Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Open accessAbstract
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of 13 electronic ground-state properties of organic molecules. The performance of each regressor/representation/property combination is assessed using learning curves which report out-of-sample errors as a function of training set size with up to ∼118k distinct molecules. Molecular structures and properties at the hybrid density functional theory (DFT) level of theory come from the QM9 database [ Ramakrishnan et al. Sci. Data 2014 , 1 , 140022 ] and include enthalpies and free energies of atomization, HOMO/LUMO energies and gap, dipole moment, polarizability, zero point vibrational energy, heat capacity, and the highest fundamental vibrational frequency. Various molecular representations have been studied (Coulomb matrix, bag of bonds, BAML and ECFP4, molecular graphs (MG)), as well as newly developed distribution based variants including histograms of distances (HD), angles (HDA/MARAD), and dihedrals (HDAD). Regressors include linear models (Bayesian ridge regression (BR) and linear regression with elastic net regularization (EN)), random forest (RF), kernel ridge regression (KRR), and two types of neural networks, graph convolutions (GC) and gated graph networks (GG). Out-of sample errors are strongly dependent on the choice of representation and regressor and molecular property. Electronic properties are typically best accounted for by MG and GC, while energetic properties are better described by HDAD and KRR. The specific combinations with the lowest out-of-sample errors in the ∼118k training set size limit are (free) energies and enthalpies of atomization (HDAD/KRR), HOMO/LUMO eigenvalue and gap (MG/GC), dipole moment (MG/GC), static polarizability (MG/GG), zero point vibrational energy (HDAD/KRR), heat capacity at room temperature (HDAD/KRR), and highest fundamental vibrational frequency (BAML/RF). We present numerical evidence that ML model predictions deviate from DFT (B3LYP) less than DFT (B3LYP) deviates from experiment for all properties. Furthermore, out-of-sample prediction errors with respect to hybrid DFT reference are on par with, or close to, chemical accuracy. The results suggest that ML models could be more accurate than hybrid DFT if explicitly electron correlated quantum (or experimental) data were available.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
CNNs are better at predicting quantum chemical energies.
“For example, CNN is better at predicting quantum chemical energies.72”
From QSAR without borders (2020) · cites this paper as [72]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135 · see citing works
Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541 · see citing works
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,904 · see citing works
Open Babel: An open chemical toolbox
O'Boyle, N. M., Banck, M. S., James, C. A. J., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011) · Journal of Cheminformatics · doi:10.1186/1758-2946-3-33 · Cited by 10,705 · see citing works
UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Rappé, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A., & Skiff, W. M. (1992) · Journal of the American Chemical Society · doi:10.1021/ja00051a040 · Cited by 10,603 · see citing works
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,597 · see citing works
Neural Message Passing for Quantum ChemistryPreprint
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,414 · see citing works
Self-citationA Chemist's Guide to Density Functional Theory
Koch, W., & Holthausen, M. C. (2001) · Wiley eBooks · doi:10.1002/3527600043 · Cited by 4,890 · see citing works
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Williams, C. K. I. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s269 · Cited by 4,390 · see citing works
Bayesian Learning for Neural Networks
Neal, R. M. (1996) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-0745-0 · Cited by 4,276 · see citing works
Handbook of Molecular Descriptors
Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107 · cited 1× · see citing works
“41 The basic idea, typical also for other cheminfor-matics descriptors 42 (e.g. the signature descriptor 43,44 ) is to represent a molecule as the set of subgraphs up to a fixed diameter (here we use ECFP4, which is a max diameter of 4 bonds).”
