Fetching the paper…
Reading the bibliography…
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation.
Jones, J. E. On the Determination of Molecular Fields. —II. From the Equation of State of a Gas. Proc. R. Soc. Lond. A 1924
1924
Earlier work this paper cites.
Andersen, H. C.; Chandler, D. Optimized Cluster Expansions for Classical Fluids. I. General Theory and Variational Formulation of the Mean Spherical Model and Hard Sphere Percus-Yevick Equations. J. Chem. Phys. 1972
1929
Earlier work this paper cites.
Gieres, F. Mathematical Surprises and Dirac’s Formalism in Quantum Mechanics. Rep. Prog. Phys. 2000
1931
Earlier work this paper cites.
Aronszajn, N. Theory of Reproducing Kernels. Trans. Amer. Math. Soc. 1950
1950
Earlier work this paper cites.
Löwdin, P.-O. On the Non-Orthogonality Problem Connected with the Use of Atomic Wave Functions in the Theory of Molecules and Crystals. J. Chem. Phys. 1950
1950
Earlier work this paper cites.
Kuhn, H. W. The Hungarian Method for the Assignment Problem. Nav. Res. Logist. Q. 1955
1955
Earlier work this paper cites.
Ramachandran, G.; Ramakrishnan, C.; Sasisekharan, V. Stereochemistry of Polypeptide Chain Configurations. Journal of Molecular Biology 1963
1963
Earlier work this paper cites.
Feynman, R. P.; Hibbs, A. R. Quantum Mechanics and Path Integrals ; McGraw-Hill: New York, 1964
1964
Earlier work this paper cites.
MacQueen, J. Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics. Berkeley, Calif., 1967; pp 281–297
1967
Earlier work this paper cites.
Alder, B. J.; Gass, D. M.; Wainwright, T. E. Studies in molecular dynamics. VIII. The transport coefficients for a hard-sphere fluid. The Journal of Chemical Physics 1970
1970
Earlier work this paper cites.
Damm, W.; Frontera, A.; Tirado–Rives, J.; Jorgensen, W. L. OPLS All-Atom Force Field for Carbohydrates. J. Comput. Chem. 1997
1970
Earlier work this paper cites.
Whitten, J. L. Coulombic Potential Energy Integrals and Approximations. The Journal of Chemical Physics 1973
1973
Earlier work this paper cites.
Stillinger, F. H.; Rahman, A. Improved Simulation of Liquid Water by Molecular Dynamics Comparison of Simple Potential Functions for Simulating Liquid Water Improved Simulation of Liquid Water by Molecular Dynamics*. J. Chem. Phys. J. Chem. Phys. J. Chem. Phys. Gen. Method J. Chem. Phys. J. Chem. Phys. J. Chem. Phys. 1974
1974
Earlier work this paper cites.
Stone, A. J. Transformation between cartesian and spherical tensors. Mol. Phys. 1975
1975
Earlier work this paper cites.
Nachbin, L. The Haar integral ; R. E. Krieger Pub. Co., 1976
1976
Earlier work this paper cites.
Pulay, P.; Fogarasi, G.; Pang, F.; Boggs, J. E. Systematic Ab Initio Gradient Calculation of Molecular Geometries, Force Constants, and Dipole Moment Derivatives. J. Am. Chem. Soc. 1979
1979
Earlier work this paper cites.
Kabsch, W.; Sander, C. Dictionary of Protein Secondary Structure: Pattern Recognition of Hydrogen-Bonded and Geometrical Features. Biopolymers 1983
1983
Earlier work this paper cites.
Steinhardt, P. J.; Nelson, D. R.; Ronchetti, M. Bond-Orientational Order in Liquids and Glasses. Phys. Rev. B 1983
1983
Earlier work this paper cites.
Sanchez, J.; Ducastelle, F.; Gratias, D. Generalized Cluster Description of Multicomponent Systems. Physica A: Statistical Mechanics and its Applications 1984
1984
Earlier work this paper cites.
Biedenharn, L. C.; Louck, J. D. The Racah-Wigner Algebra in Quantum Theory , 1st ed.; Cambridge University Press, 1984
1984
Earlier work this paper cites.
Car, R.; Parrinello, M. Unified Aproach for Molecular Dynamics and Density-Functional Theory. R. Car and M. Parrinello.Pdf. Phys. Rev. Lett. 1985
1985
Earlier work this paper cites.
Chandler, D. Introduction to Modern Statistical Mechanics ; Oxford University Press: New York, 1987
1987
Earlier work this paper cites.
Weininger, D. SMILES, a Chemical Language and Information System. 1. Introduction to Methodology and Encoding Rules. J. Chem. Inf. Model. 1988
1988
Earlier work this paper cites.
Pettifor, D. New Many-Body Potential for the Bond Order. Phys. Rev. Lett. 1989
1989
Earlier work this paper cites.
Kaufmann, K.; Baumeister, W. Single-Centre Expansion of Gaussian Basis Functions and the Angular Decomposition of Their Overlap Integrals. J. Phys. B At. Mol. Opt. Phys. 1989
1989
Earlier work this paper cites.
Allen, M. P.; Tildesley, D. J. Computer Simulation of Liquids ; Oxford University Press, USA, 1990
1990
Earlier work this paper cites.
Mayo, S. L.; Olafson, B. D.; Goddard, W. A. DREIDING: A Generic Force Field for Molecular Simulations. J. Phys. Chem. 1990
1990
Earlier work this paper cites.
Yang, W. Direct Calculation of Electron Density in Density-Functional Theory. Phys. Rev. Lett. 1991
1991
Earlier work this paper cites.
Baker, J.; Hehre, W. J. Geometry Optimization in Cartesian Coordinates: The End of theZ-Matrix? J. Comput. Chem. 1991
1991
Earlier work this paper cites.
Cheng, B.; Griffiths, R.-R.; Wengert, S.; Kunkel, C.; Stenczel, T.; Zhu, B.; Deringer, V. L.; Bernstein, N.; Margraf, J. T.; Reuter, K.; Csanyi, G. Mapping Materials and Molecules. Acc. Chem. Res. 2020
1991
Earlier work this paper cites.
Galli, G.; Parrinello, M. Large Scale Electronic Structure Calculations. Phys. Rev. Lett. 1992
1992
Earlier work this paper cites.
Yellott, J. I.; Iverson, G. J. Uniqueness properties of higher-order autocorrelation functions. J. Opt. Soc. Am. A, JOSAA 1992
1992
Earlier work this paper cites.
de Jong, S.; Kiers, H. A. Principal Covariates Regression. Chemometrics and Intelligent Laboratory Systems 1992
1992
Earlier work this paper cites.
Collins, M. A.; Parsons, D. F. Implications of Rotation–Inversion–Permutation Invariance for Analytic Molecular Potential Energy Surfaces. The Journal of Chemical Physics 1993
1993
Earlier work this paper cites.
