On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization.
暂无分享,去创建一个
[1] S. Goedecker. Minima hopping: an efficient search method for the global minimum of the potential energy surface of complex molecular systems. , 2004, The Journal of chemical physics.
[2] Zhenwei Li,et al. Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces. , 2015, Physical review letters.
[3] A. Oganov,et al. Crystal structure prediction using ab initio evolutionary techniques: principles and applications. , 2006, The Journal of chemical physics.
[4] R. Kondor,et al. On representing chemical environments , 2012, 1209.3140.
[5] M. Lazzeri,et al. Stress-driven reconstruction of an oxide surface: the anatase TiO(2)(001)-(1 x 4) surface. , 2001, Physical review letters.
[6] M Schmid,et al. Structure of Ag(111)-p(4 x 4)-O: no silver oxide. , 2006, Physical review letters.
[7] Rampi Ramprasad,et al. Adaptive machine learning framework to accelerate ab initio molecular dynamics , 2015 .
[8] Chris J Pickard,et al. Ab initio random structure searching , 2011, Journal of physics. Condensed matter : an Institute of Physics journal.
[9] Alexandre Tkatchenko,et al. Quantum-chemical insights from deep tensor neural networks , 2016, Nature Communications.
[10] A. Laio,et al. Escaping free-energy minima , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[11] Klaus-Robert Müller,et al. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. , 2013, Journal of chemical theory and computation.
[12] A. Gross,et al. Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks , 2004 .
[13] A Michaelides,et al. Revisiting the structure of the p(4 x 4) surface oxide on Ag(111). , 2006, Physical review letters.
[14] John E. Herr,et al. Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network. , 2017, The journal of physical chemistry letters.
[15] Georg Kresse,et al. Structure of the Ultrathin Aluminum Oxide Film on NiAl(110) , 2005, Science.
[16] Michele Parrinello,et al. Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.
[17] George E. Dahl,et al. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. , 2017, Journal of chemical theory and computation.
[18] Anastassia N Alexandrova,et al. Search for the Lin(0/+1/-1) (n = 5-7) Lowest-Energy Structures Using the ab Initio Gradient Embedded Genetic Algorithm (GEGA). Elucidation of the Chemical Bonding in the Lithium Clusters. , 2005, Journal of chemical theory and computation.
[19] Chris J. Pickard,et al. Predicting interface structures: From SrTiO 3 to graphene , 2014, 1407.2153.
[20] O. A. von Lilienfeld,et al. Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity. , 2016, The Journal of chemical physics.
[21] Bjørk Hammer,et al. Combining Evolutionary Algorithms with Clustering toward Rational Global Structure Optimization at the Atomic Scale. , 2017, Journal of chemical theory and computation.
[22] Mario Valle,et al. How to quantify energy landscapes of solids. , 2009, The Journal of chemical physics.
[23] A. Atrei,et al. The SnO2(110)(4 × 1) structure determined by LEED intensity analysis , 2001 .
[24] Annabella Selloni,et al. Stress-Driven Reconstruction of an Oxide Surface , 2001 .
[25] Matthias Scheffler,et al. Stability and metastability of clusters in a reactive atmosphere: theoretical evidence for unexpected stoichiometries of MgMOx. , 2013, Physical review letters.
[26] Alireza Khorshidi,et al. Amp: A modular approach to machine learning in atomistic simulations , 2016, Comput. Phys. Commun..
[27] Claire S. Adjiman,et al. Report on the sixth blind test of organic crystal structure prediction methods , 2016, Acta crystallographica Section B, Structural science, crystal engineering and materials.
[28] 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.
[29] K. Müller,et al. Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.
[30] Ho,et al. Molecular geometry optimization with a genetic algorithm. , 1995, Physical review letters.
[31] B. Hammer,et al. Steps on rutile TiO 2 (110): Active sites for water and methanol dissociation , 2011, 1111.0428.
[32] U. Diebold,et al. Surface morphologies of SnO2(1 1 0) , 2003 .
[33] Chu Zhang,et al. Structure of the SnO_{2}(110)-(4×1) Surface. , 2017, Physical review letters.
[34] K. Jacobsen,et al. Real-space grid implementation of the projector augmented wave method , 2004, cond-mat/0411218.
[35] Tarak K Patra,et al. Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn. , 2017, ACS combinatorial science.
[36] M. Rupp,et al. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties , 2013, 1307.2918.
[37] Bjørk Hammer,et al. Systematic study of Au6 to Au12 gold clusters on MgO(100) F centers using density-functional theory. , 2012, Physical review letters.
[38] U. Starke,et al. NOVEL RECONSTRUCTION MECHANISM FOR DANGLING-BOND MINIMIZATION : COMBINED METHOD SURFACE STRUCTURE DETERMINATION OF SIC(111)-(3 X 3) , 1998 .
[39] 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.
[40] R. Johnston. Evolving better nanoparticles: Genetic algorithms for optimising cluster geometries , 2003 .
[41] Bjørk Hammer,et al. A genetic algorithm for first principles global structure optimization of supported nano structures. , 2014, The Journal of chemical physics.
[42] Francisco B. Pereira,et al. An evolutionary algorithm for global minimum search of binary atomic clusters , 2010 .
[43] Dmitry Yu. Zubarev,et al. Global minimum structure searches via particle swarm optimization , 2007, J. Comput. Chem..
[44] B. Hartke. Global geometry optimization of clusters using genetic algorithms , 1993 .