Benchmarking Computational Alchemy for Carbide, Nitride, and Oxide Catalysts

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

doi:10.26434/chemrxiv.7122089.v1

Cited by 9 publications

(16 citation statements)

References 6 publications

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“…Conversely, this argument explains why ∆Z is the most important feature. It was also reported in our previous work [29,30] (and shown in Figure 1) that for a larger nuclear charge perturbation, the errors are larger, regardless of the location of the change. Thus, for the part of the dataset where the change is large (ie.…”

Section: Modelsupporting

confidence: 81%

“…In our previous work [29,30], we showed that the accuracy of APDFT approximations requires that N T and ∆Z should both be small. In general, this means that APDFT approximations are most accurate when the hypothetical system does not have more than a single atom transmuted by more than one group of the periodic Here the binomial notation is used for showing the combinatorics of one transmuted atom in any of the eight atomic sites in the top two layers of this system, and the prefactor of 2 refers to the fact that transmutations could be either +1 (into Au) or -1 (into Ir).…”

Section: Introductionmentioning

confidence: 95%

“…APDFT has also been tested for predicting material properties of bulk transition metals, [27] semiconductor band gaps and stability, [26,28] and molecular adsorptions on nanoparticles and extended surfaces. [22,24,29,30] The majority of these applications have used the Taylor series expression truncated to first order, which thus assigns a linear relationship between the energy perturbation and the alchemical derivatives of the reference shown in Eq. 2…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Models Correct Systematic Errors in Alchemical Perturbation Density Functional Theory Applications to Catalysis

Griego¹,

Zhao²,

Saravanan³

et al. 2020

Preprint

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Alchemical perturbation density functional theory (APDFT) has great promise for enabling rapid and accurate computational screening of hypothetical catalyst sites, but first order approximations are unsatisfactorily inaccurate when alchemical derivatives are large. In this work, we analyze errors in first order APDFT calculation schemes for binding energies of CHx, NHx, OHx, and OOH adsorbates over a range of different coverages on hypothetical alloys based on a Pt(111) reference system. We then construct feature vectors by fingerprinting the dopant locations in the alloy and then use a data set of about 11,100 data points to 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 can approximate reasonably 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 in principle extends this number to more than 20,000 and provides a recipe for developing other machine learning models to aid future high throughput screening studies.

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Section: Modelsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Models Correct Systematic Errors in Alchemical Perturbation Density Functional Theory Applications to Catalysis

Griego¹,

Zhao²,

Saravanan³

et al. 2020

Preprint

Self Cite

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show abstract

“…In our previous work, 29,30 we showed that the accuracy of APDFT approximations requires that N T and ΔZ should both be small.…”

mentioning

confidence: 95%

“…[24][25][26] APDFT has also been tested for predicting material properties of bulk transition metals, 27 semiconductor band gaps and stability, 26,28 and molecular adsorptions on nanoparticles and extended surfaces. 22,24,29,30 The majority of these applications have used the Taylor series expression truncated to first order, which thus assigns a linear relationship between the energy perturbation and the alchemical derivatives of the reference shown in Equation 2.…”

mentioning

confidence: 99%

Machine learning corrected alchemical perturbation density functional theory for catalysis applications

et al. 2020

Self Cite

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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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Simplifying inverse materials design problems for fixed lattices with alchemical chirality

Rudorff

Lilienfeld

2021

Sci. Adv.

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Brute-force compute campaigns relying on demanding ab initio calculations routinely search for previously unknown materials in chemical compound space (CCS), the vast set of all conceivable stable combinations of elements and structural configurations. Here, we demonstrate that four-dimensional chirality arising from antisymmetry of alchemical perturbations dissects CCS and defines approximate ranks, which reduce its formal dimensionality and break down its combinatorial scaling. The resulting “alchemical” enantiomers have the same electronic energy up to the third order, independent of respective covalent bond topology, imposing relevant constraints on chemical bonding. Alchemical chirality deepens our understanding of CCS and enables the establishment of trends without empiricism for any materials with fixed lattices. We demonstrate the efficacy for three cases: (i) new rules for electronic energy contributions to chemical bonding; (ii) analysis of the electron density of BN-doped benzene; and (iii) ranking over 2000 and 4 million BN-doped naphthalene and picene derivatives, respectively.

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Benchmarking Computational Alchemy for Carbide, Nitride, and Oxide Catalysts

Cited by 9 publications

References 6 publications

Machine Learning Models Correct Systematic Errors in Alchemical Perturbation Density Functional Theory Applications to Catalysis

Machine Learning Models Correct Systematic Errors in Alchemical Perturbation Density Functional Theory Applications to Catalysis

Machine learning corrected alchemical perturbation density functional theory for catalysis applications

Simplifying inverse materials design problems for fixed lattices with alchemical chirality

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