Karpen, M. E.; Tobias, D. J.; Brooks, C. L. Statistical Clustering Techniques for the Analysis of Long Molecular Dynamics Trajectories: Analysis of 2.2-Ns Trajectories of YPGDV. Biochemistry 1993
1993
Earlier work this paper cites.
Parr, R. G.; Yang, W. Density-Functional Theory of Atoms and Molecules , 1st ed.; International Series of Monographs on Chemistry 16; Oxford Univ. Press [u.a.]: New York, NY, 1994
1994
Earlier work this paper cites.
Ischtwan, J.; Collins, M. A. Molecular Potential Energy Surfaces by Interpolation. The Journal of Chemical Physics 1994
1994
Earlier work this paper cites.
Bader, R. F. W. Atoms in Molecules: A Quantum Theory ; The International Series of Monographs on Chemistry 22; Clarendon Press ; Oxford University Press: Oxford [England] : New York, 1994
1994
Earlier work this paper cites.
Torda, A. E.; van Gunsteren, W. F. Algorithms for Clustering Molecular Dynamics Configurations. J. Comput. Chem. 1994
1994
Earlier work this paper cites.
Blank, T. B.; Brown, S. D.; Calhoun, A. W.; Doren, D. J. Neural Network Models of Potential Energy Surfaces. J. Chem. Phys. 1995
1995
Earlier work this paper cites.
Karelson, M.; Lobanov, V. S.; Katritzky, A. R. Quantum-Chemical Descriptors in QSAR/QSPR Studies. Chem. Rev. 1996
1996
Earlier work this paper cites.
Russell, C. L.; Manolopoulos, D. E. How to Observe the Elusive Resonances in F + H2 Reactive Scattering. Chemical Physics Letters 1996
1996
Earlier work this paper cites.
Halgren, T. A. Merck Molecular Force Field. I. Basis, Form, Scope, Parameterization, and Performance of MMFF94. J. Comput. Chem. 1996
1996
Earlier work this paper cites.
Baker, J.; Chan, F. The Location of Transition States: A Comparison of Cartesian, Z-Matrix, and Natural Internal Coordinates. J. Comput. Chem. 1996
1996
Earlier work this paper cites.
Horsfield, A. P.; Bratkovsky, A. M.; Fearn, M.; Pettifor, D. G.; Aoki, M. Bond-Order Potentials: Theory and Implementation. Phys. Rev. B 1996
1996
Earlier work this paper cites.
Ho, T.-S.; Rabitz, H. A General Method for Constructing Multidimensional Molecular Potential Energy Surfaces from Ab Initio Calculations. The Journal of Chemical Physics 1996
1996
Earlier work this paper cites.
Ester, M.; Kriegel, H.-P.; Sander, J.; Xu, X. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD 1996. 1996
1996
Earlier work this paper cites.
Frishman, D.; Argos, P. Incorporation of Non-Local Interactions in Protein Secondary Structure Prediction from the Amino Acid Sequence. Protein Eng Des Sel 1996
1996
Earlier work this paper cites.
Partridge, H.; Schwenke, D. W. The Determination of an Accurate Isotope Dependent Potential Energy Surface for Water from Extensive Ab Initio Calculations and Experimental Data. J. Chem. Phys. 1997
1997
Earlier work this paper cites.
Eldar, Y.; Lindenbaum, M.; Porat, M.; Zeevi, Y. Y. The Farthest Point Strategy for Progressive Image Sampling. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 1997
1997
Earlier work this paper cites.
Gassner, H.; Probst, M.; Lauenstein, A.; Hermansson, K. Representation of Intermolecular Potential Functions by Neural Networks. J. Phys. Chem. A 1998
1998
Earlier work this paper cites.
Saunders, C.; Gammerman, A.; Vovk, V. Ridge Regression Learning Algorithm in Dual Variables. Proceedings of the 15th International Conference on Machine Learning 1998
1998
Earlier work this paper cites.
Schölkopf, B.; Smola, A.; Müller, K.-R. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Comput. 1998
1998
Earlier work this paper cites.
Karelson, M. Molecular Descriptors in QSAR/QSPR ; Wiley-Interscience: New York, 2000
2000
Earlier work this paper cites.
Ozaki, T.; Aoki, M.; Pettifor, D. G. Block Bond-Order Potential as a Convergent Moments-Based Method. Phys. Rev. B 2000
2000
Earlier work this paper cites.
Frenkel, D.; Smit, B. Understanding Molecular Simulation , 2nd ed.; Academic Press: London, 2002
2002
Earlier work this paper cites.
Kazhdan, M.; Funkhouser, T.; Rusinkiewicz, S. Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors. Proceedings of the 2003 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing. Goslar, DEU, 2003; p 156–164
2003
Earlier work this paper cites.
Wales, D. Energy Landscapes: Applications to Clusters, Biomolecules and Glasses ; Cambridge University Press, 2003
2003
Earlier work this paper cites.
Brown, A.; McCoy, A. B.; Braams, B. J.; Jin, Z.; Bowman, J. M. Quantum and Classical Studies of Vibrational Motion of CH5 on a Global Potential Energy Surface Obtained from a Novel Ab Initio Direct Dynamics Approach. J. Chem. Phys. 2004
2004
Earlier work this paper cites.
Boutin, M.; Kemper, G. On Reconstructing N-Point Configurations from the Distribution of Distances or Areas. Advances in Applied Mathematics 2004
2004
Earlier work this paper cites.
Thompson, W. J. W. J. Angular momentum : an illustrated guide to rotational symmetries for physical systems ; Wiley-VCH, 2004; p 461
2004
Earlier work this paper cites.
Schneider, G.; Fechner, U. Computer-Based de Novo Design of Drug-like Molecules. Nat Rev Drug Discov 2005
2005
Earlier work this paper cites.
Huang, X.; Braams, B. J.; Bowman, J. M. Ab Initio Potential Energy and Dipole Moment Surfaces for $H_5O_2+̂$. J. Chem. Phys. 2005
2005
Earlier work this paper cites.
Prodan, E.; Kohn, W. Nearsightedness of Electronic Matter. Proc. Natl. Acad. Sci. 2005
2005
Earlier work this paper cites.
Rasmussen, C. E.; Williams, C. K. I. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) ; The MIT Press, 2005
2005
Earlier work this paper cites.
Coifman, R. R.; Lafon, S.; Lee, A. B.; Maggioni, M.; Nadler, B.; Warner, F.; Zucker, S. W. Geometric Diffusions as a Tool for Harmonic Analysis and Structure Definition of Data: Diffusion Maps. Proc. Natl. Acad. Sci. U. S. A. 2005
2005
Earlier work this paper cites.
