LAB PUBLICATIONS

Since 2014

2021

“Arbitrarily Accurate Quantum Alchemy“, G. F. von Rudorff, (2021)
J. Chem. Phys. (in press) (2021)
https://arxiv.org/abs/2109.11238 Doi/abs/10.1063/5.0073941

2019

“Molecular shape as a (useful) bias in chemistry”, G. F. von Rudorff, (2019),
https://arxiv.org/abs/1904.07035.

“Estimating Systematic Error and Uncertainty in Ab Initio Thermochemistry. I. Atomization Energies of Hydrocarbons in the ATOMIC(hc) Protocol”, D. Bakowies (2019)

https://doi.org/10.1021/acs.jctc.9b00343

High Throughput Virtual Screening of 200 Billion Molecular Solar Heat Battery Candidates”, ChemRxiv Preprint, M Koerstz, AS Christensen, KV Mikkelsen, MB Nielsen, JH Jensen (2019)https://chemrxiv.org/articles/High_Throughput_Virtual_Screening_of_200_Billion_Molecular_Solar_Heat_Battery_Candidates/8003813

A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules” The Journal of Chemical Physics 150 (13), 131103, L Cheng, M Welborn, AS Christensen, TF Miller III (2019)
https://aip.scitation.org/doi/pdf/10.1063/1.5088393
“Rapid and accurate molecular deprotonation energies from quantum alchemy”, G.F. von Rudorff, OAvL, submitted, arxiv.org/abs/1911.13080.
“FCHL revisited: faster and more accurate quantum machine learning”, A. S. Christensen, L. A. Bratholm, F.A. Faber, D. R. Glowacki, OAvL, submitted, arxiv.org/abs/1909.01946.
“Machine learning the computational cost of quantum chemistry”, S. Heinen, M. Schwilk, G. F. von Rudorff, OAvL, submitted, arxiv.org/abs/1908.06714.
“Non-covalent quantum machine learning corrections to density functionals”, P. D. Mezei, OAvL, submitted,  arxiv.org/abs/1903.09010
“The DNA of chemistry: Scalable quantum machine learning with amons”, B. Huang, OAvL,
arxiv.org/abs/1707.04146.
“Alchemical perturbation density functional theory”, G. F. von Rudorff, OAvL,
J. Phys. Chem. B (2019)arxiv.org/abs/1809.01647.
“Atoms in molecules from alchemical perturbation density functional theory”, G. F. von Rudorff, OAvL,
J. Phys. Chem. B (2019)https://arxiv.org/abs/1907.06677.
“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 3,1546-1559 (2019), arxiv.org/abs/1808.02799.
“Operators in Machine Learning: Response Properties in Chemical Space”, A. S. Christensen, F. A. Faber, OAvL,
J. Chem. Phys. 150 064105 (2019)arxiv.org/abs/1807.08811.
“Alchemical normal modes unify chemical space”, S. Fias, K. Y. S. Chang, OAvL,
J. Phys. Chem. Lett. 10 1, 30-39 (2019)arxiv.org/abs/1809.03302.

2018

“Random versus Systematic Errors in Reaction Enthalpies Computed Using Semiempirical and Minimal Basis Set Methods”, JC Kromann, A Welford, AS Christensen, JH Jensen ACS Omega 3 (4), 4372-4377, (2018)
https://pubs.acs.org/doi/pdf/10.1021/acsomega.8b00189
“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 9, 4806-4817 (2018)arxiv.org/abs/1802.06033.
“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).
“AlxGa1-xAs crystals with direct 2 eV band gaps from computational alchemy”, K. Y. S. Chang, OAvL,
Phys Rev Materials 073802 (2018)arxiv.org/abs/1805.00299.
“Editorial: Special Topic on Data-enabled Theoretical Chemistry” M. Rupp, OAvL, K. Burke,
J Chem Phys 148 241401 (2018)arxiv.org/abs/1806.02690.
“Generalized DFTB repulsive potentials from unsupervised machine learning” J. J. Kranz, M. Kubillus, R. Ramakrishnan, OAvL, M. Elstner,
J. Chem. Theory Comput. 14 5, 2341-2352 (2018).
“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)arxiv.org/abs/1701.066493.
“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.org/abs/1712.08417.
“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)arxiv.org/abs/1710.05871.

