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The computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology.
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Publisher: Southern African Institute of Mining and Metallurgy
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Publisher: American Institute of Physics
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Publisher: American Institute of Physics
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Number: 5 Publisher: International Union of Crystallography
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Publisher: American Institute of Physics
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Publisher: American Institute of Physics
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Publisher: John Wiley & Sons, Ltd
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Publisher: American Institute of Physics
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Publisher: American Institute of Physics
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Publisher: American Chemical Society
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Publisher: American Physical Society
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Publisher: American Institute of Physics
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Publisher: National Academy of Sciences Section: Research Article
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Publisher: American Chemical Society
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1993
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Publisher: American Institute of Physics
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Publisher: American Chemical Society
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Publisher: American Physical Society
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Publisher: American Institute of Physics
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T. A. Halgren and R. B. Nachbar, “Merck molecular force field. IV. conformational energies and geometries for MMFF94,” Journal of Computational Chemistry
1996
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T. A. Halgren, “Merck molecular force field. III. Molecular geometries and vibrational frequencies for MMFF94,” Journal of Computational Chemistry
1996
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T. A. Halgren, “Merck molecular force field. II. MMFF94 van der Waals and electrostatic parameters for intermolecular interactions,” Journal of Computational Chemistry
1996
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T. A. Halgren, “Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94,” Journal of Computational Chemistry
1996
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T. A. Halgren, “Merck molecular force field. V. Extension of MMFF94 using experimental data, additional computational data, and empirical rules,” Journal of Computational Chemistry
Publisher: American Chemical Society
S. Riniker and G. A. Landrum, “Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation,” Journal of Chemical Information and Modeling · 2015
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K. Hansen, F. Biegler, R. Ramakrishnan, W. Pronobis, O. A. von Lilienfeld, K.-R. Müller, and A. Tkatchenko, “Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space,” The Journal of Physical Chemistry Letters
2015
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R. Ramakrishnan, P. Dral, M. Rupp, and O. A. von Lilienfeld, “Big Data meets Quantum Chemistry Approximations: The Δ \Delta -Machine Learning Approach,” Journal of Chemical Theory and Computation
2015
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http://arxiv.org/abs/1502.04563
R. Ramakrishnan and O. A. von Lilienfeld, “Many Molecular Properties from One Kernel in Chemical Space,” CHIMIA · 2015
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1996
Cited alongside, same era.
J. J. Moré and Z. Wu, “Distance Geometry Optimization for Protein Structures,” Journal of Global Optimization
1999
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Publisher: American Physical Society
G. Kresse and D. Joubert, “From ultrasoft pseudopotentials to the projector augmented-wave method,” Physical Review B · 1999
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T. A. Halgren, “MMFF VI. MMFF94s option for energy minimization studies,” Journal of Computational Chemistry
1999
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T. A. Halgren, “MMFF VII. Characterization of MMFF94, MMFF94s, and other widely available force fields for conformational energies and for intermolecular-interaction energies and geometries,” Journal of Computational Chemistry
1999
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New York, NY: Springer New York, 2000
V. N. Vapnik, The Nature of Statistical Learning Theory · 2000
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Publisher: American Chemical Society
T. N. Doman, S. L. McGovern, B. J. Witherbee, T. P. Kasten, R. Kurumbail, W. C. Stallings, D. T. Connolly, and B. K. Shoichet, “Molecular Docking and High-Throughput Screening for Novel Inhibitors of Protein Tyrosine Phosphatase-1B,” Journal of Medicinal Chemistry · 2002
