## Compositional changes and quantum chemistry

These studies deal with the direct influence of chemical composition on quantum mechanical observables, such as total potential energy, electronic eigenvalues, or intermolecular interactions. We use various electronic-structure levels of theory implemented in various software packages for this. One of them being quantum Monte Carlo .

32 Tutorial review on “First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties”, OAvL,

*Int. J. Quantum Chem.* ** 113 ** 1676 (2013)

Note that lambda in fig 6.b should in fact be the square root of lambda. Thanks to Qing-Long Liu for the following corrections: (i) Page 5, last sentence of first paragraph in right column: [63] should be [64]. (ii) Page 8, left column, third line: NH2 should be NH.

27 “Molten salt eutectics from atomistic simulations” S. Jayaraman, A. P. Thompson, OAvL, Rapid Communication in

*Phys. Rev. E* ** 84 ** 030201 (2011)

26 “Path integral computation of quantum free energy differences due to alchemical transformations involving mass and potential” A. Perez, OAvL,

*J. Chem. Theory Comput.* ** 7 ** 2358 (2011)

24 “Alchemical derivatives of reaction energetics”, D. Sheppard, G. Henkelman, OAvL,

*J. Chem. Phys.* **133** 084104 (2010)

23 “Enol tautomers of Watson-Crick base pair models are metastable because of nuclear quantum effects”, A. Perez, M. E. Tuckerman, H. P. Hjalmarson, OAvL,

*J. Am. Chem. Soc.* **132** 11510 (2010).

17 “Accurate ab initio energy gradients in chemical compound space”, OAvL,

*J. Chem. Phys.* ** 131** 164102 (2009).

16 “Ab initio molecular dynamics calculations of ion hydration free energies”, K. Leung, S. B. Rempe and OAvL,

*J. Chem. Phys.* ** 130** 204507 (2009) (highlighted by VJBIO).

12 “Tuning electronic eigenvalues of benzene via doping”, V. Marcon, OAvL, D. Andrienko,

*J. Chem. Phys.* ** 127 ** 064305 (2007) (highlighted by VJBIO).

8 “Alchemical variation of intermolecular energies according to molecular grand-canonical ensemble density functional theory”, OAvL and M. E. Tuckerman,

*J. Chem. Theory Comput.* **3** 1083 (2007).

7 “Molecular grand-canonical ensemble density functional theory and exploration of chemical space”, OAvL and M. E. Tuckerman,

*J. Chem. Phys.* **125** 154104 (2006).

4 “Variational particle number approach for rational compound design”, OAvL, R. Lins, U. Rothlisberger,

*Phys. Rev. Lett.* **95** 153002 (2005) (cover article). (highlighted by VJBIO)

## Machine learning in chemical compound space

In this line of work, we use statistics and large data sets of chemical compounds to construct analytical models of quantum mechanical properties. More information can be found on the quantum machine homepage.

36 “Fingerprint representation of molecules: Fourier series of radial distribution functions as descriptor for machine learning across chemical compound space”, OAvL, M. Rupp, A. Knoll,

submitted *arxiv.org/abs/1307.2918* ** ** (2013)

35 “Assessment and validation of machine learning methods for predicting molecular atomization energies”, K. Hansen, G. Montavon, F. Biegler, S. Fazli, M. Rupp, M. Scheffler, OAvL, A. Tkatchenko, K-R. Mueller,* accepted** in** J Chem Theory Comput* (2013)

34 “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. Mueller, OAvL,

accepted to appear in the “Novel Materials Discovery” issue, guest edited by R. Caflisch, G. Ceder, M. Scheffler, and E. Wang, *New Journal of Physics* ** ** (2013) *arxiv.org/abs/1305.7074* ** ** (2013)

31 “Learning Invariant Representations of Molecules for Atomization Energy Prediction”, G. Montavon, K. Hansen, S. Fazli, M. Rupp, F. Biegler, A. Ziehe, A. Tkatchenko, OAvL, K.-R. Mueller,

