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Book Chapters

5 “Quantum Machine Learning with Response Operators in Chemical Compound Space”, F. A. Faber, A. S. Christensen, OAvL,
In: S. Chmiela, K. Schutt, OAvL, A. Tkatchenko, K. Muller (eds) “Machine Learning meets Quantum Physics”, Lecture Notes in Physics (Springer) (2020)
4 “Modeling Materials Quantum Properties with Machine Learning”, F. A. Faber, OAvL,
In: O. Isayev, A. Tropsha, S. Curtarolo (eds) Materials Informatics: Methods, Tools and Applications, Wiley-CH, 171-179 1 (2019)
DOI  (Aug 2019)
3 “The fundamentals of quantum machine learning”, B. Huang, N. O. Symonds, OAvL,
In: Andreoni W., Yip S. (eds) Handbook of Materials Modeling (Springer), Cham (2018)
DOI (2018);
2 “Machine Learning, Quantum Mechanics, and Chemical Compound Space”, R. Ramakrishnan, OAvL
In: Reviews in Computational Chemistry, edited by Abby L. Parrill and Kenny B. Lipkowitz, Volume 30, Chapter 5, pages 225-256 (2017) (2016); (2017)
1 “Towards the Computational Design of Compounds from First Principles”, OAvL,
In: Mathematical Physics Studies (Springer) volume IX, page 417, (2014) ”Many-Electron Approaches in Physics, Chemistry and Mathematics: A Multidisciplinary View” edited by Luigi Delle Site (FU Berlin) and Volker Bach (TU Braunschweig).



