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Abstract Kohn–Sham density functional theory (DFT)‐based searches for hypothetical catalysts are too computationally demanding for wide searches through diverse materials space. Here, the accuracy of computational alchemy schemes on carbides, nitrides, and oxides is assessed. With a single set of reference DFT calculations, computational alchemy approximates adsorbate binding energies (BEs) on a large number of hypothetical catalysts surfaces with negligible computational cost. Analogous to previous studies on metal alloys, computational alchemy predicts adsorbate BEs on rocksalt TiC(111), TiN(100), and TiO(100) materials, which have no bandgap, in close agreement with DFT results (with mean unsigned errors up to 0.33 eV). In contrast, it is found that semiconducting systems such as rutile TiO2(110), rutile SnO2(110), and rocksalt ZnO(100) can present more significant challenges. This work identifies these challenges being linked to the density of states at the Fermi level and by adding Pt dopants in the surface layer of TiO2, it is shown that computational alchemy can become more reliable with non‐transition metal systems. This remedy provides insight that promotes computational alchemy for broad searches for catalyst active sites through materials space beyond transition metal alloys.
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Machine learning corrected alchemical perturbation density functional theory for catalysis applications
Abstract 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 CHx, NHx, OHx, 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.
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- Award ID(s):
- 1856460
- PAR ID:
- 10455598
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AIChE Journal
- Volume:
- 66
- Issue:
- 12
- ISSN:
- 0001-1541
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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