Combining state-of-the-art quantum chemistry and machine learning make gold standard potential energy surfaces accessible for medium-sized molecules Article Swipe
Apurba Nandi
,
Péter R. Nagy
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2023-njjn0
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2023-njjn0
Developing full-dimensional machine-learned potentials with the current gold-standard coupled-cluster (CC) level is a challenging already for medium-sized molecules due to the high computational cost. Consequently, researchers are often bound to use lower-level electronic structure methods such as density functional theory or second-order Moller-Plesset perturbation theory (MP2). Here, we demonstrate on a representative example that gold-standard potentials can now be effectively constructed for molecules of 15 atoms using off-the-shelf hardware. This is achieved by accelerating the CCSD(T) computations via the accurate and cost-effective frozen natural orbital (FNO) approach. The Delta-machine learning (Delta-ML) approach is employed with the use of permutationally invariant polynomials to fit a full-dimensional PES of the acetylacetone molecule, but any other effective descriptor and ML approach can similarly benefit from the accelerated data generation proposed here. Our benchmarks for the global minima, H-transfer TS, and many high-lying configurations show the excellent agreement of FNO-CCSD(T) results with conventional CCSD(T) while achieving a significant time advantage of about a factor of 30-40. The obtained Delta-ML PES shows high fidelity from multiple perspectives including energetic, structural, and vibrational properties. We obtain the symmetric double well H-transfer barrier of 3.15 kcal/mol in excellent agreement with the direct FNO-CCSD(T) barrier of 3.11 kcal/mol as well as with the benchmark CCSD(F12*)(T+)/CBS value of 3.21 kcal/mol. Furthermore, the tunneling splitting due to H-atom transfer is calculated using a 1D double-well potential, providing improved estimates over previous ones obtained using an MP2-based PES. The methodology introduced here represents a significant advancement in the efficient and precise construction of potentials at the CCSD(T) level for molecules above the current limit of 15 atoms.
Related Topics
Concepts
Molecule
Density functional theory
Maxima and minima
Perturbation theory (quantum mechanics)
Quantum
Quantum chemistry
Potential energy surface
Atom (system on chip)
Chemistry
Physics
Computational chemistry
Atomic physics
Quantum mechanics
Computer science
Mathematics
Mathematical analysis
Supramolecular chemistry
Embedded system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2023-njjn0
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/65770feafd283d7904c26304/original/combining-state-of-the-art-quantum-chemistry-and-machine-learning-make-gold-standard-potential-energy-surfaces-accessible-for-medium-sized-molecules.pdf
- OA Status
- gold
- References
- 75
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389617564
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389617564Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26434/chemrxiv-2023-njjn0Digital Object Identifier
- Title
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Combining state-of-the-art quantum chemistry and machine learning make gold standard potential energy surfaces accessible for medium-sized moleculesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-12-12Full publication date if available
- Authors
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Apurba Nandi, Péter R. NagyList of authors in order
- Landing page
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https://doi.org/10.26434/chemrxiv-2023-njjn0Publisher landing page
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/65770feafd283d7904c26304/original/combining-state-of-the-art-quantum-chemistry-and-machine-learning-make-gold-standard-potential-energy-surfaces-accessible-for-medium-sized-molecules.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/65770feafd283d7904c26304/original/combining-state-of-the-art-quantum-chemistry-and-machine-learning-make-gold-standard-potential-energy-surfaces-accessible-for-medium-sized-molecules.pdfDirect OA link when available
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Molecule, Density functional theory, Maxima and minima, Perturbation theory (quantum mechanics), Quantum, Quantum chemistry, Potential energy surface, Atom (system on chip), Chemistry, Physics, Computational chemistry, Atomic physics, Quantum mechanics, Computer science, Mathematics, Mathematical analysis, Supramolecular chemistry, Embedded systemTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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75Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 75 |
| abstract_inverted_index.a | 12, 50, 103, 152, 158, 222, 242 |
| abstract_inverted_index.15 | 64, 264 |
| abstract_inverted_index.1D | 223 |
| abstract_inverted_index.ML | 116 |
| abstract_inverted_index.We | 178 |
| abstract_inverted_index.an | 234 |
| abstract_inverted_index.as | 36, 200, 202 |
| abstract_inverted_index.at | 253 |
| abstract_inverted_index.be | 58 |
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| abstract_inverted_index.in | 189, 245 |
| abstract_inverted_index.is | 11, 70, 92, 219 |
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| abstract_inverted_index.or | 40 |
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| abstract_inverted_index.we | 47 |
| abstract_inverted_index.Our | 128 |
| abstract_inverted_index.PES | 105, 165 |