104 “Energy-free machine learning predictions of {\em ab initio} structures”. Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld
(2020) arXiv:2102.02806
103 “Elucidating atmospheric brown carbon — Supplanting chemical intuition with exhaustive enumeration and machine learning”. Enrico Tapavicza, Guido Falk von Rudorff, David O. De Haan, Mario Contin, Christian George, Matthieu Riva, O. Anatole von Lilienfeld
(2020) arXiv:2101.07301
102 “Ab initio machine learning in chemical compound space”. Bing Huang, O.Anatole von Lilienfeld. arXiv preprint,
(2020) arXiv:2012.07502 To appear as part of the special issue “Machine Learning in Chemistry” of Chemical Reviews
101 “Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental solvation accuracy. “Jan Weinreich, Nicholas J Browning, and O Anatole von Lilienfeld. arXiv preprint,
(2020) arXiv:2012.09722, 2020.9
100 “Quantum based machine learning of competing chemical reaction profiles”, S. N. Heinen, G. F. von Rudorff, OAvL,
(2020) arXiv (2020).
99 “Data enhanced Hammett-equation: reaction barriers in chemical space”, M. Bragato, G. F. von Rudorff, OAvL,
(2020), DOI: 10.1039/d0sc04235h, arXiv (2020).
98 “Dictionary of 140k GDB and ZINC derived AMONs”, B. Huang, OAvL,
(2020) arXiv (2020).
97 “Solving the inverse materials design problem with alchemical chirality”, G. F. von Rudorff, OAvL,
(2020) arXiv (2020).
96 “An assessment of the structural resolution of various fingerprints commonly used in machine learning”, Parsaeifard, Behnam; De, Deb; A. S.Christensen, F. Faber, Kocer, Emir, De, Sandip; Behler, Jörg; OAvL ; Goedecker, Stefan,
DOI: 10.1088/2632-2153/abb212, arXiv (2020).
95 “ML Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL and PhysNet”, S. Käser, D. Koner, A. S. Christensen, OAvL, M. Meuwly,
J. Phys. Chem. A (2020), DOI: 10.1021/acs.jpca.0c05979, arXiv (2020).
94 “Thousands of reactants and transition states for competing E2 and SN2 reactions”, G. F. von Rudorff, S. N. Heinen, M. Bragato, OAvL,
arXiv (2020).
93 “Large yet bounded: Spin gap ranges in carbenes” M. Schwilk, D. Tahchieva, OAvL,
arXiv (2020).
92 “Retrospective: A decade of Machine learning for Chemical Discovery”, K. Burke, OAvL
Nat. Commun. DOI: 10.1038/s41467-020-18556-9,
91 “Quantum machine learning using atom-in-molecule-based fragments selected on-the-fly”, B. Huang, OAvL,
Nat. Chem. 2020 in press, DOI: 10.1038/s41557-020-0527-z, arXiv (2017).
90 “Effects of perturbation order and basis set on alchemical predictions”, G. Domenichini, G. F. von Rudorff, OAvL,
J. Chem. Phys., (2020), DOI; 10.1063/5.0023590, arXiv (2020).
89 “Wasserstein metric for improved QML with adjacency matrix representations”, Onur Caylak, OAvL, Bjorn Baumeier,
Mach. Learn.: Sci. Tech. , arXiv (2020).
88 “Neural networks and kernel ridge regression for excited states dynamics of CH2NH+2: From single-state to multi-state representations and multi-property machine learning models”, Julia Westermayr, Felix A. Faber, A. S. Christensen, OAvL, P. Marquetand,
Mach. Learn.: Sci. Tech. , arXiv (2019).
87 “Exploring chemical compound space with quantum-based machine learning”, OAvL, K.-R. Muller and A. Tkatchenko,
Nat Rev Chem 4, 347–358 (2020), DOI: 10.1038/s41570-020-0189-9, arXiv (2019).
86 “Quantum-chemistry-aided identification, synthesis and experimental validation of model systems for conformationally controlled reaction studies: Separation of the conformers of 2,3-dibromobuta-1,3-diene in the gas phase”, A. Kilaj, H. Gao, D. Tahchieva, R. Ramakrishnan, D. Bachmann, D. Gillingham, OAvL, K. Kuepper, S. Willitsch,
Phys. Chem. Chem. Phys., 22, 13431-13439 (2020), DOI: 10.1039/D0CP01396J, arXiv (2020).
85 “Alchemical perturbation density functional theory”, G. F. von Rudorff, OAvL,
Phys. Rev. Research 2, 023220 (2020), DOI: 10.1103/PhysRevResearch.2.023220, arXiv (2018).
84 “Machine learning the computational cost of quantum chemistry”, S. Heinen, M. Schwilk, G. F. von Rudorff, OAvL,
Mach. Learn.: Sci. Tech. 1 025002 (2020), DOI: 10.1088/2632-2153/ab6ac4, arXiv (2019).
83 “Non-covalent quantum machine learning corrections to density functionals”, P. D. Mezei, OAvL,
J. Chem. Theory Comput., 16, 4, 2647–2653 (2020), DOI: 10.1021/acs.jctc.0c00181, arXiv (2019).
82 “Introducing Machine Learning: Science and Technology”, OAvL,
Mach. Learn.: Sci. Tech. 1 010201 (2020), DOI: 10.1088/2632-2153/ab6d5d, arXiv (20
81 “FCHL revisited: faster and more accurate quantum machine learning”, A. S. Christensen, L. A. Bratholm, F.A. Faber, OAvL,
J Chem Phys 152 044107 (2020), DOI: 10.1063/1.5126701, arXiv (2019).
80 “Rapid and accurate molecular deprotonation energies from quantum alchemy”, G.F. von Rudorff, OAvL,
Phys. Chem. Chem. Phys., 22, 10519-10525 (2020) (Special issue Michiel Sprik), DOI: 10.1039/C9CP06471K, arXiv (2019).


