Publications
This publication list is no longer up-dated, please check Google scholar for an overview of our latest contributions:
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Researcher ID
ORCID 0000-0001-7419-0466
We also post our papers on TWITTER! Follow us @ProfvLilienfeld
DOI 2 bibtex:
https://www.doi2bib.org/
https://github.com/bibcure/doi2bib
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)
https://www.springer.com/gp/book/9783030402440
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 https://doi.org/10.1002/9783527802265.ch6 (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 https://doi.org/10.1007/978-3-319-42913-7_67-1 (2018); https://arxiv.org/abs/1807.04259
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)
arxiv.org/abs/1510.07512 (2016); http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1119355435.html (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).
Articles
Research papers in reverse chronological order (preprints and peer reviewed)
(118) ’ Selected Machine Learning of HOMO-LUMO gaps with Improved Data-Efficiency’, B.Mazouin, A.A. Schöpfer,OAvL,
arxiv.org/pdf/2110.02596
(117) ’Quantum machine learning in chemical compound space’, B. Huang, OAvL,
arxiv.org/abs/2012.07502
(116) ’Quantum based machine learning of competing chemical reaction profiles’, S. Heinen, G. F. vonRudorff, OAvL,
arxiv.org/abs/2009.13429
(115) ’Dictionary of 140k GDB and ZINC derived AMONs’, B. Huang, OAvL,
arxiv.org/abs/2008.05260
(114) ’Large yet bounded: Spin gap ranges in carbenes’, M. Schwilk, D. Tahchieva, OAvL,
arxiv.org/abs/2004.10600
2022
(113)’Alchemical geometry relaxation’, G. Domenichini, OAvL
https://arxiv.org/pdf/2201.07129.pdf DOI:10.1063/5.0085817
(112) ’An orbital-based representation for accurate Quantum Machine Learning’, K. Karandashev, OAvL,
arxiv.org/pdf/2112.12877 DOI:10.1063/5.0083301
(111)’Non-covalent interactions between molecular dimers (S66) in electric
fields’, M.Schwilk, PD Mezei,DN Tahchieva,OAvL,
DOI:10.1088/2516-1075/ac4eeb arxiv.org/pdf/2109.15225
2021
(110)’Introduction: Machine Learning at the Atomic Scale’, M Cheriotti, C Clementi, OAvL,
Chem. Rev. 2021, 121, 16, 9719–972 (2021)
DOI:10.1021/acs.chemrev.1c00598
(109)’Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space’, S Heinen, GF von Rudorff, OAvL,
The Journal of Chemical Physics 155, 064105, (2021)
DOI:full/10.1063/5.0059742
(108)’Conformer-specific polar cycloaddition of dibromobutadiene with trapped propene ions’, A. Kilaj, J. Wang, P. Straňák, M. Schwilk, U. Rivero, Lei Xu, OAvL, J. Küpper, S. Willitsch,
Nat.Commun 12,6047 (2021)
DOI:10.1038/s41467-021-26309-5
(107)’Density Functional Geometries and Zero-Point Energies in Ab Initio Thermochemical Treatments of Compounds with First-Row Atoms(H,C,N,O,F)’, D Bakowies, OAvL,
Journal of Chemical Theory and Computation 17, 8, 4872-4890, (2021)
DOI: 10.1021/acs.jctc.1c00474
(106) ’Ab initio machine learning in chemical compound space’, B. Huang, OAvL,
Chemical Reviews (2021),
arxiv.org/abs/2012.07502 DOI:abs/10.1021/acs.chemrev.0c01303
(105) ’Machine learning based energy-free structure predictions of molecules, transition states, and solids’,
D Lemm, GF von Rudorff,OAvL,
Nature Communication 4468 (2021),
arxiv.org/abs/2102.02806 DOI: s41467-021-24525-7
(104) ’Elucidating atmospheric brown carbon–Supplanting chemical intuition with exhaustive enumeration and machine learning’, E. Tapavicza, G. F. von Rudorff, D.O. De Haan, M. Contin, C. George, M. Riva, OAvL,