BackgroundAn introduction to kernel-based learning algorithms
Müller, K. R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914517 · Cited by 3,485 · see citing works
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572 · see citing works
Challenges for Density Functional Theory
Cohen, A. J., Mori‐Sánchez, P., & Yang, W. (2011) · Chemical Reviews · doi:10.1021/cr200107z · Cited by 2,529 · see citing works
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312 · see citing works
Density-Functional Theory of Atoms and Molecules
Parr, R. G., & Weitao, Y. (1995) · Oxford University Press eBooks · doi:10.1093/oso/9780195092769.001.0001 · Cited by 2,237 · see citing works
Curtiss, L. A., Raghavachari, K., Redfern, P. C., & Pople, J. A. (1997) · The Journal of Chemical Physics · doi:10.1063/1.473182 · Cited by 2,128 · see citing works
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966 · see citing works
Self-citationANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921 · see citing works
Perspective on density functional theory
Burke, K. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4704546 · Cited by 1,758 · see citing works
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631 · see citing works
Self-citationEnumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
Ruddigkeit, L., van Deursen, R., Blum, L. C., & Reymond, J.-L. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300415d · Cited by 1,505 · see citing works
Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017) · Nature Communications · doi:10.1038/ncomms13890 · Cited by 1,445 · see citing works
Density functional theory is straying from the path toward the exact functional
Medvedev, M. G., Bushmarinov, I. S., Sun, J., Perdew, J. P., & Lyssenko, K. A. (2017) · Science · doi:10.1126/science.aah5975 · Cited by 953 · see citing works
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00099 · Cited by 911 · see citing works
Self-citationLarge-scale prediction and testing of drug activity on side-effect targets
Lounkine, E., Keiser, M. J., Whitebread, S., Mikhailov, D., Hamon, J., Jenkins, J. L., Lavan, P., Weber, E., Doak, A. K., Côté, S., et al. (2012) · Nature · doi:10.1038/nature11159 · Cited by 879 · see citing works
Automated design of ligands to polypharmacological profiles
Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X.-P., Norval, S., Sassano, M. F., Shin, A. I., Webster, L. A., et al. (2012) · Nature · doi:10.1038/nature11691 · Cited by 832 · see citing works
Ridge Regression: Biased Estimation for Nonorthogonal Problems
Hoerl, A. E., & Kennard, R. W. (2000) · Technometrics · doi:10.1080/00401706.2000.10485983 · Cited by 820 · see citing works
Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b00831 · Cited by 815 · see citing works
Self-citationComparing molecules and solids across structural and alchemical space
De, S., Bartók, A. P., Csányi, G., & Ceriotti, M. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp00415f · Cited by 778 · see citing works
Machine learning unifies the modeling of materials and molecules
Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017) · Science Advances · doi:10.1126/sciadv.1701816 · Cited by 764 · see citing works
What Do the Kohn−Sham Orbitals and Eigenvalues Mean?
Stowasser, R., & Hoffmann, R. (1999) · Journal of the American Chemical Society · doi:10.1021/ja9826892 · Cited by 683 · see citing works
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., von Lilienfeld, O. A., Tkatchenko, A., & Müller, K. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400195d · Cited by 615 · see citing works
Sinha, P., Boesch, S. E., Gu, C., et al. (2004) · The Journal of Physical Chemistry A · doi:10.1021/jp048233q · Cited by 474 · see citing works
Plata, R. E., & Singleton, D. A. (2015) · Journal of the American Chemical Society · doi:10.1021/ja5111392 · Cited by 446 · see citing works
Orthogonalization corrections for semiempirical methods
Weber, W., & Thiel, W. (2000) · Theoretical Chemistry Accounts · doi:10.1007/s002149900083 · Cited by 433 · see citing works
Unified representation of molecules and crystals for machine learning
Huo, H., & Rupp, M. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/aca005 · Cited by 392 · see citing works
