Can chemometrics be used to guide the selection of suitable DFT functionals?

Venkatraman, Vishwesh; Abburu, Sailesh; Alsberg, Bjørn K.

doi:10.1016/j.chemolab.2015.01.013

Cited by 6 publications

(3 citation statements)

References 68 publications

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“…This research can be classified as either dealing with more technical aspects on combining ML and DFT (see Sec. III E) or simply mappings or calculations not discussed yet [477], [478], [479], [480], [481], [482], [483], [484], [414] [485], [486], [487], [488].…”

Section: Other Approachesmentioning

confidence: 99%

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A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

Fiedler¹,

Shah²,

Bußmann³

et al. 2021

Preprint

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With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise to accelerate large-scale simulations and their analysis to hitherto unattainable scales. Machine learning is a rapidly growing field for the processing of such complex datasets. It has recently gained traction in the domain of electronic structure simulations, where density functional theory takes the prominent role of the most widely used electronic structure method. Thus, DFT calculations represent one of the largest loads on academic high-performance computing systems across the world. Accelerating these with machine learning can reduce the resources required and enables simulations of larger systems. Hence, the combination of density functional theory and machine learning has the potential to rapidly advance electronic structure applications such as in-silico materials discovery and the search for new chemical reaction pathways. We provide the theoretical background of both density functional theory and machine learning on a generally accessible level. This serves as the basis of our comprehensive review including research articles up to December 2020 in chemistry and materials science that employ machine-learning techniques. In our analysis, we categorize the body of research into main threads and extract impactful results. We conclude our review with an outlook on exciting research directions in terms of a citation analysis.

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Section: Other Approachesmentioning

confidence: 99%

“…Similarly, in Ref. [480] the choice of the exchange-correlation functional is predicted from chemical descriptors. Finally, DFT-based models are also integrated into elaborate statistical mechanics frameworks as in Ref.…”

Section: Other Approachesmentioning

confidence: 99%

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

Fiedler¹,

Shah²,

Bußmann³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…These errors can be minimised by either tuning existing DFT functionals to suit the molecular system being studied or rely on machine learning algorithms such as, partial least squares, support vector machines and random forests, to help predict the best DFT functionals for new molecules obtained from the de novo design. 80 Some of the spurious results observed for the CAM-B3LYP functional may be due to the fact that it does not incorporate a full range separation. The functional has only 65% HF exchange at long range instead of the correct 100% asymptotic HF exchange.…”

Section: Moleculementioning

confidence: 99%