Kuz’min, V. E.; Artemenko, A. G.; Polischuk, P. G.; Muratov, E. N.; Hromov, A. I.; Liahovskiy, A. V.; Andronati, S. A.; Makan, S. Y. Hierarchic system of QSAR models (1D-4D) on the base of simplex representation of molecular structure. Journal of Molecular Modeling 2005
2005
Earlier work this paper cites.
Das, P.; Moll, M.; Stamati, H.; Kavraki, L. E.; Clementi, C. Low-Dimensional, Free-Energy Landscapes of Protein-Folding Reactions by Nonlinear Dimensionality Reduction. Proc. Natl. Acad. Sci. U. S. A. 2006
2006
Earlier work this paper cites.
Caflisch, A. Network and Graph Analyses of Folding Free Energy Surfaces. Curr. Opin. Struct. Biol. 2006
2006
Earlier work this paper cites.
Oganov, A. R.; Glass, C. W. Crystal Structure Prediction Using Ab Initio Evolutionary Techniques: Principles and Applications. J. Chem. Phys. 2006
2006
Earlier work this paper cites.
Behler, J.; Parrinello, M. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces. Phys. Rev. Lett. 2007
2007
Earlier work this paper cites.
Behler, J.; Lorenz, S.; Reuter, K. Representing Molecule-Surface Interactions with Symmetry-Adapted Neural Networks. The Journal of Chemical Physics 2007
2007
Earlier work this paper cites.
Obrezanova, O.; Csányi, G.; Gola, J. M. R.; Segall, M. D. Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties. J. Chem. Inf. Model. 2007
2007
Earlier work this paper cites.
Tuckerman, M. Statistical Mechanics and Molecular Simulations ; Oxford University Press, 2008
2008
Earlier work this paper cites.
Ruppert, J.; Welch, W.; Jain, A. N. Automatic Identification and Representation of Protein Binding Sites for Molecular Docking. Protein Sci. 2008
2008
Earlier work this paper cites.
van der Maaten, L.; Hinton, G. Visualizing Data Using T-SNE. J. Mach. Learn. Res. 2008
2008
Earlier work this paper cites.
Braams, B. J.; Bowman, J. M. Permutationally Invariant Potential Energy Surfaces in High Dimensionality. Int. Rev. Phys. Chem. 2009
2009
Earlier work this paper cites.
Blum, L. C.; Reymond, J.-L. 970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13. J. Am. Chem. Soc. 2009
2009
Earlier work this paper cites.
Vanommeslaeghe, K.; Hatcher, E.; Acharya, C.; Kundu, S.; Zhong, S.; Shim, J.; Darian, E.; Guvench, O.; Lopes, P.; Vorobyov, I.; Mackerell, A. D. CHARMM General Force Field: A Force Field for Drug-like Molecules Compatible with the CHARMM All-Atom Additive Biological Force Fields. J. Comput. Chem. 2009
2009
Earlier work this paper cites.
Handley, C. M.; Popelier, P. L. A. Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning. J. Chem. Theory Comput. 2009
2009
Earlier work this paper cites.
Bowman, G. R.; Beauchamp, K. A.; Boxer, G.; Pande, V. S. Progress and Challenges in the Automated Construction of Markov State Models for Full Protein Systems. J. Chem. Phys. 2009
2009
Earlier work this paper cites.
Pietrucci, F.; Laio, A. A Collective Variable for the Efficient Exploration of Protein Beta-Sheet Structures: Application to SH3 and GB1. J. Chem. Theory Comput. 2009
2009
Earlier work this paper cites.
Oganov, A. R.; Valle, M. How to Quantify Energy Landscapes of Solids. The Journal of Chemical Physics 2009
2009
Earlier work this paper cites.
Cuturi, M. Positive Definite Kernels in Machine Learning. ArXiv Prepr. ArXiv09115367 2009
2009
Earlier work this paper cites.
Mahoney, M. W.; Drineas, P. CUR Matrix Decompositions for Improved Data Analysis. Proc. Natl. Acad. Sci. U. S. A. 2009
2009
Earlier work this paper cites.
Bartók, A. P.; Payne, M. C.; Kondor, R.; Csányi, G. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons. Phys. Rev. Lett. 2010
2010
Earlier work this paper cites.
Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics ; Methods and Principles in Medicinal Chemistry; Wiley, 2010; Vol. 2; pp 1–252
2010
Earlier work this paper cites.
Bowman, J. M.; Braams, B. J.; Carter, S.; Chen, C.; Czakó, G.; Fu, B.; Huang, X.; Kamarchik, E.; Sharma, A. R.; Shepler, B. C.; Wang, Y.; Xie, Z. Ab-Initio-Based Potential Energy Surfaces for Complex Molecules and Molecular Complexes. J. Phys. Chem. Lett. 2010
2010
Earlier work this paper cites.
Xie, Z.; Bowman, J. M. Permutationally Invariant Polynomial Basis for Molecular Energy Surface Fitting via Monomial Symmetrization. J. Chem. Theory Comput. 2010
2010
Earlier work this paper cites.
Ferguson, A. L.; Panagiotopoulos, A. Z.; Debenedetti, P. G.; Kevrekidis, I. G. Systematic Determination of Order Parameters for Chain Dynamics Using Diffusion Maps. Proc. Natl. Acad. Sci. U. S. A. 2010
2010
Earlier work this paper cites.
Angioletti-Uberti, S.; Ceriotti, M.; Lee, P. D.; Finnis, M. W. Solid-Liquid Interface Free Energy through Metadynamics Simulations. Phys. Rev. B - Condens. Matter Mater. Phys. 2010
2010
Earlier work this paper cites.
Valle, M.; Oganov, A. R. Crystal Fingerprint Space – a Novel Paradigm for Studying Crystal-Structure Sets. Acta Crystallogr A Found Crystallogr 2010
2010
Earlier work this paper cites.
Amsler, M.; Goedecker, S. Crystal Structure Prediction Using the Minima Hopping Method. J. Chem. Phys. 2010
2010
Earlier work this paper cites.
Andrade, C. H.; Pasqualoto, K. F. M.; Ferreira, E. I.; Hopfinger, A. J. 4D-QSAR: Perspectives in Drug Design. Molecules 2010
2010
Earlier work this paper cites.
Morales, M. A.; Pierleoni, C.; Schwegler, E.; Ceperley, D. M. Evidence for a First-Order Liquid-Liquid Transition in High-Pressure Hydrogen from Ab Initio Simulations. Proc. Natl. Acad. Sci. U. S. A. 2010
2010
Earlier work this paper cites.
Ceriotti, M.; Tribello, G. A.; Parrinello, M. Simplifying the Representation of Complex Free-Energy Landscapes Using Sketch-Map. Proc. Natl. Acad. Sci. U. S. A. 2011
2011
Earlier work this paper cites.