Kirkpatrick, P. N., & Ellis, C. (2004) · Nature · doi:10.1038/432823a · Cited by 340 · see citing works
Huigens, R. W., Morrison, K. C., Hicklin, R. W., Flood, T. A., Richter, M. F., & Hergenrother, P. J. (2013) · Nature Chemistry · doi:10.1038/nchem.1549 · Cited by 323 · see citing works
Hickey, A. L., & Rowley, C. N. (2014) · The Journal of Physical Chemistry A · doi:10.1021/jp502475e · Cited by 319 · see citing works
Huang, B., & von Lilienfeld, O. A. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4964627 · Cited by 294 · see citing works
Self-citationVovk, V. (2013) · Springer eBooks · doi:10.1007/978-3-642-41136-6_11 · Cited by 290 · see citing works
The Signature Molecular Descriptor. 1. Using Extended Valence Sequences in QSAR and QSPR Studies
Faulon, J., Visco, D. P., & Pophale, R. S. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci020345w · Cited by 234 · see citing works
von Lilienfeld, O. A., Ramakrishnan, R., Rupp, M., & Knoll, A. M. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24912 · Cited by 218 · see citing works
THE RATIO OF THE MEAN DEVIATION TO THE STANDARD DEVIATION AS A TEST OF NORMALITY
Geary, R. C. (1935) · Biometrika · doi:10.1093/biomet/27.3-4.310 · Cited by 194 · see citing works
Accelerated materials property predictions and design using motif-based fingerprints
Huan, T. D., Mannodi‐Kanakkithodi, A., & Ramprasad, R. (2015) · Physical Review B · doi:10.1103/physrevb.92.014106 · Cited by 169 · see citing works
Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations
Dral, P. O., von Lilienfeld, O. A., & Thiel, W. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00141 · Cited by 111 · see citing works
Visco, D. P., Pophale, R. S., Rintoul, M. D., & Faulon, J. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00144-9 · Cited by 109 · see citing works
Basis Set Limit CCSD(T) Harmonic Vibrational Frequencies
Tew, D. P., Klopper, W., Heckert, M., & Gauß, J. (2007) · The Journal of Physical Chemistry A · doi:10.1021/jp070851u · Cited by 103 · see citing works
Dral, P. O., Wu, X. M., Spörkel, L., et al. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b01047 · Cited by 96 · see citing works
Many Molecular Properties from One Kernel in Chemical Space
(2015) · CHIMIA International Journal for Chemistry · doi:10.2533/chimia.2015.182 · Cited by 83 · see citing works
A Numerical Study on Learning Curves in Stochastic Multilayer Feedforward Networks
Müller, K. R., Finke, M., Murata, N., Schulten, K., & Амари, Ш.-И. (1996) · Neural Computation · doi:10.1162/neco.1996.8.5.1085 · Cited by 70 · see citing works
Ghasemi, S. A., Amsler, M., Hennig, R. G., et al. (2010) · Physical Review B · doi:10.1103/physrevb.81.214107 · Cited by 35 · see citing works
LC-GAP: Localized Coulomb Descriptors for the Gaussian Approximation Potential
Barker, J., Bulin, J., Hamaekers, J., & Mathias, S. (2017) · Springer eBooks · doi:10.1007/978-3-319-62458-7_2 · Cited by 27 · see citing works
DeTar, D. F. (2007) · The Journal of Physical Chemistry A · doi:10.1021/jp066312r · Cited by 17 · see citing works
Constant Size Molecular Descriptors For Use With Machine LearningPreprint
Collins, C. R., Gordon, G. J., von Lilienfeld, O. A., & Yaron, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.06649 · Cited by 12 · see citing works
Localized Coulomb Descriptors for the Gaussian Approximation PotentialPreprint
Barker, J., Bulin, J., Hamaekers, J., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05126 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968 · see citing works
SchNet – A deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217 · see citing works
Analyzing Learned Molecular Representations for Property Prediction
Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00237 · Cited by 1,923 · see citing works
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chen, C., Ye, W., Zuo, Y., Zheng, C., & Ong, S. P. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b01294 · Cited by 1,552 · see citing works
Recent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172 · see citing works
“There have been several previous attempts to represent crystals and molecules using fi xed-size descriptors such as Coulomb matrix 87 – 89 , classical force fi eld inspired descriptors (CFID) 90 – 92 , pair-distribution function (PRDF), Voronoi tessellation 93 – 95 .”