* Advances in Neural Information Processing Systems* 449-457** 25 ** (2012)

Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger,

29 “Reply to Comment on “Fast and accurate modeling of molecular atomization energies with machine learning””, M. Rupp, A. Tkatchenko, K.-R. Mueller, OAvL,

*Phys. Rev. Lett.* ** 109 ** 059802 (2012)

28 “Fast and accurate modeling of molecular atomization energies with machine learning”, M. Rupp, A. Tkatchenko, K.-R. Mueller, OAvL,

*Phys. Rev. Lett.* ** 108 ** 058301 (2012) *arxiv.org/abs/1109.2618E* ** ** (2011)

25 “Towards quantitative structure-property relationships for charge transfer rates of polycyclic aromatic hydrocarbons” M. Misra, D. Andrienko, B. Baumeier , J.-L. Faulon, OAvL,

*J. Chem. Theory Comput.* ** 7 ** 2549 (2011)

## Many-body van der Waals forces

30 “Collective many-body van der Waals interactions in molecular systems”, R. A. DiStasio, OAvL, A. Tkatchenko,

*PNAS* ** 109 ** 14791-14795 (2012)

22 “Two and three-body interatomic dispersion energy contributions to binding in molecules and solids”, OAvL, A. Tkatchenko,

*J. Chem. Phys* **132** 234109 (2010) (highlighted by VJBIO).

15 “Popular Kohn-Sham density functionals strongly overestimate many-body interactions in van der Waals systems”, A. Tkatchenko and OAvL,

* Phys. Rev. B ***78** 045116 (2008).

## Optimized atom centered potentials and pseudopotentials

33 “Force correcting atom centered potentials for generalized gradient approximated density functional theory: Approaching hybrid functional accuracy for geometries and harmonic frequencies in small chlorofluorocarbons”, OAvL,

*Mol. Phys.* ** ** (2013) *arxiv.org/abs/1301.3225* ** ** (2013)

14 “Structure and band gaps of Ga-(V) semiconductors: The challenge of Ga pseudopotentials”, OAvL and P. A. Schultz,

*Phys Rev B*** 77 ** 115202 (2008).

13 “Predicting noncovalent interactions between aromatic biomolecules with London-dispersion-corrected DFT”, I-C. Lin, OAvL, M. D. Coutinho-Neto, I. Tavernelli, U. Rothlisberger,

*J. Phys. Chem. B ***111** 14346 (2007).

11 “Study of weakly bonded carbon compounds using dispersion corrected density functional theory”, E. Tapavicza, I-C. Lin, OAvL, I. Tavernelli, M. D. Coutinho, U. Rothlisberger,

* J. Chem. Theory Comput. * ** 3 **1673 (2007).

6 “Adsorption of Ar on graphite using London dispersion forces corrected Kohn-Sham density functional theory”, A. Tkatchenko and OAvL,

*Phys. Rev. B* **73** 153406 (2006).

5 “Coarse-grained interaction potentials for polyaromatic hydrocarbons”, OAvL and D. Andrienko,

*J. Chem. Phys. * **124** 054307 (2006).

3 “Performance of optimized atom centered potentials for weakly bonded systems using density functional theory”, OAvL, I. Tavernelli, U. Rothlisberger, D. Sebastiani,

*Phys. Rev. B* **71** 195119 (2005). (highlighted by VJBIO)

2 “Variational optimization of effective atom centered potentials for molecular properties”, OAvL, I. Tavernelli, U. Rothlisberger, D. Sebastiani,

*J. Chem. Phys.* **122** 14113 (2005). (highlighted by VJBIO)

1 “Optimization of effective atom centered potentials for London dispersion forces in density functional theory”, OAvL, I. Tavernelli, U. Rothlisberger, D. Sebastiani,

*Phys. Rev. Lett.* **93** 153004 (2004). (highlighted by VJBIO)