Environ. Sci. Technol. 55, 12, 8447-8457 (2021),
arxiv.org/abs/2101.07301 DOI:abs/10.1021/acs.est.1c00885
(103) ’Simplifying inverse materials design problems for fixed lattices with alchemical chirality’, G. F. von Rudorff, OAvL,
Science Advances 7 eabf1173 (2021),
DOI: 10.1126/sciadv.abf117, arxiv.org/abs/2008.02784
(102) ’Machine Learning Meets Chemical Physics’, M. Ceriotti, C. Clementi, OAvL,
J. Phys. Chem. 154 160401 (2021)
DOI:10.1063/5.0051418
(101) ’Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation’, J. Weinreich, N. J. Browning, OAvL,
J. Phys. Chem. 154 134113 (2021)
DOI:10.1063/5.0041548
(100) ’An assessment of the structural resolution of various fingerprints commonly used in machine learning’, B. Parseifard, D. S. De, A. S. Christensen, F. A. Faber, E. Kocer, S. De, J. Behler, OAvL, S. Goedecker,
Mach. Learn.: Sci. Tech. 2 015018 (2021)
DOI:10.1088/2632-2153
2020
(99) ’Machine Learning Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL, and PhysNet’, S. Käser, D. Koner, A. S. Christensen, OAvL, M. Meuwly,
J. Phys. Chem. A 124 8853 (2020)
DOI: 10.1021/acs.jpca.0c05979
(98) ’Retrospective on a decade of machine learning for chemical discovery’, OAvL, K. Burke,
Nature Communications 11 1 (2020)
DOI:s41467-020-18556-9
(97) ’On the role of gradients for machine learning of molecular energies and forces’, A. S. Christensen, OAvL,
Mach. Learn.: Sci. Tech. 1 045018 (2020)
DOI:10.1088/2632-2153
(96) ’Effects of perturbation order and basis set on alchemical predictions’, G. Domenichini, G. F. von Rudorff, OAvL,
J. Chem. Phys. 153 144118 (2020)
DOI:10.1063/5.0023590
(95) ’Thousands of reactants and transition states for competing E2 and S2 reactions’, G. F. von Rudorff, S. N. Heinen, M. Bragato, OAvL,
Mach. Learn.: Sci. Tech. 1 045026 (2020)
DOI:10.1088/2632-2153
(94) ’Quantum machine learning using atom-in-molecule-based fragments selected on the fly’, B. Huang, OAvL,
Nature Chemistry 12 945 (2020),
DOI:s41557-020-0527-z, arxiv.org/abs/1707.04146
(93) ’Data Enhanced Reaction Predictions in Chemical Space With Hammett’s Equation’, M. Bragato,G. F. von Rudorff, OAvL,
Chemical Science 11 11859 (2020),
arxiv.org/abs/2004.14946
(92) ’Wasserstein metric for improved QML with adjacency matrix representations’, O. Caylak,OAvL, B. Baumeier,
Mach. Learn.: Sci. Tech. (2020),
DOI:10.1088/2632-2153/aba048/meta#artAbst
(90) ’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 Küpper, S. Willitsch,
Phys. Chem. Chem. Phys. 22 13431 (2020),
[DOI: 10.1039/D0CP01396J](doi: 10.1039/D0CP01396J)
(89) ’Exploring Chemical Compound Space with Quantum-Based Machine Learning, OAvL, KR Müller, A Tkatchenko,
Nature Chemistry Reviews 4 347 (2020),
DOI:10.1038/s41570-020-0189-9, arxiv.org/abs/1911.10084
(88) ’Alchemical perturbation density functional theory’, G. F. von Rudorff, OAvL,
Phys Rev Research 2 023220 (2020)
DOI:10.1103/PhysRevResearch.2.023220 , arxiv.org/abs/1809.01647
(87) ’Neural networks and kernel ridge regression for excited states dynamics of CH2NH+2 : …’, JWestermayr, FA Faber, AS Christensen, OAvL, P. Marquetand
Mach. Learn.: Sci. Tech. 1 010201 (2020),
DOI:10.1088/2632-2153/ab88d0, arxiv.org/abs/1912.08484
(86) ’Non-covalent quantum machine learning corrections to density functionals’, P. D. Mezei, OAvL, J Chem Theory Comput 16 2647 (2020),
