---
res:
  bibo_abstract:
  - Machine learning models are poised to make a transformative impact on chemical
    sciences by dramatically accelerating computational algorithms and amplifying
    insights available from computational chemistry methods. However, achieving this
    requires a confluence and coaction of expertise in computer science and physical
    sciences. This review is written for new and experienced researchers working at
    the intersection of both fields. We first provide concise tutorials of computational
    chemistry and machine learning methods, showing how insights involving both can
    be achieved. We then follow with a critical review of noteworthy applications
    that demonstrate how computational chemistry and machine learning can be used
    together to provide insightful (and useful) predictions in molecular and materials
    modeling, retrosyntheses, catalysis, and drug design.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: John A.
      foaf_name: Keith, John A.
      foaf_surname: Keith
  - foaf_Person:
      foaf_givenName: Valentin
      foaf_name: Valentin Vassilev-Galindo, Valentin
      foaf_surname: Valentin Vassilev-Galindo
  - foaf_Person:
      foaf_givenName: Bingqing
      foaf_name: Cheng, Bingqing
      foaf_surname: Cheng
      foaf_workInfoHomepage: http://www.librecat.org/personId=cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
    orcid: 0000-0002-3584-9632
  - foaf_Person:
      foaf_givenName: Stefan
      foaf_name: Chmiela, Stefan
      foaf_surname: Chmiela
  - foaf_Person:
      foaf_givenName: Michael
      foaf_name: Gastegger, Michael
      foaf_surname: Gastegger
  - foaf_Person:
      foaf_givenName: Klaus-Robert
      foaf_name: Müller, Klaus-Robert
      foaf_surname: Müller
  - foaf_Person:
      foaf_givenName: Alexandre
      foaf_name: Tkatchenko, Alexandre
      foaf_surname: Tkatchenko
  bibo_doi: 10.1021/acs.chemrev.1c00107
  bibo_issue: '16'
  bibo_volume: 121
  dct_date: 2021^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0009-2665
  - http://id.crossref.org/issn/1520-6890
  dct_language: eng
  dct_publisher: American Chemical Society@
  dct_title: Combining machine learning and computational chemistry for predictive
    insights into chemical systems@
...