The Experts below are selected from a list of 903 Experts worldwide ranked by ideXlab platform
Joel M Bowman - One of the best experts on this subject based on the ideXlab platform.
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δ machine learning for potential energy surfaces a pip approach to bring a dft based pes to ccsd t level of theory
Journal of Chemical Physics, 2021Co-Authors: Apurba Nandi, P L Houston, Riccardo Conte, Joel M BowmanAbstract:“Δ-machine learning” refers to a machine learning approach to bring a property such as a potential energy surface (PES) based on low-level (LL) density functional theory (DFT) energies and gradients close to a coupled cluster (CC) level of accuracy. Here, we present such an approach that uses the permutationally invariant polynomial (PIP) method to fit high-dimensional PESs. The approach is represented by a simple equation, in Obvious Notation VLL→CC = VLL + ΔVCC–LL, and demonstrated for CH4, H3O+, and trans and cis-N-methyl acetamide (NMA), CH3CONHCH3. For these molecules, the LL PES, VLL, is a PIP fit to DFT/B3LYP/6-31+G(d) energies and gradients and ΔVCC–LL is a precise PIP fit obtained using a low-order PIP basis set and based on a relatively small number of CCSD(T) energies. For CH4, these are new calculations adopting an aug-cc-pVDZ basis, for H3O+, previous CCSD(T)-F12/aug-cc-pVQZ energies are used, while for NMA, new CCSD(T)-F12/aug-cc-pVDZ calculations are performed. With as few as 200 CCSD(T) energies, the new PESs are in excellent agreement with benchmark CCSD(T) results for the small molecules, and for 12-atom NMA, training is done with 4696 CCSD(T) energies.
Apurba Nandi - One of the best experts on this subject based on the ideXlab platform.
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δ machine learning for potential energy surfaces a pip approach to bring a dft based pes to ccsd t level of theory
Journal of Chemical Physics, 2021Co-Authors: Apurba Nandi, P L Houston, Riccardo Conte, Joel M BowmanAbstract:“Δ-machine learning” refers to a machine learning approach to bring a property such as a potential energy surface (PES) based on low-level (LL) density functional theory (DFT) energies and gradients close to a coupled cluster (CC) level of accuracy. Here, we present such an approach that uses the permutationally invariant polynomial (PIP) method to fit high-dimensional PESs. The approach is represented by a simple equation, in Obvious Notation VLL→CC = VLL + ΔVCC–LL, and demonstrated for CH4, H3O+, and trans and cis-N-methyl acetamide (NMA), CH3CONHCH3. For these molecules, the LL PES, VLL, is a PIP fit to DFT/B3LYP/6-31+G(d) energies and gradients and ΔVCC–LL is a precise PIP fit obtained using a low-order PIP basis set and based on a relatively small number of CCSD(T) energies. For CH4, these are new calculations adopting an aug-cc-pVDZ basis, for H3O+, previous CCSD(T)-F12/aug-cc-pVQZ energies are used, while for NMA, new CCSD(T)-F12/aug-cc-pVDZ calculations are performed. With as few as 200 CCSD(T) energies, the new PESs are in excellent agreement with benchmark CCSD(T) results for the small molecules, and for 12-atom NMA, training is done with 4696 CCSD(T) energies.
P L Houston - One of the best experts on this subject based on the ideXlab platform.