Spiwok, V.; Králová, B. Metadynamics in the Conformational Space Nonlinearly Dimensionally Reduced by Isomap. J. Chem. Phys. 2011
2011
Earlier work this paper cites.
Kamerlin, S. C. L.; Warshel, A. The Empirical Valence Bond Model: Theory and Applications. WIREs Comput Mol Sci 2011
2011
Earlier work this paper cites.
Pietrucci, F.; Andreoni, W. Graph Theory Meets Ab Initio Molecular Dynamics: Atomic Structures and Transformations at the Nanoscale. Phys. Rev. Lett. 2011
2011
Earlier work this paper cites.
Behler, J. Atom-Centered Symmetry Functions for Constructing High-Dimensional Neural Network Potentials. The Journal of Chemical Physics 2011
2011
Earlier work this paper cites.
Behler, J. Neural Network Potential-Energy Surfaces in Chemistry: A Tool for Large-Scale Simulations. Phys. Chem. Chem. Phys. PCCP 2011
2011
Earlier work this paper cites.
Artrith, N.; Morawietz, T.; Behler, J. High-Dimensional Neural-Network Potentials for Multicomponent Systems: Applications to Zinc Oxide. Phys. Rev. B 2011
2011
Earlier work this paper cites.
Rohrdanz, M. A.; Zheng, W.; Maggioni, M.; Clementi, C. Determination of Reaction Coordinates via Locally Scaled Diffusion Map. J. Chem. Phys. 2011
2011
Earlier work this paper cites.
Pietropaolo, A.; Branduardi, D.; Bonomi, M.; Parrinello, M. A Chirality-Based Metrics for Free-Energy Calculations in Biomolecular Systems. J. Comput. Chem. 2011
2011
Earlier work this paper cites.
Pickard, C. J.; Needs, R. J. Ab Initio Random Structure Searching. J. Phys. Condens. Matter 2011
2011
Earlier work this paper cites.
Sosso, G. C.; Miceli, G.; Caravati, S.; Behler, J.; Bernasconi, M. Neural Network Interatomic Potential for the Phase Change Material GeTe. Phys. Rev. B 2012
2012
Earlier work this paper cites.
Rupp, M.; Tkatchenko, A.; Müller, K.-R.; von Lilienfeld, O. A. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. Phys. Rev. Lett. 2012
2012
Earlier work this paper cites.
Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. P. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research 2012
2012
Earlier work this paper cites.
Mysinger, M. M.; Carchia, M.; Irwin, J. J.; Shoichet, B. K. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. Journal of Medicinal Chemistry 2012
2012
Earlier work this paper cites.
Burke, K. Perspective on Density Functional Theory. J. Chem. Phys. 2012
2012
Earlier work this paper cites.
Moussa, J. E. Comment on “Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning”. Phys. Rev. Lett. 2012
2012
Earlier work this paper cites.
Montavon, G.; Hansen, K.; Fazli, S.; Rupp, M.; Biegler, F.; Ziehe, A.; Tkatchenko, A.; Lilienfeld, A. V.; Müller, K.-R. In Advances in Neural Information Processing Systems 25 ; Pereira, F., Burges, C. J. C., Bottou, L., Weinberger, K. Q., Eds.; Curran Associates, Inc., 2012; pp 440–448
2012
Earlier work this paper cites.
Vilhelmsen, L. B.; Hammer, B. Systematic Study of Au 6 to Au 12 Gold Clusters on MgO(100) F Centers Using Density-Functional Theory. Phys. Rev. Lett. 2012
2012
Earlier work this paper cites.
Richard, R. M.; Herbert, J. M. A Generalized Many-Body Expansion and a Unified View of Fragment-Based Methods in Electronic Structure Theory. The Journal of Chemical Physics 2012
2012
Earlier work this paper cites.
Kakarala, R. The Bispectrum as a Source of Phase-Sensitive Invariants for Fourier Descriptors: A Group-Theoretic Approach. J. Math. Imaging Vis. 2012
2012
Earlier work this paper cites.
Kakarala, R. The Bispectrum as a Source of Phase-Sensitive Invariants for Fourier Descriptors: A Group-Theoretic Approach. J Math Imaging Vis 2012
2012
Earlier work this paper cites.
Dastmalchi, S.; Hamzeh-Mivehroud, M.; Asadpour-Zeynali, K. Comparison of different 2D and 3D-QSAR methods on activity prediction of histamine H3 receptor antagonists. Iranian Journal of Pharmaceutical Research 2012
2012
Earlier work this paper cites.
Rohrdanz, M. A.; Zheng, W.; Clementi, C. Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions. Annu. Rev. Phys. Chem. 2013
2013
Cited alongside, same era.
Bartók, A. P.; Kondor, R.; Csányi, G. On Representing Chemical Environments. Phys. Rev. B 2013
2013
Cited alongside, same era.
Booth, G. H.; Grüneis, A.; Kresse, G.; Alavi, A. Towards an Exact Description of Electronic Wavefunctions in Real Solids. Nature 2013
2013
Cited alongside, same era.
Sadeghi, A.; Ghasemi, S. A.; Schaefer, B.; Mohr, S.; Lill, M. A.; Goedecker, S. Metrics for Measuring Distances in Configuration Spaces. J. Chem. Phys. 2013
2013
Cited alongside, same era.
Jiang, B.; Guo, H. Permutation Invariant Polynomial Neural Network Approach to Fitting Potential Energy Surfaces. The Journal of Chemical Physics 2013
Deringer, V. L.; Caro, M. A.; Jana, R.; Aarva, A.; Elliott, S. R.; Laurila, T.; Csányi, G.; Pastewka, L. Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory. Chem. Mater. 2018
2018
Later among the works it cites.
Choudhary, K.; DeCost, B.; Tavazza, F. Machine Learning with Force-Field-Inspired Descriptors for Materials: Fast Screening and Mapping Energy Landscape. Phys. Rev. Materials 2018
2018
Later among the works it cites.
Caro, M. A.; Aarva, A.; Deringer, V. L.; Csányi, G.; Laurila, T. Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning. Chem. Mater. 2018
2018
Later among the works it cites.
Welborn, M.; Cheng, L.; Miller, T. F. Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis. J. Chem. Theory Comput. 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2013
Cited alongside, same era.
Hansen, K.; Montavon, G.; Biegler, F.; Fazli, S.; Rupp, M.; Scheffler, M.; von Lilienfeld, O. A.; Tkatchenko, A.; Müller, K.-R. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. J. Chem. Theory Comput. 2013
2013
Cited alongside, same era.
Barros, K.; Kato, Y. Efficient Langevin Simulation of Coupled Classical Fields and Fermions. Phys. Rev. B 2013
2013
Cited alongside, same era.
Mickel, W.; Kapfer, S. C.; Schröder-Turk, G. E.; Mecke, K. Shortcomings of the Bond Orientational Order Parameters for the Analysis of Disordered Particulate Matter. The Journal of Chemical Physics 2013
2013
Cited alongside, same era.