2017

“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).
“Exploring water adsorption on isoelectronically doped graphene using alchemical derivatives”, Y. S. Al-Hamdani, A. Michaelides, OAvL,
J Chem Phys 147 164113 (2017)arxiv.org/abs/1703.10083.
“Alchemical Predictions for Computational Catalysis: Potential and Limitations”, K. Saravanan, J. Kitchin, OAvL, J. Keith,
J. Phys. Chem. Lett. 8 20, 5002-5007 (2017).
“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 11, 5255-5264 (2017)arxiv.org/abs/1702.05532.
“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.org/abs/1611.07435.

2016

“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.
“Alchemical screening of ionic crystals” A. Solovyeva, OAvL,
Phys Chem Chem Phys 18 31078 (2016)arxiv.org/abs/1605.08080 (2016).
“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).
“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.
“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.
“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).
“Guiding ab initio calculations by alchemical derivatives”, M. to Baben, J. O. Achenbach, OAvL,
J. Chem. Phys. 144 104103 (2016).
“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

“Machine learning for many-body physics: efficient solution of dynamical mean-field theory”, L.-F. Arsenault, OAvL, A. J. Millis,
Phys. Rev. B 90, 155136arxiv.org/abs/1506.08858.
“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.
“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.
“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).
“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.
“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).
“Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations”, P. Dral, OAvL, W. Thiel,
J. Chem. Theory Comput. 11 2120 (2015).
“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.
“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.
“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.
“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.
“Special Issue on Machine Learning and Quantum Mechanics”, M. Rupp,
Int. J. Quantum Chem. 115 (16): 1003–1004, Wiley, (2015).
“Machine Learning for Quantum Mechanics in a Nutshell”, M. Rupp,
Int. J. Quantum Chem. 115 (16): 1058–1073, Wiley, (2015). [supplement]
“Understanding Kernel Ridge Regression: Common Behaviors from Simple Functions to Density Functionals”, Kevin Vu, John C. Snyder, Li Li, M. Rupp, Brandon F. Chen, Tarek Khelif, Klaus-Robert Müller, Kieron Burke,
Int. J. Quantum Chem. 115 (16): 1115–1128, Wiley, (2015).
“Nonlinear Gradient Denoising: Finding Accurate Extrema from Inaccurate Functional Derivatives”, John C. Snyder, M. Rupp, Klaus-Robert Müller, Kieron Burke,
Int. J. Quantum Chem. 115 (16): 1102–1114, Wiley, (2015).

2014

“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).
“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.
“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.
“Quantum chemistry structures and properties of 134 kilo molecules”, R. Ramakrishnan, P. O. Dral, M. Rupp, OAvL,
Scientific Data 140022 (2014).
“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).
“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).
“Modeling electronic quantum transport with machine learning”, A. Lopez-Bezanilla, OAvL,
Phys Rev B 89 235411 (2014)arxiv.org/abs/1401.8277 (2014).

2 “Machine Learning Estimates of Natural Product Conformational Energies”, M. Rupp, M. R. Bauer, Rainer Wilcken, Andreas Lange, Michael Reutlinger, Frank M. Boeckler, Gisbert Schneider:  PLoS Computational Biology, 10(1): e1003400, Public Library of Science, (2014).

1 “Vibrational energy levels of difluorodioxirane computed with variational and perturbative methods from a hybrid force field”, R Ramakrishnan, T Carrington Jr,

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 119, 107-112 Sci. Data 1, 140022 (2014)

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