Localized Coulomb Descriptors for the Gaussian Approximation Potential

Atoms, Chemical Bonds, and Bond Dissociation Energies

S. Fliszár · 1994

Earlier work this paper cites.

Density functional and density matrix method scaling linearly with the number of atoms

W. Kohn · 1996

Earlier work this paper cites.

Nearsightedness of electronic matter

E. Prodan and W. Kohn · 2005

Earlier work this paper cites.

Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance

C. J. Willmott and K. Matsuura · 2005

Earlier work this paper cites.

Gaussian Processes for Machine Learning

C. E. Rasmussen and C. K. I. Williams · 2006

Earlier work this paper cites.

Numerical Simulation in Molecular Dynamics: Numerics, Algorithms, Parallelization, Applications

M. Griebel, S. Knapek, and G. Zumbusch · 2007

Earlier work this paper cites.

970 million druglike small molecules for virtual screening in the chemical universe database GDB-13

L. C. Blum and J.-L. Reymond · 2009

Earlier work this paper cites.

Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons

A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi · 2010

Cited alongside, same era.

Modeling Materials: Continuum, Atomistic and Multiscale Techniques

E. Tadmor and R. Miller · 2011

Cited alongside, same era.

Learning invariant representations of molecules for atomization energy prediction

G. Montavon, K. Hansen, S. Fazli, M. Rupp, F. Biegler, A. Ziehe, A. Tkatchenko, O. A. von Lilienfeld, and K.-R. Müller · 2012

Cited alongside, same era.

Computational aspects of many-body potentials

S. J. Plimpton and A. P. Thompson · 2012

Cited alongside, same era.

Fast and accurate modeling of molecular atomization energies with machine learning

M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. von Lilienfeld · 2012

Cited alongside, same era.

On representing chemical environments

A. P. Bartók, R. Kondor, and G. Csányi · 2013

Machine learning of molecular electronic properties in chemical compound space

G. Montavon, M. Rupp, V. Gobre, A. Vazquez-Mayagoitia, K. Hansen, A. Tkatchenko, K.-R. Müller, and O. A. von Lilienfeld · 2013

Later among the works it cites.

Quantum chemistry structures and properties of 134 kilo molecules

R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. von Lilienfeld · 2014

Later among the works it cites.

Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

K. Hansen, F. Biegler, R. Ramakrishnan, W. Pronobis, O. A. von Lilienfeld, K.-R. Müller, and A. Tkatchenko · 2015

Later among the works it cites.

Machine learning for quantum mechanics in a nutshell

M. Rupp · 2015

Later among the works it cites.

Machine learning for quantum mechanical properties of atoms in molecules

M. Rupp, R. Ramakrishnan, and O. A. von Lilienfeld · 2015

Later among the works it cites.