Evidence map›Paper›PMID 38680571›Full record

ArticleACS central science2024

Organic Reactivity Made Easy and Accurate with Automated Multireference Calculations.

Jacob J Wardzala, Daniel S King, Lawal Ogunfowora, Brett Savoie, Laura Gagliardi

Abstract read
In one paragraph

Article in ACS central science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Cartesian equivariant representations for learning and understanding molecular orbitals.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Weighted active space protocol for multireference machine-learned potentials.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jacob J WardzalaDepartment of Chemistry,University of Chicago, Chicago, Illinois 60637, United States.ORCID https://orcid.org/0000-0003-0496-5566
Daniel S KingDepartment of Chemistry,University of Chicago, Chicago, Illinois 60637, United States.ORCID https://orcid.org/0000-0003-0208-5274
Lawal OgunfoworaDavidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States.
Brett SavoieDavidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States.ORCID https://orcid.org/0000-0002-7039-4039
Laura GagliardiDepartment of Chemistry, Pritzker School of Molecular Engineering, James Franck Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States.ORCID https://orcid.org/0000-0001-5227-1396

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In organic reactivity studies, quantum chemical calculations play a pivotal role as the foundation of understanding and machine learning model development. While prevalent black-box methods like density functional theory (DFT) and coupled-cluster theory (e.g., CCSD(T)) have significantly advanced our understanding of chemical reactivity, they frequently fall short in describing multiconfigurational transition states and intermediates. Achieving a more accurate description necessitates the use of multireference methods. However, these methods have not been used at scale due to their often-faulty predictions without expert input. Here, we overcome this deficiency with automated multiconfigurational pair-density functional theory (MC-PDFT) calculations. We apply this method to 908 automatically generated organic reactions. We find 68% of these reactions present significant multiconfigurational character in which the automated multiconfigurational approach often provides a more accurate and/or efficient description than DFT and CCSD(T). This work presents the first high-throughput application of automated multiconfigurational methods to reactivity, enabled by automated active space selection algorithms and the computation of electronic correlation with MC-PDFT on-top functionals. This approach can be used in a black-box fashion, avoiding significant active space inconsistency error in both single- and multireference cases and providing accurate multiconfigurational descriptions when needed.

Identifiers

PMID38680571
PMCPMC11046455

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.