Quantum Machine Learning im chemischen Raum

A.P. Thompson, L.P. Swiler, C.R. Trott, S.M. Foiles, G.J. Tucker, Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials Journal of Computational Physics. ,vol. 285, pp. 316- 330 ,(2015) , 10.1016/J.JCP.2014.12.018

Michael Lewis, Moneyball: The Art of Winning an Unfair Game ,(2003)

Alexander Golbraikh, Min Shen, Zhiyan Xiao, Yun-De Xiao, Kuo-Hsiung Lee, Alexander Tropsha, Rational selection of training and test sets for the development of validated QSAR models. Journal of Computer-aided Molecular Design. ,vol. 17, pp. 241- 253 ,(2003) , 10.1023/A:1025386326946

S. Alireza Ghasemi, Albert Hofstetter, Santanu Saha, Stefan Goedecker, Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network Physical Review B. ,vol. 92, pp. 045131- ,(2015) , 10.1103/PHYSREVB.92.045131

Raghunathan Ramakrishnan, Mia Hartmann, Enrico Tapavicza, O. Anatole von Lilienfeld, Electronic spectra from TDDFT and machine learning in chemical space The Journal of Chemical Physics. ,vol. 143, pp. 084111- 084111 ,(2015) , 10.1063/1.4928757

Tristan Bereau, Denis Andrienko, O. Anatole von Lilienfeld, Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules Journal of Chemical Theory and Computation. ,vol. 11, pp. 3225- 3233 ,(2015) , 10.1021/ACS.JCTC.5B00301

Matthias Rupp, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld, Machine Learning for Quantum Mechanical Properties of Atoms in Molecules Journal of Physical Chemistry Letters. ,vol. 6, pp. 3309- 3313 ,(2015) , 10.1021/ACS.JPCLETT.5B01456

Felix A. Faber, Alexander Lindmaa, O. Anatole von Lilienfeld, Rickard Armiento, Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals. Physical Review Letters. ,vol. 117, pp. 135502- ,(2016) , 10.1103/PHYSREVLETT.117.135502

Jian Sun, Jiang Wu, Tao Song, LiHong Hu, KaiLu Shan, GuanHua Chen, Alternative approach to chemical accuracy: a neural networks-based first-principles method for heat of formation of molecules made of H, C, N, O, F, S, and Cl. Journal of Physical Chemistry A. ,vol. 118, pp. 9120- 9131 ,(2014) , 10.1021/JP502096Y

Bobby G. Sumpter, Donald W. Noid, Potential energy surfaces for macromolecules. A neural network technique Chemical Physics Letters. ,vol. 192, pp. 455- 462 ,(1992) , 10.1016/0009-2614(92)85498-Y