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δ machine learning for potential energy surfaces a pip approach to bring a dft based pes to ccsd t level of theory
Journal of Chemical Physics, 2021Co-Authors: Apurba Nandi, P L Houston, Riccardo Conte, Joel M BowmanAbstract:“Δ-machine learning” refers to a machine learning approach to bring a property such as a potential energy surface (PES) based on low-level (LL) density functional theory (DFT) energies and gradients close to a coupled cluster (CC) level of accuracy. Here, we present such an approach that uses the permutationally invariant polynomial (PIP) method to fit high-dimensional PESs. The approach is represented by a simple equation, in Obvious Notation VLL→CC = VLL + ΔVCC–LL, and demonstrated for CH4, H3O+, and trans and cis-N-methyl acetamide (NMA), CH3CONHCH3. For these molecules, the LL PES, VLL, is a PIP fit to DFT/B3LYP/6-31+G(d) energies and gradients and ΔVCC–LL is a precise PIP fit obtained using a low-order PIP basis set and based on a relatively small number of CCSD(T) energies. For CH4, these are new calculations adopting an aug-cc-pVDZ basis, for H3O+, previous CCSD(T)-F12/aug-cc-pVQZ energies are used, while for NMA, new CCSD(T)-F12/aug-cc-pVDZ calculations are performed. With as few as 200 CCSD(T) energies, the new PESs are in excellent agreement with benchmark CCSD(T) results for the small molecules, and for 12-atom NMA, training is done with 4696 CCSD(T) energies.
Riccardo Conte - One of the best experts on this subject based on the ideXlab platform.
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δ machine learning for potential energy surfaces a pip approach to bring a dft based pes to ccsd t level of theory
Journal of Chemical Physics, 2021Co-Authors: Apurba Nandi, P L Houston, Riccardo Conte, Joel M BowmanAbstract:“Δ-machine learning” refers to a machine learning approach to bring a property such as a potential energy surface (PES) based on low-level (LL) density functional theory (DFT) energies and gradients close to a coupled cluster (CC) level of accuracy. Here, we present such an approach that uses the permutationally invariant polynomial (PIP) method to fit high-dimensional PESs. The approach is represented by a simple equation, in Obvious Notation VLL→CC = VLL + ΔVCC–LL, and demonstrated for CH4, H3O+, and trans and cis-N-methyl acetamide (NMA), CH3CONHCH3. For these molecules, the LL PES, VLL, is a PIP fit to DFT/B3LYP/6-31+G(d) energies and gradients and ΔVCC–LL is a precise PIP fit obtained using a low-order PIP basis set and based on a relatively small number of CCSD(T) energies. For CH4, these are new calculations adopting an aug-cc-pVDZ basis, for H3O+, previous CCSD(T)-F12/aug-cc-pVQZ energies are used, while for NMA, new CCSD(T)-F12/aug-cc-pVDZ calculations are performed. With as few as 200 CCSD(T) energies, the new PESs are in excellent agreement with benchmark CCSD(T) results for the small molecules, and for 12-atom NMA, training is done with 4696 CCSD(T) energies.
Bowman, Joel M. - One of the best experts on this subject based on the ideXlab platform.
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Delta-Machine Learning for Potential Energy Surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) Level of Theory
'AIP Publishing', 2020Co-Authors: Nandi Apurba, Qu Chen, Houston Paul, Conte Riccardo, Bowman, Joel M.Abstract:"$\Delta$-machine learning" refers to a machine learning approach to bring a property such as a potential energy surface based on low-level (LL) DFT energies and gradients to close to a coupled-cluster (CC) level of accuracy. Here we present such an approach that uses the permutationally invariant polynomial (PIP) method to fit high-dimensional PESs. The approach is represented by a simple equation, in Obvious Notation, $V_{LL{\rightarrow}CC}=V_{LL}+\Delta{V_{CC-LL}}$. The difference potential $\Delta{V_{CC-LL}}$ is obtained using a relatively small number of coupled-cluster energies and precisely fit using a low-order PIP basis. The approach is demonstrated for CH$_4$ and H$_3$O$^+$. For both molecules, the LL PES, $V_{LL}$, is a PIP fit to DFT/B3LYP/6-31+G(d) energies and gradients, and $\Delta{V_{CC-LL}}$ is a PIP fit based on CCSD(T) energies. For CH$_4$ these are new calculations using an aug-cc-pVDZ basis, while for H$_3$O$^+$ previous CCSD(T)-F12/aug-cc-pVQZ energies are used. With as few as 200 CCSD(T) energies, the new PESs are in excellent agreement with benchmark CCSD(T) results