79 “Operator quantum machine learning: Navigating the chemical space of response properties”, A. S. Christensen, OAvL,
CHIMIA 73 1028 (2019), DOI: 10.2533/chimia.2019.1028, arXiv (2019).
78 “Atoms in molecules from alchemical perturbation density functional theory”, G. F. von Rudorff, OAvL,
J. Phys. Chem. 123, 47, 10073–10082 (2019), (Special issue David Beratan), DOI: 10.1021/acs.jpcb.9b07799, arXiv (2019).
77 “Operators in Machine Learning: Response Properties in Chemical Space”, A. S. Christensen, F. A. Faber, OAvL,
J. Chem. Phys. 150, 064105 (2019), DOI: 10.1063/1.5053562, arXiv (2018).
76 “Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited” P. Zaspel, B. Huang, H. Harbrecht, OAvL
J. Chem. Theory Comput., 15, 1546–1559 (2019), DOI: 10.1021/acs.jctc.8b00832, arXiv (2018).
75 “Alchemical normal modes unify chemical space”, S. Fias, K. Y. S. Chang, OAvL,
J. Phys. Chem. Lett. 10, 30–39 (2019), DOI: 10.1021/acs.jpclett.8b02805, arXiv (2018).


74 “Torsional potentials of glyoxal, oxalyl halides and their thiocarbonyl derivatives: Challenges for DFT”, D. Tahchieva, D. Bakowies, R. Ramakrishnan, OAvL,
J Chem Theory Comput (2018), arXiv (2018).
73 “Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts”, B. Sawatlon, B. Meyer, S. Heinen, OAvL, C. Corminboeuf,
Chem. Sci., 9, 7069-7077 (2018), DOI: 10.1039/C8SC01949E.
72 “AlxGa1-xAs crystals with direct 2 eV band gaps from computational alchemy”, K. Y. S. Chang, OAvL,
Phys Rev Materials 073802 (2018), arXiv (2018).
71 “Editorial: Special Topic on Data-enabled Theoretical Chemistry” M. Rupp, OAvL, K. Burke
J Chem Phys 148 241401 (2018)
70 “Generalized DFTB repulsive potentials from unsupervised machine learning” J. J. Kranz, M. Kubillus, R. Ramakrishnan, OAvL, M. Elstner
J Chem Theory Comput 14 2341 (2018).
69 “Constant Size Molecular Descriptors For Use With Machine Learning”, C. R. Collins, G. J. Gordon, OAvL, D. J. Yaron
J Chem Phys 148 241718 (2018)
68 “Alchemical and structural distribution based representation for improved QML”, F. A. Faber, A. S. Christensen, B. Huang, OAvL,
J Chem Phys 148 241717 (2018), arXiv (2017).
67 “Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning”, T. Bereau, R. A. DiStasio, A. Tkatchenko, OAvL,
J Chem Phys 148 241706 (2018), DOI: 10.1063/1.5009502, arXiv (2017).


66 “Quantum machine learning in chemical compound space”, OAvL
Angew. Chem. Int. Ed. 57 4164 (2017)
“Quantum Machine Learning im chemischen Raum” (German version), OAvL
Angew. Chem. 130 4235 (2017)
65 “Exploring water adsorption on isoelectronically doped graphene using alchemical derivatives”, Y. S. Al-Hamdani, A. Michaelides, OAvL
J Chem Phys 147 164113 (2017)
64 “Alchemical Predictions for Computational Catalysis: Potential and Limitations”, K. Saravanan, J. Kitchin, OAvL, J. Keith
J Phys Chem Lett (2017)
63 “Prediction errors of molecular machine learning models lower than hybrid DFT error”, F. A. Faber, L. Hutchison, B. Huang, J. Gilmer, S. S. Schoenholz, G. E. Dahl, O. Vinyals, S. Kearnes, P. F. Riley, OAvL
J Chem Theory Comput 13 5255 (2017)
62 “Genetic optimization of training sets for improved machine learning models of molecular properties”, N. J. Browning, R. Ramakrishnan, OAvL, U. Rothlisberger
J. Phys. Chem. Lett (2017), arXiv (2016).