DOI:10.1021/acs.jctc.0c00181, arxiv.org/abs/1903.09010
(85) ’Rapid and accurate molecular deprotonation energies from quantum alchemy’, G. F. von Rudorff, OAvL,
Phys. Chem. Chem. Phys. 22 10519 (2020),
[DOI: 10.1039/C9CP06471K](doi: 10.1039/C9CP06471K)
(84) ’FCHL revisited: faster and more accurate quantum machine learning’ AS Christensen, LA Bratholm, FA Faber, OAvL,
J. Chem. Phys. 152 044107 (2020)
DOI:10.1063/1.5126701
(83) ’Machine learning the computational cost of quantum chemistry’ S Heinen, M Schwilk, GF von Rudorff, OAvL,
Mach. Learn.: Sci. Tech. 1 025002 (2020)
DOI:10.1088/2632-2153 , arxiv.org/abs/1908.06714
(82) ’Introducing Machine Learning: Science and Technology’, OAvL,
Mach. Learn.: Sci. Tech. 1 010201 (2020)
DOI:10.1088/2632-2153
2019
(81) ’Operator quantum machine learning: Navigating the chemical space of response properties’ AS Christensen, OAvL,
CHIMIA 73 1028 (2019)
DOI:10.2533/chimia.2019.1028
(80) ’Atoms in molecules from alchemical perturbation density functional theory’, GF von Rudorff, OAvL,
J. Phys. Chem. B 123 10073 (2019)
DOI:10.1021/acs.jpcb.9b07799, arxiv.org/abs/1907.06677
(79) ’Operators in quantum machine learning: Response properties in chemical space’ AS Christensen, FA Faber, OAvL,
J. Chem. Phys. 150 064105 (2019)
DOI:10.1063/1.5053562
(78) ’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.org/abs/1807.08811
(77) ’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 (2019),
DOI:10.1021/acs.jctc.8b00832, arxiv.org/abs/1808.02799
2018
(76) ’Alchemical normal modes unify chemical space’, S. Fias, K. Y. S. Chang, OAvL,
J. Phys. Chem. Lett.10 30 (2018),
DOI:10.1021/acs.jpclett.8b02805, arxiv.org/abs/1809.03302
(75) ’Torsional potentials of glyoxal, oxalyl halides and their thiocarbonyl derivatives: Challenges for DFT’, D. Tahchieva, D. Bakowies, R. Ramakrishnan, OAvL,
J. Chem. Theory Comput.14 4806 (2018)
DOI:10.1021/acs.jctc.8b00174, arxiv.org/abs/1802.06033
(74) ’Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts’, B. Sawatlon, B. Meyer, S. Heinen, OAvL, C. Corminboeuf,
Chemical Science 9 7069 (2018)
DOI: 10.1039/C8SC01949E
(73) ’AlxGa1-x As crystals with direct 2 eV band gaps from computational alchemy’, K. Y. S. Chang, OAvL,
Phys. Rev. Materials 2 073802 (2018),
DOI:10.1103/PhysRevMaterials.2.073802, arxiv.org/abs/1805.00299
(72) ’Editorial: Special Topic on Data-enabled Theoretical Chemistry’, M. Rupp, OAvL, K. Burke
J. Chem. Phys. 148 241401 (2018),
DOI:10.1063/1.5043213, arxiv.org/abs/1806.02690
(71) ’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)
DOI:10.1021/acs.jctc.7b00933
(70) ’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)
DOI:10.1063/1.5020441, arxiv.org/abs/1701.066493
(69) ’Alchemical and structural distribution based representation for universal QML’, F. A. Faber, A. S. Christensen, B. Huang, OAvL,
J. Chem. Phys. 148 241717 (2018)
DOI:10.1063/1.5020710, arxiv.org/abs/1712.08417
(68) ’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.org/abs/1710.05871
2017
(67) ’Quantum machine learning in chemical compound space’, OAvL,
Angew. Chem. Int. Ed. 57 4164 (2017)
DOI:10.1002/anie.201709686
(66) ’Alchemical Predictions for Computational Catalysis: Potential and Limitations’, K. Saravanan, J. Kitchin, OAvL, J. Keith,
J. Phys. Chem. Lett. 8 5002 (2017)
DOI:10.1063/1.5009502
(65) ’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)
DOI:10.1021/acs.jctc.7b00577
