Benchmarking Computational Alchemy for Carbide, Nitride, and Oxide Catalysts

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

doi:10.1002/adts.201800142

“…We now provide a tutorial on how a simple form of alchemical perturbation density functional theory (APDFT) can be used for computational catalysis applications. [7,[18][19][20] To begin, we consider the BE calculation for an OH molecule on Pt(111). This system will be referred to as our reference state and subsequently labeled with λ = 0.…”

Section: Alchemical Perturbation Density Functional Theorymentioning

confidence: 99%

“…This allows bandgaps to collapse, and computational alchemy appears to be valid. [20] Future work will address the physical F I G U R E 5 A, Energy profiles for the CH 4 dehydrogenation mechanism on hypothetical alloys of Pt. The reference pathway occurs on pure Pt and is denoted with red asterisks.…”

Section: Usability and Reproducibility With Computational Alchemymentioning

confidence: 99%

Acceleration of catalyst discovery with easy, fast, and reproducible computational alchemy

Griego

¹

,

Kitchin

²

,

Keith

³

2020

Int J of Quantum Chemistry

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The expense of quantum chemistry calculations significantly hinders the search for novel catalysts. Here, we provide a tutorial for using an easy and highly cost-efficient calculation scheme, called alchemical perturbation density functional theory (APDFT), for rapid predictions of binding energies of reaction intermediates and reaction barrier heights based on the Kohn-Sham density functional theory (DFT) reference data. We outline standard procedures used in computational catalysis applications, explain how computational alchemy calculations can be carried out for those applications, and then present benchmarking studies of binding energy and barrier height predictions. Using a single OH binding energy on the Pt(111) surface as a reference case, we use computational alchemy to predict binding energies of 32 variations of this system with a mean unsigned error of less than 0.05 eV relative to single-point DFT calculations. Using a single nudged elastic band calculation for CH 4 dehydrogenation on Pt(111) as a reference case, we generate 32 new pathways with barrier heights having mean unsigned errors of less than 0.3 eV relative to single-point DFT calculations. Notably, this easy APDFT scheme brings no appreciable computational cost once reference calculations are performed, and this shows that simple applications of computational alchemy can significantly impact DFT-driven explorations for catalysts. To accelerate computational catalysis discovery and ensure computational reproducibility, we also include Python modules that allow users to perform their own computational alchemy calculations. K E Y W O R D S adsorption energies, barrier heights, binding energies, computational catalysis, density functional theory, nudged elastic band calculations 1 | INTRODUCTION Advances in computational chemistry open new possibilities for impressively large-scale computational screening of hypothetical catalysts across materials space. [1-3] However, productively leveraging high-throughput screening has been challenging. For useful and insightful predictions, computational screening studies must be reproducible while also (a) determining important active sites that are stable under specified environmental conditions on large numbers of material compositions and (b) elucidating important elementary reaction steps with barrier heights that are

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“…We now provide a tutorial on how a simple form of APDFT can be employed for computational catalysis applications [18], [19], [7], [20] . To begin, we consider the BE calculation for an OH molecule on Pt(111).…”

Section: Apdftmentioning

confidence: 99%

“…[7] Another limitation is that these simple APDFT treatments appear not to be valid for materials that have a band gap. [20] While more is being learned about the promise and limitations, we see APDFT as a potentially transformative model for applied computational chemistry communities and especially for computational catalysis. We will now show benchmarking calculations to validate the use of computational alchemy for predictions of BE and E a in two different examples.…”

Section: And the Expression Inmentioning

confidence: 99%

“…This allows bandgaps to collapse, and computational alchemy appears to be valid. [20] Future work will address the physical descriptions of these systems and their relation to existing models such as the d-band model. We [7] and other groups [6] have also noted that predictions from first order APDFT become less accurate with the magnitude of the alchemical derivatives that are in play.…”

Section: Usability and Reproducibility With Computational Alchemymentioning

confidence: 99%

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Acceleration of Catalyst Discovery with Easy, Fast, and Reproducible Computational Alchemy

Griego

¹

,

Kitchin

²

,

Keith

³

2020

Preprint

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The expense of quantum chemistry calculations significantly hinders the search for novel catalysts. Here, we provide a tutorial for using an easy and highly cost-efficient calculation scheme called alchemical perturbation density functional theory (APDFT) for rapid predictions of binding energies of reaction intermediates and reaction barrier heights based on Kohn-Sham density functional theory reference data. We outline standard procedures used in computational catalysis applications, explain how computational alchemy calculations can be carried out for those applications, and then present bench marking studies of binding energy and barrier height predictions. Using a single OH binding energy on the Pt(111) surface as a reference case, we use computational alchemy to predict binding energies of 32 variations of this system with a mean unsigned error of less than 0.05 eV relative to single-point DFT calculations. Using a single nudged elastic band calculation for CH 4 dehydrogenation on Pt(111) as a reference case, we generate 32 new pathways with barrier heights having mean unsigned errors of less than 0.3 eV relative to single-point DFT calculations. Notably, this easy APDFT scheme brings no appreciable computational cost once reference calculations are done, and this shows that simple applications of computational alchemy can significantly impact DFT-driven explorations for catalysts. To accelerate computational catalysis discovery and ensure computational reproducibility, we also include Python modules that allow users to perform their own computational alchemy calculations.

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Machine learning corrected alchemical perturbation density functional theory for catalysis applications

Griego

¹

,

Zhao

²

,

Saravanan

³

et al. 2020

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Alchemical perturbation density functional theory (APDFT) has promise for enabling computational screening of hypothetical catalyst sites. Here, we analyze errors in first order APDFT calculation schemes for binding energies of CH x , NH x , OH x , and OOH adsorbates over a range of different coverages on hypothetical alloys based on a Pt(111) reference system. We then train three different support vector regression machine learning models that correct systematic APDFT prediction errors for each of the three classes of carbon, nitrogen, and oxygen based adsorbates. While uncorrected first order APDFT alone approximates accurate adsorbate binding energies on up to 36 hypothetical alloys based on a single Kohn-Sham DFT calculation on a 3 × 3 unit cell for Pt(111), the machine learning-corrected APDFT extends this number to more than 20,000 and provides a recipe for developing other machine learning-based APDFT models. K E Y W O R D S adsorption, binding energies, high throughput screening 1 | INTRODUCTION Many efforts in computational catalysis are focused on screening materials for innovative catalysts that promote high chemical activity. 1-3 Descriptors for catalyst activity, for example, an adsorbate binding

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Benchmarking Computational Alchemy for Carbide, Nitride, and Oxide Catalysts

Cited by 20 publications

References 34 publications

Acceleration of catalyst discovery with easy, fast, and reproducible computational alchemy

Acceleration of catalyst discovery with easy, fast, and reproducible computational alchemy

Acceleration of Catalyst Discovery with Easy, Fast, and Reproducible Computational Alchemy

Machine learning corrected alchemical perturbation density functional theory for catalysis applications

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