61 “Understanding molecular representations in machine learning: The role of uniqueness and target similarity”, B. Huang, OAvL,
J. Chem. Phys. (Communication) 145 161102 (2016)
60 “Alchemical screening of ionic crystals” A. Solovyeva, OAvL
Phys Chem Chem Phys 1831078 (2016), (2016)
59 “Blind test of density-functional-based methods on intermolecular interaction energies”, D. E. Taylor, J. G. Angyan, G. Galli, C. Zhang, F. Gygi, K. Hirao, OAvL, R. Podeszwa, I. W. Bulik, T. M. Henderson, G. E. Scuseria, J. Toulouse, R. Peverati, D. G. Truhlar, K. Szalewicz,
J. Chem. Phys. 145 124105 (2016)
58 “Machine Learning Energies of 2 M Elpasolite (ABC2D6) Crystals”, F. Faber, A. Lindmaa, OAvL, R. Armiento
Phys. Rev. Lett. 117 135502 (2016)
57 “Fast and accurate predictions of covalent bonds in chemical space”, K. Y. S. Chang, S. Fias, R. Ramakrishnan, OAvL
J. Chem. Phys. 144 174110 (2016)
56 “Tuning dissociation using isoelectronically doped graphene and hexagonal boron nitride: water and other small molecules”, Y. Al-Hamdani, D. Alfe, OAvL, A. Michaelides
J. Chem. Phys. 144 154706 (2016)
55 “Guiding ab initio calculations by alchemical derivatives”, M. to Baben, J. O. Achenbach, OAvL
J. Chem. Phys. 144 104103 (2016)
54 “Properties and reactivity of nucleic acids relevant to epigenomics, transcriptomics, and therapeutics”, D. Gillingham, S. Geigle, OAvL
Chem. Soc. Rev. DOI: 10.1039/C5CS00271K (2016)


53 “Machine learning for many-body physics: efficient solution of dynamical mean-field theory”, L.-F. Arsenault, OAvL, A. J. Millis
52 “Electronic Spectra from TDDFT and Machine Learning in Chemical Space”, R. Ramakrishnan, M. Hartmann, E. Tapavicza, OAvL,
J. Chem. Phys. 143 084111 (2015)
51 “Machine Learning for Quantum Mechanical Properties of Atoms in Molecules”, M. Rupp, R. Ramakrishnan, OAvL,
J. Phys. Chem. Lett. 6 3309 (2015)
50 “Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Non-Locality in Chemical Space”, K. Hansen, F. Biegler, R. Ramakrishnan, W. Pronobis, OAvL, K.-R. Mueller, A. Tkatchenko,
J. Phys. Chem. Lett. 6 2326 (2015).
49 “Transferable atomic multipole machine learning models for small organic molecules”, T. Bereau, D. Andrienko, OAvL
J. Chem. Theory Comput. 11 3225 (2015)
48 “Water on hexagonal boron nitride from diffusion Monte Carlo”, Y. Al-Hamdani, M. Ma, D. Alfe, OAvL, A. Michaelides
J. Chem. Phys. 142 181101 (2015)
47 “Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations”, P. Dral, OAvL, W. Thiel
J. Chem. Theory Comput. 11 2120 (2015)
46 “Big Data meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach”, R. Ramakrishnan, P. O. Dral, M. Rupp, OAvL,
J. Chem. Theory Comput. 11 2087 (2015),
45 “Crystal Structure Representations for Machine Learning Models of Formation Energies”, F. Faber, A. Lindmaa, OAvL, R. Armiento,
Int. J. Quantum Chem. doi:10.1002/qua.24917 (2015)
44 “Many Molecular Properties from One Kernel in Chemical Space”, R. Ramakrishnan, OAvL
CHIMIA 69 182 (2015) ; see here for supplementary material related to this publication.
43 “Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties”, OAvL, R. Ramakrishnan, M. Rupp, A. Knoll
Int. J. Quantum Chem. doi:10.1002/qua.2491 (2015)