(64) ’Exploring water adsorption on isoelectronically doped graphene using alchemical derivatives’, Y. S. Al-Hamdani, A. Michaelides, OAvL,
J. Chem. Phys. 147 164113 (2017)
DOI:10.1063/1.4986314
(63) ’Machine Learning, Quantum Mechanics, and Chemical Compound Space’, R. Ramakrishnan, OAvL
Reviews in Computational Chemistry, edited by Abby L. Parrill and Kenny B. Lipkowitz, Volume
30, Chapter 5, pages 225-256 (2017)
arxiv.org/abs/1510.07512 (2016)
(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 8 1351 (2017)
arxiv.org/abs/1611.07435
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), arxiv.org/abs/1608.06194
60 “Alchemical screening of ionic crystals” A. Solovyeva, OAvL
Phys Chem Chem Phys 1831078 (2016), arxiv.org/abs/1605.08080 (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) arxiv.org/abs/1508.05315
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), arxiv.org/abs/1509.02847
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)
2015
53 “Machine learning for many-body physics: efficient solution of dynamical mean-field theory”, L.-F. Arsenault, OAvL, A. J. Millis
arxiv.org/abs/1506.08858
52 “Electronic Spectra from TDDFT and Machine Learning in Chemical Space”, R. Ramakrishnan, M. Hartmann, E. Tapavicza, OAvL,
J. Chem. Phys. 143 084111 (2015)arxiv.org/abs/1504.01966
51 “Machine Learning for Quantum Mechanical Properties of Atoms in Molecules”, M. Rupp, R. Ramakrishnan, OAvL,
J. Phys. Chem. Lett. 6 3309 (2015).arxiv.org/abs/1505.00350
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)arxiv.org/abs/1503.05453
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), arxiv.org/abs/1503.04987
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), arxiv.org/abs/1503.07406
44 “Many Molecular Properties from One Kernel in Chemical Space”, R. Ramakrishnan, OAvL
CHIMIA 69 182 (2015) ; arxiv.org/abs/1502.04563 ; 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) arxiv.org/abs/1307.2918
2014
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), arxiv.org/abs/1408.1143
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), arxiv.org/abs/1503.07034
39 “Quantum chemistry structures and properties of 134 kilo molecules”, R. Ramakrishnan, P. O. Dral, M. Rupp, OAvL,
Scientific Data 1 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), arxiv.org/abs/1403.6645 (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), arxiv.org/abs/1401.8277 (2014)
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
J Chem Theory Comput 9 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)arxiv.org/abs/1305.7074 (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)> arxiv.org/abs/1301.3225 (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.
2012
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)arxiv.org/abs/1109.2618E (2011)
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. 7 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. 7 2549 (2011)
2010
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).
2009
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).
2008
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).
2007
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. 3 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).
2006
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).
2005
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)
2004
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)