Montavon, G.; Rupp, M.; Gobre, V.; Vazquez-Mayagoitia, A.; Hansen, K.; Tkatchenko, A.; Müller, K. R.; Anatole Von Lilienfeld, O. Machine Learning of Molecular Electronic Properties in Chemical Compound Space. New J. Phys. 2013
2013
Cited alongside, same era.
Cuturi, M. In Advances in Neural Information Processing Systems 26 ; Burges, C. J. C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K. Q., Eds.; Curran Associates, Inc., 2013; pp 2292–2300
2013
Cited alongside, same era.
Stewart, J. J. P. Optimization of Parameters for Semiempirical Methods VI: More Modifications to the NDDO Approximations and Re-Optimization of Parameters. J Mol Model 2013
2013
Cited alongside, same era.
Ceriotti, M.; Tribello, G. A.; Parrinello, M. Demonstrating the Transferability and the Descriptive Power of Sketch-Map. J. Chem. Theory Comput. 2013
2013
Cited alongside, same era.
Kanekal, K. H.; Bereau, T. Resolution limit of data-driven coarse-grained models spanning chemical space. Journal of Chemical Physics 2019
2019
Later among the works it cites.
Jackson, N. E.; Bowen, A. S.; Antony, L. W.; Webb, M. A.; Vishwanath, V.; de Pablo, J. J. Electronic structure at coarse-grained resolutions from supervised machine learning. Science Advances 2019
2019
Later among the works it cites.
Wang, J.; Olsson, S.; Wehmeyer, C.; Pérez, A.; Charron, N. E.; De Fabritiis, G.; Noé, F.; Clementi, C. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields. ACS Central Science 2019
2019
Later among the works it cites.
Grisafi, A.; Fabrizio, A.; Meyer, B.; Wilkins, D. M.; Corminboeuf, C.; Ceriotti, M. Transferable Machine-Learning Model of the Electron Density. ACS Cent. Sci. 2019
2019
Later among the works it cites.
Wilkins, D. M.; Grisafi, A.; Yang, Y.; Lao, K. U.; DiStasio, R. A.; Ceriotti, M. Accurate Molecular Polarizabilities with Coupled Cluster Theory and Machine Learning. Proc. Natl. Acad. Sci. U. S. A. 2019
2019
Later among the works it cites.
Schütt, K. T.; Gastegger, M.; Tkatchenko, A.; Müller, K.-R.; Maurer, R. J. Unifying Machine Learning and Quantum Chemistry with a Deep Neural Network for Molecular Wavefunctions. Nat Commun 2019
2019
Later among the works it cites.
Chen, C.; Ye, W.; Zuo, Y.; Zheng, C.; Ong, S. P. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. Chem. Mater. 2019
2019
Later among the works it cites.
Bernstein, N.; Bhattarai, B.; Csányi, G.; Drabold, D. A.; Elliott, S. R.; Deringer, V. L. Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon. Angew. Chem. Int. Ed. 2019
2019
Later among the works it cites.
Willatt, M. J.; Musil, F.; Ceriotti, M. Atom-Density Representations for Machine Learning. J. Chem. Phys. 2019
2019
Later among the works it cites.
Stuke, A.; Todorović, M.; Rupp, M.; Kunkel, C.; Ghosh, K.; Himanen, L.; Rinke, P. Chemical Diversity in Molecular Orbital Energy Predictions with Kernel Ridge Regression. J. Chem. Phys. 2019
2019
Later among the works it cites.
Zheng, S.; Yan, X.; Yang, Y.; Xu, J. Identifying Structure–Property Relationships through SMILES Syntax Analysis with Self-Attention Mechanism. J. Chem. Inf. Model. 2019
2019
Later among the works it cites.
Shin, B.; Park, S.; Kang, K.; Ho, J. C. Self-attention based molecule representation for predicting drug-target interaction. Machine Learning for Healthcare Conference. 2019; pp 230–248
2019
Later among the works it cites.
Drautz, R. Atomic Cluster Expansion for Accurate and Transferable Interatomic Potentials. Phys. Rev. B 2019
2019
Later among the works it cites.
Bachmayr, M.; Csanyi, G.; Drautz, R.; Dusson, G.; Etter, S.; van der Oord, C.; Ortner, C. Approximation of Atomic Interactions with Spherical Harmonics. 2019
2019
Later among the works it cites.
Noh, J.; Kim, J.; Stein, H. S.; Sanchez-Lengeling, B.; Gregoire, J. M.; Aspuru-Guzik, A.; Jung, Y. Inverse Design of Solid-State Materials via a Continuous Representation. Matter 2019
2019
Later among the works it cites.
Anderson, B.; Hy, T. S.; Kondor, R. Cormorant: Covariant Molecular Neural Networks. NeurIPS. 2019; p 10
2019
Later among the works it cites.
Grisafi, A.; Wilkins, D. M.; Willatt, M. J.; Ceriotti, M. In Machine Learning in Chemistry ; Pyzer-Knapp, E. O., Laino, T., Eds.; American Chemical Society: Washington, DC, 2019; Vol. 1326; pp 1–21
2019
Later among the works it cites.
Raimbault, N.; Grisafi, A.; Ceriotti, M.; Rossi, M. Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals. New J. Phys. 2019
2019
Later among the works it cites.
Yu, Q.; Bowman, J. M. Classical, Thermostated Ring Polymer, and Quantum VSCF/VCI Calculations of IR Spectra of H 7 O 3 + and H 9 O 4 + (Eigen) and Comparison with Experiment. J. Phys. Chem. A 2019
2019
Later among the works it cites.
Christensen, A. S.; Faber, F. A.; von Lilienfeld, O. A. Operators in Quantum Machine Learning: Response Properties in Chemical Space. J. Chem. Phys. 2019
2019
Later among the works it cites.
Batra, R.; Tran, H. D.; Kim, C.; Chapman, J.; Chen, L.; Chandrasekaran, A.; Ramprasad, R. General Atomic Neighborhood Fingerprint for Machine Learning-Based Methods. J. Phys. Chem. C 2019
2019
Later among the works it cites.
Musil, F.; Willatt, M. J.; Langovoy, M. A.; Ceriotti, M. Fast and Accurate Uncertainty Estimation in Chemical Machine Learning. J. Chem. Theory Comput. 2019
2019
Later among the works it cites.
Veit, M.; Jain, S. K.; Bonakala, S.; Rudra, I.; Hohl, D.; Csányi, G. Equation of State of Fluid Methane from First Principles with Machine Learning Potentials. J. Chem. Theory Comput. 2019
2019
Later among the works it cites.