| abstract_inverted_index.TS, | 135 |
| abstract_inverted_index.The | 87, 162, 237 |
| abstract_inverted_index.and | 80, 115, 136, 175, 248 |
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| abstract_inverted_index.via | 77 |
| abstract_inverted_index.(CC) | 9 |
| abstract_inverted_index.3.11 | 198 |
| abstract_inverted_index.3.15 | 187 |
| abstract_inverted_index.3.21 | 209 |
| abstract_inverted_index.PES. | 236 |
| abstract_inverted_index.This | 69 |
| abstract_inverted_index.data | 124 |
| abstract_inverted_index.from | 121, 169 |
| abstract_inverted_index.here | 240 |
| abstract_inverted_index.high | 21, 167 |
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| abstract_inverted_index.ones | 231 |
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| abstract_inverted_index.show | 140 |
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| abstract_inverted_index.that | 53 |
| abstract_inverted_index.time | 154 |
| abstract_inverted_index.well | 183, 201 |
| abstract_inverted_index.with | 4, 94, 147, 192, 203 |
| abstract_inverted_index.(FNO) | 85 |
| abstract_inverted_index.Here, | 46 |
| abstract_inverted_index.about | 157 |
| abstract_inverted_index.above | 259 |
| abstract_inverted_index.atoms | 65 |
| abstract_inverted_index.bound | 28 |
| abstract_inverted_index.cost. | 23 |
| abstract_inverted_index.here. | 127 |
| abstract_inverted_index.level | 10, 256 |
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| abstract_inverted_index.other | 112 |
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| abstract_inverted_index.using | 66, 221, 233 |
| abstract_inverted_index.value | 207 |
| abstract_inverted_index.while | 150 |
| abstract_inverted_index.(MP2). | 45 |
| abstract_inverted_index.30-40. | 161 |
| abstract_inverted_index.H-atom | 217 |
| abstract_inverted_index.atoms. | 265 |
| abstract_inverted_index.direct | 194 |
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| abstract_inverted_index.global | 132 |
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| abstract_inverted_index.barrier | 185, 196 |
| abstract_inverted_index.benefit | 120 |
| abstract_inverted_index.current | 6, 261 |
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| abstract_inverted_index.minima, | 133 |
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| abstract_inverted_index.orbital | 84 |
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| abstract_inverted_index.results | 146 |
| abstract_inverted_index.Delta-ML | 164 |
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| abstract_inverted_index.achieved | 71 |
| abstract_inverted_index.approach | 91, 117 |
| abstract_inverted_index.employed | 93 |
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| abstract_inverted_index.improved | 227 |
| abstract_inverted_index.kcal/mol | 188, 199 |
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| abstract_inverted_index.transfer | 218 |
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| abstract_inverted_index.achieving | 151 |
| abstract_inverted_index.advantage | 155 |
| abstract_inverted_index.agreement | 143, 191 |
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| abstract_inverted_index.benchmark | 205 |
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| abstract_inverted_index.excellent | 142, 190 |
| abstract_inverted_index.hardware. | 68 |
| abstract_inverted_index.including | 172 |
| abstract_inverted_index.invariant | 99 |
| abstract_inverted_index.kcal/mol. | 210 |
| abstract_inverted_index.molecule, | 109 |
| abstract_inverted_index.molecules | 17, 62, 258 |
| abstract_inverted_index.providing | 226 |
| abstract_inverted_index.similarly | 119 |
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| abstract_inverted_index.tunneling | 213 |
| abstract_inverted_index.(Delta-ML) | 90 |
| abstract_inverted_index.Developing | 0 |
| abstract_inverted_index.H-transfer | 134, 184 |
| abstract_inverted_index.benchmarks | 129 |
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| abstract_inverted_index.energetic, | 173 |
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| abstract_inverted_index.introduced | 239 |
| abstract_inverted_index.potential, | 225 |
| abstract_inverted_index.potentials | 3, 55, 252 |
| abstract_inverted_index.represents | 241 |
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| abstract_inverted_index.accelerated | 123 |
| abstract_inverted_index.advancement | 244 |
| abstract_inverted_index.challenging | 13 |
| abstract_inverted_index.constructed | 60 |
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| abstract_inverted_index.double-well | 224 |
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| abstract_inverted_index.polynomials | 100 |
| abstract_inverted_index.properties. | 177 |
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| abstract_inverted_index.significant | 153, 243 |
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| abstract_inverted_index.vibrational | 176 |
| abstract_inverted_index.Furthermore, | 211 |
| abstract_inverted_index.accelerating | 73 |
| abstract_inverted_index.computations | 76 |
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| abstract_inverted_index.perspectives | 171 |
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| abstract_inverted_index.second-order | 41 |
| abstract_inverted_index.Consequently, | 24 |
| abstract_inverted_index.Delta-machine | 88 |
| abstract_inverted_index.acetylacetone | 108 |
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| abstract_inverted_index.off-the-shelf | 67 |
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