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M. Schwilk, D. N. Tahchieva, and O. A. von Lilienfeld, “Large yet bounded: Spin gap ranges in carbenes,” arXiv:2004.10600 [physics] · 2004
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B. Huang and O. A. von Lilienfeld, “Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity,” The Journal of Chemical Physics
2016
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Publisher: American Physical Society
F. A. Faber, A. Lindmaa, O. A. von Lilienfeld, and R. Armiento, “Machine Learning Energies of 2 Million Elpasolite $(AB{C}_{2}{D}_{6})$ Crystals,” Physical Review Letters · 2016
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J. J. P. Stewart, “Mopac2016, stewart computational chemistry, colorado springs, co, usa,” 2016
2016
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A. S. Christensen, F. A. Faber, B. Huang, L. A. Bratholm, A. Tkatchenko, K.-R. Müller, and O. A. v. Lilienfeld, “"QML: A Python Toolkit for Quantum Machine Learning" https://github.com/qmlcode/qml,” 2017
2017
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J. Schmidt, J. Shi, P. Borlido, L. Chen, S. Botti, and M. A. Marques, “Predicting the thermodynamic stability of solids combining density functional theory and machine learning,” Chemistry of Materials
2017
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F. A. Faber, A. S. Christensen, B. Huang, and O. A. von Lilienfeld, “Alchemical and structural distribution based representation for universal quantum machine learning,” The Journal of Chemical Physics
2018
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O. A. von Lilienfeld, “Quantum Machine Learning in Chemical Compound Space,” Angewandte Chemie International Edition
2018
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_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/wcms.1327
F. Neese, “Software update: the ORCA program system, version 4.0,” WIREs Computational Molecular Science · 2018
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N. Yoshikawa and G. R. Hutchison, “Fast, efficient fragment-based coordinate generation for Open Babel,” Journal of Cheminformatics
2019
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E. Mansimov, O. Mahmood, S. Kang, and K. Cho, “Molecular Geometry Prediction using a Deep Generative Graph Neural Network,” Scientific Reports
2019
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Publisher: American Chemical Society
C. Bannwarth, S. Ehlert, and S. Grimme, “GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions,” Journal of Chemical Theory and Computation · 2019
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Publisher: American Institute of Physics
D. A. Kreplin, P. J. Knowles, and H.-J. Werner, “Second-order MCSCF optimization revisited. I. Improved algorithms for fast and robust second-order CASSCF convergence,” The Journal of Chemical Physics · 2019
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G. F. v. Rudorff, S. Heinen, M. Bragato, and O. A. v. Lilienfeld, “Thousands of reactants and transition states for competing E2 and SN2 reactions,” Machine Learning: Science and Technology
2020
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Publisher: IOP Publishing
M. Krenn, F. Häse, A. Nigam, P. Friederich, and A. Aspuru-Guzik, “Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation,” Machine Learning: Science and Technology · 2020
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2020
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Number: 7 Publisher: Nature Publishing Group
O. A. von Lilienfeld, K.-R. Müller, and A. Tkatchenko, “Exploring chemical compound space with quantum-based machine learning,” Nature Reviews Chemistry · 2020
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Publisher: American Institute of Physics
A. S. Christensen, L. A. Bratholm, F. A. Faber, and O. Anatole von Lilienfeld, “FCHL revisited: Faster and more accurate quantum machine learning,” The Journal of Chemical Physics · 2020
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B. Huang and O. A. von Lilienfeld, “Quantum machine learning using atom-in-molecule-based fragments selected on the fly,” Nature Chemistry
2020
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Publisher: IOP Publishing
O. Çaylak, O. A. v. Lilienfeld, and B. Baumeier, “Wasserstein metric for improved quantum machine learning with adjacency matrix representations,” Machine Learning: Science and Technology · 2020
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Publisher: Royal Society of Chemistry
G. F. v. Rudorff and O. A. v. Lilienfeld, “Rapid and accurate molecular deprotonation energies from quantum alchemy,” Physical Chemistry Chemical Physics · 2020
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S. Senthil, S. Chakraborty, and R. Ramakrishnan, “Troubleshooting unstable molecules in chemical space,” Chemical Science
2021
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https://github.com/charnley/rmsd, ce7f533
J. C. Kromann, “Calculate Root-mean-square deviation (RMSD) of two molecules,” Github · 2021
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