Caro, M. A. Optimizing Many-Body Atomic Descriptors for Enhanced Computational Performance of Machine Learning Based Interatomic Potentials. Phys. Rev. B 2019
2019
Later among the works it cites.
Mills, K.; Ryczko, K.; Luchak, I.; Domurad, A.; Beeler, C.; Tamblyn, I. Extensive deep neural networks for transferring small scale learning to large scale systems. Chemical Science 2019
2019
Later among the works it cites.
Seko, A.; Togo, A.; Tanaka, I. Group-Theoretical High-Order Rotational Invariants for Structural Representations: Application to Linearized Machine Learning Interatomic Potential. Phys. Rev. B 2019
2019
Later among the works it cites.
Lemke, T.; Peter, C. EncoderMap: Dimensionality Reduction and Generation of Molecule Conformations. J. Chem. Theory Comput. 2019
2019
Later among the works it cites.
Helfrecht, B. A.; Semino, R.; Pireddu, G.; Auerbach, S. M.; Ceriotti, M. A New Kind of Atlas of Zeolite Building Blocks. J. Chem. Phys. 2019
2019
Later among the works it cites.
Ceriotti, M. Unsupervised Machine Learning in Atomistic Simulations, between Predictions and Understanding. J. Chem. Phys. 2019
2019
Later among the works it cites.
Oganov, A. R.; Pickard, C. J.; Zhu, Q.; Needs, R. J. Structure Prediction Drives Materials Discovery. Nat Rev Mater 2019
2019
Later among the works it cites.
Helfrecht, B. A.; Gasparotto, P.; Giberti, F.; Ceriotti, M. Atomic Motif Recognition in (Bio)Polymers: Benchmarks from the Protein Data Bank. Front. Mol. Biosci. 2019
2019
Later among the works it cites.
Engel, E. A.; Anelli, A.; Hofstetter, A.; Paruzzo, F.; Emsley, L.; Ceriotti, M. A Bayesian Approach to NMR Crystal Structure Determination. Phys. Chem. Chem. Phys. 2019
2019
Later among the works it cites.
Gao, H.; Wang, J.; Sun, J. Improve the Performance of Machine-Learning Potentials by Optimizing Descriptors. J. Chem. Phys. 2019
2019
Later among the works it cites.
Singraber, A.; Morawietz, T.; Behler, J.; Dellago, C. Parallel Multistream Training of High-Dimensional Neural Network Potentials. J. Chem. Theory Comput. 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
Yang, Y.; Lao, K.-U.; Wilkins, D. M.; Grisafi, A.; Ceriotti, M. Quantum Mechanical Static Dipole Polarizabilities in the QM7b and AlphaML Showcase Databases. Sci Data 2019
2019
Later among the works it cites.
Chandrasekaran, A.; Kamal, D.; Batra, R.; Kim, C.; Chen, L.; Ramprasad, R. Solving the Electronic Structure Problem with Machine Learning. npj Comput Mater 2019
2019
Later among the works it cites.
Fabrizio, A.; Grisafi, A.; Meyer, B.; Ceriotti, M.; Corminboeuf, C. Electron Density Learning of Non-Covalent Systems. Chem. Sci. 2019
2019
Later among the works it cites.
Würger, T.; Feiler, C.; Musil, F.; Feldbauer, G. B. V.; Höche, D.; Lamaka, S. V.; Zheludkevich, M. L.; Meißner, R. H. Data Science Based Mg Corrosion Engineering. Front. Mater. 2019
2019
Later among the works it cites.
Homer, E. R.; Hensley, D. M.; Rosenbrock, C. W.; Nguyen, A. H.; Hart, G. L. W. Machine-Learning Informed Representations for Grain Boundary Structures. Front. Mater. 2019
2019
Later among the works it cites.
Schwalbe-Koda, D.; Jensen, Z.; Olivetti, E.; Gómez-Bombarelli, R. Graph Similarity Drives Zeolite Diffusionless Transformations and Intergrowth. Nat. Mater. 2019
2019
Later among the works it cites.
Fulford, M.; Salvalaglio, M.; Molteni, C. DeepIce: A Deep Neural Network Approach To Identify Ice and Water Molecules. J. Chem. Inf. Model. 2019
2019
Later among the works it cites.
Huang, J.-X.; Csányi, G.; Zhao, J.-B.; Cheng, J.; Deringer, V. L. First-Principles Study of Alkali-Metal Intercalation in Disordered Carbon Anode Materials. J. Mater. Chem. A 2019
2019
Later among the works it cites.
Bernstein, N.; Csányi, G.; Deringer, V. L. De Novo Exploration and Self-Guided Learning of Potential-Energy Surfaces. npj Comput Mater 2019
2019
Later among the works it cites.
Aarva, A.; Deringer, V. L.; Sainio, S.; Laurila, T.; Caro, M. A. Understanding X-Ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra. Chem. Mater. 2019
2019
Later among the works it cites.
Dick, S.; Fernandez-Serra, M. Learning from the Density to Correct Total Energy and Forces in First Principle Simulations. J. Chem. Phys. 2019
2019
Later among the works it cites.
Cheng, L.; Welborn, M.; Christensen, A. S.; Miller, T. F. A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules. J. Chem. Phys. 2019
2019
Later among the works it cites.
Cheng, B.; Mazzola, G.; Pickard, C. J.; Ceriotti, M. Evidence for Supercritical Behaviour of High-Pressure Liquid Hydrogen. Nature 2020
2020
Later among the works it cites.
Manzhos, S.; Carrington, T. Neural Network Potential Energy Surfaces for Small Molecules and Reactions. Chem. Rev. 2020
2020
Later among the works it cites.
Westermayr, J.; Marquetand, P. Machine Learning for Electronically Excited States of Molecules. Chem. Rev. 2020
2020
Later among the works it cites.
Wills, T. J.; Polshakov, D. A.; Robinson, M. C.; Lee, A. A. Impact of Chemist-In-The-Loop Molecular Representations on Machine Learning Outcomes. J. Chem. Inf. Model. 2020
2020
Later among the works it cites.
van der Oord, C.; Dusson, G.; Csányi, G.; Ortner, C. Regularised Atomic Body-Ordered Permutation-Invariant Polynomials for the Construction of Interatomic Potentials. Mach. Learn. Sci. Technol. 2020
2020
Later among the works it cites.
Çaylak, O.; von Lilienfeld, A.; Baumeier, B. Wasserstein Metric for Improved Quantum Machine Learning with Adjacency Matrix Representations. Mach. Learn.: Sci. Technol. 2020
2020
Later among the works it cites.
Jung, H.; Stocker, S.; Kunkel, C.; Oberhofer, H.; Han, B.; Reuter, K.; Margraf, J. T. Size-Extensive Molecular Machine Learning with Global Representations. ChemSystemsChem 2020
2020
Later among the works it cites.
