular potential energy surfaces from ab initio data. , 2003, Annual review of physical chemistry.
[27] J. Behler. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. , 2011, The Journal of chemical physics.
[28] M. Malshe,et al. Development of generalized potential-energy surfaces using many-body expansions, neural networks, and moiety energy approximations. , 2009, The Journal of chemical physics.
[29] D. Shaw,et al. Assessment of the utility of contact‐based restraints in accelerating the prediction of protein structure using molecular dynamics simulations , 2015, Protein science : a publication of the Protein Society.
[30] Andrew A. Peterson,et al. Neural: Machine-Learning for Atomistics, v0.1a , 2014 .
[31] N. A. Romero,et al. Electronic structure calculations with GPAW: a real-space implementation of the projector augmented-wave method , 2010, Journal of physics. Condensed matter : an Institute of Physics journal.
[32] Andrew A. Peterson,et al. Global Optimization of Adsorbate–Surface Structures While Preserving Molecular Identity , 2014, Topics in Catalysis.
[33] Kai Yan,et al. The Influence of Elastic Strain on Catalytic Activity in the Hydrogen Evolution Reaction. , 2016, Angewandte Chemie.
[34] Donald L. Thompson,et al. Ab initio dynamics: HeH+ + H2 → He + H3+ (C2ν) classical trajectories using a quantum mechanical potential‐energy surface , 1973 .
[35] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[36] Nongnuch Artrith,et al. Understanding the composition and activity of electrocatalytic nanoalloys in aqueous solvents: a combination of DFT and accurate neural network potentials. , 2014, Nano letters.
[37] A. Gross,et al. Descriptions of surface chemical reactions using a neural network representation of the potential-energy surface , 2006 .
[38] Efthimios Kaxiras,et al. Stress effects on the initial lithiation of crystalline silicon nanowires: reactive molecular dynamics simulations using ReaxFF. , 2015, Physical chemistry chemical physics : PCCP.
[39] Jürgen Hafner,et al. Ab‐initio simulations of materials using VASP: Density‐functional theory and beyond , 2008, J. Comput. Chem..
[40] Joel M. Bowman,et al. Permutationally invariant potential energy surfaces in high dimensionality , 2009 .
[41] M. A. Collins,et al. Molecular potential energy surfaces by interpolation , 1994 .
[42] Jenq-Neng Hwang,et al. Handbook of Neural Network Signal Processing , 2000, IEEE Transactions on Neural Networks.
[43] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[44] J. Doye,et al. Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms , 1997, cond-mat/9803344.
[45] Yin-Jia Zhang,et al. Competition between CO2 Reduction and H2 Evolution on Transition-Metal Electrocatalysts , 2014 .
[46] D. Marx. Ab initio molecular dynamics: Theory and Implementation , 2000 .
[47] R. Feynman. Forces in Molecules , 1939 .
[48] R. Kondor,et al. On representing chemical environments , 2012, 1209.3140.
[49] D. Varshalovich,et al. Quantum Theory of Angular Momentum , 1988 .
[50] A. V. Meremianin. Multipole expansions in four-dimensional hyperspherical harmonics , 2006 .
[51] Alireza Khorshidi,et al. Amp: Machine Learning for Atomistics v0.3alpha , 2015 .
[52] Satish T. S. Bukkapatnam,et al. Neural Networks in Chemical Reaction Dynamics , 2012 .