Machine learning corrected alchemical perturbation density functional theory for catalysis applications

Griego, Charles D.; Zhao, Lingyan; Saravanan, Karthikeyan; Keith, John A.

doi:10.1002/aic.17041

Cited by 17 publications

(16 citation statements)

References 53 publications

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“… 351 In 2020, neural networks have been proposed for the prediction of overpotentials relevant for heterogeneous catalyst candidates, 352 as well as a higher-order correction scheme in alchemical perturbation density functional theory applications to catalytic activity. 260 An overview on machine learning for computational heterogeneous catalysis was also contributed in 2019. 353 …”

Section: Propertiesmentioning

confidence: 99%

Ab Initio Machine Learning in Chemical Compound Space

2021

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Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling of this space, for example, in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest subsets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an ab initio view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics.

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Section: Propertiesmentioning

confidence: 99%

Ab Initio Machine Learning in Chemical Compound Space

2021

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“…678 Almost trivially simple ML approaches can be used in catalysis studies to deduce insights into interaction trends between single metal atoms and oxide supports, 679 to identify the significance of features (e.g. adsorbate type or coverage) where CompChem theories break down, 680 or they can be used to identify trends that result in optimal catalysis across multiple objectives such as activity and cost (Fig. 15).…”

Section: Catalysismentioning

confidence: 99%

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Keith,

Vassilev-Galindo,

Cheng

et al. 2021

Preprint

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Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We then follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

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“…The most common mapping deals with the electronic exchange-correlation energy [433], [434], [435], [436], [437], [438], [439], [440], [441], [442], [443], [444], [445], [446], [447], [448], [449], [450], [451], [452], [453], [454]. As discussed in Sec.…”

Section: Learning Electronic Structuresmentioning

confidence: 99%

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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Machine learning corrected alchemical perturbation density functional theory for catalysis applications

Cited by 17 publications

References 53 publications

Ab Initio Machine Learning in Chemical Compound Space

Ab Initio Machine Learning in Chemical Compound Space

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

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

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