Christensen, A. S.; Bratholm, L. A.; Faber, F. A.; Anatole von Lilienfeld, O. FCHL Revisited: Faster and More Accurate Quantum Machine Learning. J. Chem. Phys. 2020
2020
Later among the works it cites.
Pozdnyakov, S. N.; Willatt, M. J.; Bartók, A. P.; Ortner, C.; Csányi, G.; Ceriotti, M. Incompleteness of Atomic Structure Representations. Phys. Rev. Lett. 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
Christiansen, M.-P. V.; Mortensen, H. L.; Meldgaard, S. A.; Hammer, B. Gaussian Representation for Image Recognition and Reinforcement Learning of Atomistic Structure. J. Chem. Phys. 2020
2020
Later among the works it cites.
Nigam, J.; Pozdnyakov, S.; Ceriotti, M. Recursive Evaluation and Iterative Contraction of N -Body Equivariant Features. J. Chem. Phys. 2020
2020
Later among the works it cites.
Drautz, R. Atomic Cluster Expansion of Scalar, Vectorial, and Tensorial Properties Including Magnetism and Charge Transfer. Phys. Rev. B 2020
2020
Later among the works it cites.
Scherer, C.; Scheid, R.; Andrienko, D.; Bereau, T. Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids. J. Chem. Theory Comput. 2020
2020
Later among the works it cites.
Veit, M.; Wilkins, D. M.; Yang, Y.; DiStasio, R. A.; Ceriotti, M. Predicting Molecular Dipole Moments by Combining Atomic Partial Charges and Atomic Dipoles. J. Chem. Phys. 2020
2020
Later among the works it cites.
Zhang, Y.; Ye, S.; Zhang, J.; Hu, C.; Jiang, J.; Jiang, B. Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties. J. Phys. Chem. B 2020
2020
Later among the works it cites.
Zhang, L.; Chen, M.; Wu, X.; Wang, H.; E, W.; Car, R. Deep Neural Network for the Dielectric Response of Insulators. Phys. Rev. B 2020
2020
Later among the works it cites.
Metcalf, D. P.; Koutsoukas, A.; Spronk, S. A.; Claus, B. L.; Loughney, D. A.; Johnson, S. R.; Cheney, D. L.; Sherrill, C. D. Approaches for Machine Learning Intermolecular Interaction Energies and Application to Energy Components from Symmetry Adapted Perturbation Theory. J. Chem. Phys. 2020
2020
Later among the works it cites.
Jinnouchi, R.; Karsai, F.; Verdi, C.; Asahi, R.; Kresse, G. Descriptors Representing Two- and Three-Body Atomic Distributions and Their Effects on the Accuracy of Machine-Learned Inter-Atomic Potentials. J. Chem. Phys. 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
Onat, B.; Ortner, C.; Kermode, J. R. Sensitivity and Dimensionality of Atomic Environment Representations Used for Machine Learning Interatomic Potentials. J. Chem. Phys. 2020
2020
Later among the works it cites.
Parsaeifard, B.; De, D. S.; Christensen, A. S.; Faber, F. A.; Kocer, E.; De, S.; Behler, J.; von Lilienfeld, A.; Goedecker, S. An Assessment of the Structural Resolution of Various Fingerprints Commonly Used in Machine Learning. Mach. Learn.: Sci. Technol. 2020
2020
Later among the works it cites.
Helfrecht, B. A.; Cersonsky, R. K.; Fraux, G.; Ceriotti, M. Structure-Property Maps with Kernel Principal Covariates Regression. Mach. Learn.: Sci. Technol. 2020
2020
Later among the works it cites.
Zuo, Y.; Chen, C.; Li, X.; Deng, Z.; Chen, Y.; Behler, J.; Csányi, G.; Shapeev, A. V.; Thompson, A. P.; Wood, M. A.; Ong, S. P. Performance and Cost Assessment of Machine Learning Interatomic Potentials. J. Phys. Chem. A 2020
2020
Later among the works it cites.
Pozdnyakov, S.; Willatt, M.; Ceriotti, M. Dataset: Randomly-Displaced Methane Configurations. https://archive.materialscloud.org/record/2020.110 , 2020; (accessed 2020-11-05)
2020
Later among the works it cites.
Ben Mahmoud, C.; Anelli, A.; Csányi, G.; Ceriotti, M. Learning the Electronic Density of States in Condensed Matter. Phys. Rev. B 2020
2020
Later among the works it cites.
Gao, X.; Ramezanghorbani, F.; Isayev, O.; Smith, J. S.; Roitberg, A. E. TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials. Journal of chemical information and modeling 2020
2020
Later among the works it cites.
Lu, D.; Wang, H.; Chen, M.; Liu, J.; Lin, L.; Car, R.; E, W.; Jia, W.; Zhang, L. 86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy. 2020
2020
Later among the works it cites.
Himanen, L.; Jäger, M. O. J.; Morooka, E. V.; Federici Canova, F.; Ranawat, Y. S.; Gao, D. Z.; Rinke, P.; Foster, A. S. DScribe: Library of descriptors for machine learning in materials science. Computer Physics Communications 2020
2020
Later among the works it cites.
Musil, F.; Veit, M.; Junge, T.; Stricker, M.; Goscinki, A.; Fraux, G.; Ceriotti, M. LIBRASCAL. https://github.com/cosmo-epfl/librascal , 2020; https://github.com/cosmo-epfl/librascal
2020
Later among the works it cites.
Kapil, V.; Wilkins, D. M.; Lan, J.; Ceriotti, M. Inexpensive Modeling of Quantum Dynamics Using Path Integral Generalized Langevin Equation Thermostats. J. Chem. Phys. 2020
2020
Later among the works it cites.
Fabrizio, A.; Briling, K. R.; Girardier, D. D.; Corminboeuf, C. Learning On-Top: Regressing the on-Top Pair Density for Real-Space Visualization of Electron Correlation. J. Chem. Phys. 2020
2020
Later among the works it cites.
Rowe, P.; Deringer, V. L.; Gasparotto, P.; Csányi, G.; Michaelides, A. An Accurate and Transferable Machine Learning Potential for Carbon. J. Chem. Phys. 2020
2020
Later among the works it cites.
Maksimov, D.; Baldauf, C.; Rossi, M. The Conformational Space of a Flexible Amino Acid at Metallic Surfaces. Int J Quantum Chem 2020
2020
Later among the works it cites.
Yang, J. Mapping Temperature-Dependent Energy–Structure–Property Relationships for Solid Solutions of Inorganic Halide Perovskites. J. Mater. Chem. C 2020
2020
Later among the works it cites.
Gasparotto, P.; Bochicchio, D.; Ceriotti, M.; Pavan, G. M. Identifying and Tracking Defects in Dynamic Supramolecular Polymers. J. Phys. Chem. B 2020
2020
Later among the works it cites.