42 “Water on BN doped benzene: A hard test for exchange-correlation functionals and the impact of exact exchange on weak binding”, Y. S. Al-Hamdani,D. Alfe, OAvL, A. Michaelides,
J. Chem. Phys. 141 , 18C530 (2014)
41 “Machine learning for Many-Body Physics : The case of the Anderson impurity model”, L.-F. Arsenault, A. Lopez-Bezanilla, OAvL, A. Millis,
Phys. Rev. B 90 155136 (2014),
40 “Quantum Mechanical Treatment of Variable Molecular Composition: From “Alchemical” Changes of State Functions to Rational Compound Design” , K. Y. S. Chang and OAvL,
CHIMIA 68 602 (2014)
39 “Quantum chemistry structures and properties of 134 kilo molecules”, R. Ramakrishnan, P. O. Dral, M. Rupp, OAvL,
Scientific Data 140022 (2014)
38 “Toward transferable interatomic van der Waals potentials: The role of multipole electrostatics and many-body dispersion without electrons”, T. Bereau, OAvL,
J. Chem. Phys. 141 034101 (2014) (2014)
37 “Application of diffusion Monte Carlo to materials dominated by van der Waals interactions”, A. Benali, N. A. Romero, L. Shulenburger, J. Kim, OAvL,
J Chem Theory Comput 10 3417 (2014)
36 “Modeling electronic quantum transport with machine learning”, A. Lopez-Bezanilla, OAvL,
Phys Rev B 89 235411 (2014) (2014)


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
J Chem Theory Comput 3404 (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,
in 2013 “Focus on Novel Materials Discovery” issue, guest edited by R. Caflisch, G. Ceder, K. Kremer, T. Pollock, M. Scheffler, and E. G. Wang,
New J. Phys. 15 095003 (2013) (2013)
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. 111 2147 (2013) (2013)
invited paper for special issue dedicated to Martin Quack on the occasion of his 65th birthday.
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.


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
30 “Collective many-body van der Waals interactions in molecular systems”, R. A. DiStasio, OAvL, A. Tkatchenko
PNAS 109 14791-14795 (2012)
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) (2011)


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. 2358 (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. 2549 (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).
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).
21 “Long range interactions in nanoscale sciences”, R. H. French, V. A. Parsegian, R. Podgornik, R. F. Rajter et al.,
Rev. Mod. Phys 82 1887 (2010) (highlighted by VJNANO).
20 “Translation of Walter Noll’s “Derivation of the Fundamental Equations of Continuum Thermodynamics from Statistical Mechanics””, R. B. Lehoucq, OAvL,
Journal of Elasticity 100 5 (2010).
19 “Molecular simulation of the thermal and transport properties of three alkali nitrate salts”, S. Jayaraman, A. P. Thompson, OAvL, E. J. Maginn,
Ind. Eng. Chem. Res. 49 559 (2010).


18 “Simple intrinsic defects in gallium arsenide”, P. A. Schultz and OAvL,
Modelling Simul. Mater. Sci. Eng. 17 084007 (2009). (In focus issue: Challenges for first-principles based properties of defects in semiconductors and oxides)
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).


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).
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).
12 “Tuning electronic eigenvalues of benzene via doping”, V. Marcon, OAvL, D. Andrienko,
J. Chem. Phys. 127 064305 (2007) (highlighted by VJBIO).
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. 1673 (2007).
10 “Spectroscopic properties of CCl3F calculated by density functional theory”, OAvL, C. Leonard, N. C. Handy, S. Carter, M. B. Willeke, M. Quack,
Phys. Chem. Chem. Phys. 9 5027 (2007).
9 “Library of dispersion corrected atom centered potentials for generalized gradient approximation functionals: Elements H, C, N, O, He, Ar and Kr”, I-C. Lin, M. D. Coutinho-Neto, C. Felsenheimer, OAvL, I. Tavernelli and U. Rothlisberger,
Phys. Rev. B 75 205131 (2007).
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).
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).


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)
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)