BackgroundMachine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069 · see citing works
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852 · see citing works
Machine learning unifies the modeling of materials and molecules
Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017) · Science Advances · doi:10.1126/sciadv.1701816 · Cited by 764 · see citing works
Deep learning for molecular design—a review of the state of the art
Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683 · see citing works
A Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works
Mater, A. C., & Coote, M. L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00266 · Cited by 565 · see citing works
Machine learning molecular dynamics for the simulation of infrared spectra
Gastegger, M., Behler, J., & Marquetand, P. (2017) · Chemical Science · doi:10.1039/c7sc02267k · Cited by 556 · see citing works
Imbalzano, G., Anelli, A., Giofré, D., Klees, S., Behler, J., & Ceriotti, M. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5024611 · Cited by 330 · see citing works
Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b08750 · Cited by 289 · see citing works
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
Janet, J. P., Duan, C., Yang, T., Nandy, A., & Kulik, H. J. (2019) · Chemical Science · doi:10.1039/c9sc02298h · Cited by 243 · see citing works
Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
Meyer, B., Sawatlon, B., Heinen, S., von Lilienfeld, O. A., & Corminboeuf, C. (2018) · Chemical Science · doi:10.1039/c8sc01949e · Cited by 209 · see citing works
Accurate molecular polarizabilities with coupled cluster theory and machine learning
Wilkins, D. M., Grisafi, A., Yang, Y., Lao, K. U., DiStasio, R. A., & Ceriotti, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816132116 · Cited by 204 · see citing works
Janet, J. P., Ramesh, S., Duan, C., & Kulik, H. J. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00026 · Cited by 200 · see citing works
Janet, J. P., Chan, L., & Kulik, H. J. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00170 · Cited by 200 · see citing works
Model agnostic generation of counterfactual explanations for molecules
Wellawatte, G. P., Seshadri, A., & White, A. D. (2022) · Chemical Science · doi:10.1039/d1sc05259d · Cited by 143 · see citing works
Willatt, M. J., Musil, F., & Ceriotti, M. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp05921g · Cited by 141 · see citing works
tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes
Balcells, D., & Skjelstad, B. B. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01041 · Cited by 143 · see citing works
Janet, J. P., Liu, F., Nandy, A., Duan, C., Yang, T., Lin, S., & Kulik, H. J. (2019) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.9b00109 · Cited by 114 · see citing works
Machine learning in materials design: Algorithm and application*
Song, Z., Chen, X., Meng, F., Cheng, G., Wang, C., Sun, Z., & Yin, W.-J. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abc0e3 · Cited by 63 · see citing works
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
Kranz, J. J., Kubillus, M., Ramakrishnan, R., von Lilienfeld, O. A., & Elstner, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00933 · Cited by 63 · see citing works
Self-citationEnumeration of <i>de novo</i> inorganic complexes for chemical discovery and machine learning
Gugler, S., Janet, J. P., & Kulik, H. J. (2020) · Molecular Systems Design & Engineering · doi:10.1039/c9me00069k · Cited by 43 · see citing works
Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment
Iovanac, N. C., & Savoie, B. M. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b01398 · Cited by 41 · see citing works
- Authors
- Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A.
- Affiliations
- Google (United States) (US)
- University of Basel (CH)
- Google (United Kingdom) (GB)
- Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
- ORCID
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-10-10
- Volume
- 13
- Issue
- 11
- Pages
- 5255-5264
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2753962198
- Cited by
- 651
- References
- 62
- Citations by year
- 2026: 28 · 2025: 58 · 2024: 61 · 2023: 67 · 2022: 65 · 2021: 89 · 2020: 122 · 2019: 72 · 2018: 79 · 2017: 10
- FWCI
- 32.95
- Type
- journal article
- Language
- en
- Open access
- green
- edoc.unibas.ch
- Licence
- other-oa
- Locations
- Journal of Chemical Theory and Computation (publishedVersion)
- PubMed (publishedVersion)
- edoc (University of Basel) (acceptedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- edoc (University of Basel)
- Funders
- Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung (310030_160067, PP00P2_138932)
- Air Force Office of Scientific Research (FA9550-15-1-0026)
- Forschungsfonds, Universit?t Basel
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- hybrid density functional theory, QM9, molecular representation, Coulomb matrix, ECFP4, molecular graphs, elastic net, kernel ridge regression, graph convolutional networks, random forest, enthalpy of atomization, HOMO-LUMO gap, dipole moment, polarizability, vibration characteristics, chemical accuracy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