Nicholas, T. C.; Goodwin, A. L.; Deringer, V. L. Understanding the Geometric Diversity of Inorganic and Hybrid Frameworks through Structural Coarse-Graining. Chem. Sci. 2020
2020
Later among the works it cites.
Zhou, Y.; Sun, L.; Zewdie, G. M.; Mazzarello, R.; Deringer, V. L.; Ma, E.; Zhang, W. Bonding Similarities and Differences between Y–Sb–Te and Sc–Sb–Te Phase-Change Memory Materials. J. Mater. Chem. C 2020
2020
Later among the works it cites.
Caro, M. A.; Csányi, G.; Laurila, T.; Deringer, V. L. Machine Learning Driven Simulated Deposition of Carbon Films: From Low-Density to Diamondlike Amorphous Carbon. Phys. Rev. B 2020
2020
Later among the works it cites.
Reinhardt, A.; Pickard, C. J.; Cheng, B. Predicting the Phase Diagram of Titanium Dioxide with Random Search and Pattern Recognition. Phys. Chem. Chem. Phys. 2020
2020
Later among the works it cites.
Basdogan, Y.; Groenenboom, M. C.; Henderson, E.; De, S.; Rempe, S. B.; Keith, J. A. Machine Learning-Guided Approach for Studying Solvation Environments. J. Chem. Theory Comput. 2020
2020
Later among the works it cites.
Monserrat, B.; Brandenburg, J. G.; Engel, E. A.; Cheng, B. Liquid Water Contains the Building Blocks of Diverse Ice Phases. Nat Commun 2020
2020
Later among the works it cites.
Deringer, V. L.; Caro, M. A.; Csányi, G. A General-Purpose Machine-Learning Force Field for Bulk and Nanostructured Phosphorus. Nat Commun 2020
2020
Later among the works it cites.
Shen, C.; Ding, J.; Wang, Z.; Cao, D.; Ding, X.; Hou, T. From machine learning to deep learning: Advances in scoring functions for protein–ligand docking. WIREs Computational Molecular Science 2020
2020
Later among the works it cites.
Zankov, D.; V.,; Matveieva, M.; Nikonenko, A.; Nugmanov, R.; Varnek, A.; Polishchuk, P.; Madzhidov, T. QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach. 2020
2020
Later among the works it cites.
Axelrod, S.; Gomez-Bombarelli, R. Molecular machine learning with conformer ensembles. 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
Deringer, V. L.; Bernstein, N.; Csányi, G.; Ben Mahmoud, C.; Ceriotti, M.; Wilson, M.; Drabold, D. A.; Elliott, S. R. Origins of Structural and Electronic Transitions in Disordered Silicon. Nature 2021
2021
Closest in time.
Kalita, B.; Li, L.; McCarty, R. J.; Burke, K. Learning to Approximate Density Functionals. Acc. Chem. Res. 2021
2021
Closest in time.
Deringer, V. L.; Bartók, A. P.; Noam Bernstein, D. M. W.; Ceriotti, M.; Csányi, G. Gaussian Process Regression for Materials and Molecules. Chem. Rev. (under review) 2021
2021
Closest in time.
Unke, O. T.; Chmiela, S.; Sauceda, H. E.; Gastegger, M.; Poltavsky, I.; Schütt, K. T.; Tkatchenko, A.; Müller, K.-R. Machine Learning Force Fields. Chem. Rev. 2021
2021
Closest in time.
Behler, J. Four Generations of High-Dimensional Neural Network Potentials. Chem. Rev. 2021
2021
Closest in time.
Glielmo, A.; Husic, B. E.; Rodriguez, A.; Clementi, C.; Noé, F.; Laio, A. Unsupervised Learning Methods for Molecular Simulation Data. Chem. Rev. 2021
2021
Closest in time.
Yue, S.; Muniz, M. C.; Calegari Andrade, M. F.; Zhang, L.; Car, R.; Panagiotopoulos, A. Z. When Do Short-Range Atomistic Machine-Learning Models Fall Short? J. Chem. Phys. 2021
2021
Closest in time.
Uhrin, M. Through the eyes of a descriptor: Constructing complete, invertible, descriptions of atomic environments. ArXiV e-prints 2021
2021
Closest in time.
Zamani, M.; Imbalzano, G.; Tappy, N.; Alexander, D. T. L.; Martí-Sánchez, S.; Ghisalberti, L.; Ramasse, Q. M.; Friedl, M.; Tütüncüoglu, G.; Francaviglia, L.; Bienvenue, S.; Hébert, C.; Arbiol, J.; Ceriotti, M.; Fontcuberta I Morral, A. Dataset: 3D Ordering at the Liquid–Solid Polar Interface of Nanowires. https://archive.materialscloud.org/record/2020.141 , 2020; (accessed 2021-01-07)
2021
Closest in time.
Goscinski, A.; Fraux, G.; Imbalzano, G.; Ceriotti, M. The Role of Feature Space in Atomistic Learning. Mach. Learn.: Sci. Technol. 2021
2021
Closest in time.
2021
Closest in time.
Cersonsky, R. K.; Helfrecht, B.; Engel, E. A.; Kliavinek, S.; Ceriotti, M. Improving Sample and Feature Selection with Principal Covariates Regression. Mach. Learn.: Sci. Technol. 2021
2021
Closest in time.
Musil, F.; Veit, M.; Goscinski, A.; Fraux, G.; Willatt, M. J.; Stricker, M.; Ceriotti, M. Efficient Implementation of Atom-Density Representations. J. Chem. Phys. 2021
2021
Closest in time.
Novikov, I. S.; Gubaev, K.; Podryabinkin, E. V.; Shapeev, A. V. The MLIP Package: Moment Tensor Potentials with MPI and Active Learning. Mach. Learn.: Sci. Technol. 2021
2021
Closest in time.
Capelli, R.; Gardin, A.; Empereur-mot, C.; Doni, G.; Pavan, G. M. A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields. ChemRxiv:14039834.v3 2021
2021
Closest in time.
Weinreich, J.; Browning, N. J.; von Lilienfeld, O. A. Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation. J. Chem. Phys. 2021
2021
Closest in time.
Gallarati, S.; Fabregat, R.; Laplaza, R.; Bhattacharjee, S.; Wodrich, M. D.; Corminboeuf, C. Reaction-Based Machine Learning Representations for Predicting the Enantioselectivity of Organocatalysts. Chem. Sci. 2021
2021
Closest in time.
Fung, V.; Hu, G.; Ganesh, P.; Sumpter, B. G. Machine Learned Features from Density of States for Accurate Adsorption Energy Prediction. Nat Commun 2021
2021
Closest in time.
Grisafi, A.; Nigam, J.; Ceriotti, M. Multi-Scale Approach for the Prediction of Atomic Scale Properties. Chem. Sci. 2021
2090
Closest in time.