Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited

Zaspel, Peter; Huang, Bing; Harbrecht, Helmut; Lilienfeld, O. Anatole von

doi:10.1021/acs.jctc.8b00832

Cited by 105 publications

(189 citation statements)

References 86 publications

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“…[5][6][7] Further studies demonstrate the advantages of predicting the differences between low-and high-fidelity calculations (i.e., Δ-Learning), [8] or of using multiple levels of fidelity to train the same model. [9,10] It is also possible to link easily-computable properties to the outcomes of extensive calculations, as shown by how Seko et al used calculated bulk moduli as inputs to a model that predicts melting temperature. [11] Neural networks offer more possibilities through the ability to train the same model on multiple properties (e.g., multi-task or transfer learning).…”

Section: Introductionmentioning

confidence: 99%

Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations

et al. 2019

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Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the tradeoff between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies, and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than 9 heavy atoms and 0.012 eV for a small set of molecules with between 10 and 14 heavy atoms. Our two best models, which have different accuracy/speed tradeoffs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.

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Section: Introductionmentioning

confidence: 99%

Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations

et al. 2019

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“…the positions of all the atoms) of a material is known, ML may in principle provide the same information about the material as a DFT calculation would: structural stability, phonon dispersion relations, elastic constants etc. It might even in principle provide data of a better quality than standard (semi-)local DFT calculations, comparable to more advanced DFT calculations with hybrid functionals or even higher-level methods as recently demonstrated for molecules [20].…”

Section: Introductionmentioning

confidence: 84%

Materials property prediction using symmetry-labeled graphs as atomic position independent descriptors

et al. 2019

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Computational materials screening studies require fast calculation of the properties of thousands of materials. The calculations are often performed with Density Functional Theory (DFT), but the necessary computer time sets limitations for the investigated material space. Therefore, the development of machine learning models for prediction of DFT calculated properties are currently of interest. A particular challenge for new materials is that the atomic positions are generally not known. We present a machine learning model for the prediction of DFT-calculated formation energies based on Voronoi quotient graphs and local symmetry classification without the need for detailed information about atomic positions. The model is implemented as a message passing neural network and tested on the Open Quantum Materials Database (OQMD) and the Materials Project database. The test mean absolute error is 20 meV on the OQMD database and 40 meV on Materials Project Database. The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe3 selenides with very limited overlap with the OQMD training set. Pretraining on OQMD and subsequent training on 100 selenides result in a mean absolute error below 0.1 eV for the formation energy of the selenides. arXiv:1905.06048v2 [cond-mat.mtrl-sci]

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“…Computational chemistry is naturally a sub-field that has been increasingly boosted by the advances and unique capabilities of ML (Rupp et al, 2012;Ramakrishnan et al, 2014Ramakrishnan et al, , 2015Dral et al, 2015;Sánchez-Lengeling and Aspuru-Guzik, 2017;Christensen et al, 2019;Iype and Urolagin, 2019;Mezei and Von Lilienfeld, 2019;Zaspel et al, 2019).…”

Section: Improving Computational and Quantum Chemistrymentioning

confidence: 99%

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

2019

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Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.

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Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited

Cited by 105 publications

References 86 publications

Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations

Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations

Materials property prediction using symmetry-labeled graphs as atomic position independent descriptors

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

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