Selected configuration interaction dressed by perturbation

We introduce a new variant of the complete active space second-order perturbation theory (CASPT2) method that performs similarly to multistate CASPT2 (MS-CASPT2) in regions of the potential energy surface where the electronic states are energetically well separated and is akin to extended MS-CASPT2 (XMS-CASPT2) in case the underlying zeroth-order references are near-degenerate. Our approach follows a recipe analogous to XMS-CASPT2 to ensure approximate invariance under unitary transformations of the model states and a dynamical weighting scheme to smoothly interpolate the Fock operator between a statespecific and a state-average regime. The resulting extended dynamically weighted CASPT2 (XDW-CASPT2) methodology possesses the most desirable features of both MS-CASPT2 and XMS-CASPT2, i.e. the ability to provide accurate transition energies and correctly describe avoided crossings and conical intersections. The reliability of XDW-CASPT2 is assessed on a number of molecular systems. First, we consider the dissociation of lithium fluoride, highlighting the distinctive characteristics of the new approach. Second, the invariance of the theory is investigated by studying the conical intersection of the distorted allene molecule. Finally, the relative accuracy in the calculation of vertical excitation energies is benchmarked on a set of 26 organic compounds. We found that XDW-CASPT2, al-beit being only approximately invariant, produces smooth potential energy surfaces around conical intersections and avoided crossings, performing equally well to the strictly invariant XMS-CASPT2 method. The accuracy of vertical transition energies is almost identical to MS-CASPT2, with a mean absolute deviation of 0.01 to 0.02 eV, in contrast to 0.12 eV for XMS-CASPT2.

Section: Conical Intersection In Allenementioning

confidence: 99%

Extended Dynamically Weighted CASPT2: The Best of Two Worlds

Battaglia

Lindh

2020

“…The increase of computational ressources coupled to the emergence of more advanced algorithms has led to a resurgence of the selected configuration interaction (SCI) approaches [1][2][3] as an effective strategy to rapidly reach the full CI (FCI) limit at a fraction of the cost of a genuine FCI calculation thanks to a sparse exploration of the FCI space. [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] This revival is especially beneficial for the calculation of transition energies between electronic states, 6,[15][16][17][18][19][20][21][22][23] as the accurate determination of these energies remains one of the great challenges faced by theoretical chemists.…”

Section: Introductionmentioning

confidence: 99%

A Mountaineering Strategy to Excited States: Highly Accurate Energies and Benchmarks for Medium Sized Molecules

Loos

Lipparini

Boggio‐Pasqua

et al. 2020

Self Cite

179

270

“…34 This implementation of CIPSI has been further developed to efficiently create pure spin states 35 and to provide a dressed perturbation approach. 36 Alternative methods to using perturbation theory to select configurations have also been created. For example, the Λ-CI approach 37 uses the expectation value of the configuration's energy.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Configuration Interaction for ab Initio Potential Energy Curves

Coe

2019

The concept of machine learning configuration interaction (MLCI) [J. Chem. Theory Comput. 2018, 14, 5739], where an artificial neural network (ANN) learns on the fly to select important configurations, is further developed so that accurate ab initio potential energy curves can be efficiently calculated. This development includes employing the artificial neural network also as a hash function for the efficient deletion of duplicates on the fly so that the singles and doubles space does not need to be stored and this barrier to scalability is removed. In addition configuration state functions are introduced into the approach so that pure spin states are guaranteed, and the transferability of data between geometries is exploited. This improved approach is demonstrated on potential energy curves for the nitrogen molecule, water, and carbon monoxide. The results are compared with full configuration interaction values, when available, and different transfer protocols are investigated. It is shown that, for all of the considered systems, accurate potential energy curves can now be efficiently computed with MLCI. For the potential curves of N 2 and CO, MLCI can achieve lower errors than stochastically selecting configurations while also using substantially less processor hours.