Predicting Chemical Reaction Barriers with a Machine Learning Model View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-16

AUTHORS

Aayush R. Singh, Brian A. Rohr, Joseph A. Gauthier, Jens K. Nørskov

ABSTRACT

In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst’s affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. In our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity. More... »

PAGES

1-8

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10562-019-02705-x

DOI

http://dx.doi.org/10.1007/s10562-019-02705-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112829675


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0306", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Chemistry (incl. Structural)", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/03", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Chemical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "SLAC National Accelerator Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.445003.6", 
          "name": [
            "Department of Chemical Engineering, SUNCAT Center for Interface Science and Catalysis, Stanford University, 94305, Stanford, CA, USA", 
            "SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, 2575 Sand Hill Road, 94025, Menlo Park, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Singh", 
        "givenName": "Aayush R.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "SLAC National Accelerator Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.445003.6", 
          "name": [
            "Department of Chemical Engineering, SUNCAT Center for Interface Science and Catalysis, Stanford University, 94305, Stanford, CA, USA", 
            "SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, 2575 Sand Hill Road, 94025, Menlo Park, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rohr", 
        "givenName": "Brian A.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "SLAC National Accelerator Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.445003.6", 
          "name": [
            "Department of Chemical Engineering, SUNCAT Center for Interface Science and Catalysis, Stanford University, 94305, Stanford, CA, USA", 
            "SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, 2575 Sand Hill Road, 94025, Menlo Park, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gauthier", 
        "givenName": "Joseph A.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Technical University of Denmark", 
          "id": "https://www.grid.ac/institutes/grid.5170.3", 
          "name": [
            "Department of Chemical Engineering, SUNCAT Center for Interface Science and Catalysis, Stanford University, 94305, Stanford, CA, USA", 
            "SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, 2575 Sand Hill Road, 94025, Menlo Park, CA, USA", 
            "Department of Physics, Denmark Technical University, 2800, Kgs. Lyngby, Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "N\u00f8rskov", 
        "givenName": "Jens K.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.cpc.2016.05.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009522604"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcat.2006.02.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021157589"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c1cp20547a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024156140"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0360-0564(02)45013-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031250828"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.jpclett.5b01660", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055113977"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.jpclett.6b01254", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055114528"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ct400195d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055424727"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1323224", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057694620"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1329672", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057695436"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.480097", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058073990"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.41.7892", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060554329"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.41.7892", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060554329"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2283900", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069860493"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ncomms14621", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084128853", 
          "https://doi.org/10.1038/ncomms14621"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c7cp00375g", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084915296"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acscatal.7b01648", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090971920"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41467-017-00839-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092102030", 
          "https://doi.org/10.1038/s41467-017-00839-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.119.150601", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092172333"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.119.150601", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092172333"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.5023563", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104438967"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevmaterials.2.083802", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106063218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevmaterials.2.083802", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106063218"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-03-16", 
    "datePublishedReg": "2019-03-16", 
    "description": "In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst\u2019s affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. In our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity. ", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10562-019-02705-x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1126974", 
        "issn": [
          "1011-372X", 
          "1572-879X"
        ], 
        "name": "Catalysis Letters", 
        "type": "Periodical"
      }
    ], 
    "name": "Predicting Chemical Reaction Barriers with a Machine Learning Model", 
    "pagination": "1-8", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "f90947a055a0e3b7dbb51e5d0bb51cca06b95f7c7899ed3e74bb44137e38f704"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10562-019-02705-x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112829675"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10562-019-02705-x", 
      "https://app.dimensions.ai/details/publication/pub.1112829675"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:07", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000360_0000000360/records_118342_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10562-019-02705-x"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10562-019-02705-x'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10562-019-02705-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10562-019-02705-x'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10562-019-02705-x'


 

This table displays all metadata directly associated to this object as RDF triples.

137 TRIPLES      21 PREDICATES      43 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10562-019-02705-x schema:about anzsrc-for:03
2 anzsrc-for:0306
3 schema:author N42ceecf181134880952c7e0916b9d6b7
4 schema:citation sg:pub.10.1038/ncomms14621
5 sg:pub.10.1038/s41467-017-00839-3
6 https://doi.org/10.1016/j.cpc.2016.05.010
7 https://doi.org/10.1016/j.jcat.2006.02.016
8 https://doi.org/10.1016/s0360-0564(02)45013-4
9 https://doi.org/10.1021/acs.jpclett.5b01660
10 https://doi.org/10.1021/acs.jpclett.6b01254
11 https://doi.org/10.1021/acscatal.7b01648
12 https://doi.org/10.1021/ct400195d
13 https://doi.org/10.1039/c1cp20547a
14 https://doi.org/10.1039/c7cp00375g
15 https://doi.org/10.1063/1.1323224
16 https://doi.org/10.1063/1.1329672
17 https://doi.org/10.1063/1.480097
18 https://doi.org/10.1063/1.5023563
19 https://doi.org/10.1103/physrevb.41.7892
20 https://doi.org/10.1103/physrevlett.119.150601
21 https://doi.org/10.1103/physrevmaterials.2.083802
22 https://doi.org/10.2307/2283900
23 schema:datePublished 2019-03-16
24 schema:datePublishedReg 2019-03-16
25 schema:description In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst’s affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. In our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity.
26 schema:genre research_article
27 schema:inLanguage en
28 schema:isAccessibleForFree false
29 schema:isPartOf sg:journal.1126974
30 schema:name Predicting Chemical Reaction Barriers with a Machine Learning Model
31 schema:pagination 1-8
32 schema:productId N25e20c037312499686d7d0b9ab34e501
33 N653df3e263e24ff9bfbc8a37aea65f40
34 N9fa7508835cf4d63b806e4093d0af67a
35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112829675
36 https://doi.org/10.1007/s10562-019-02705-x
37 schema:sdDatePublished 2019-04-11T12:07
38 schema:sdLicense https://scigraph.springernature.com/explorer/license/
39 schema:sdPublisher N59e4160b1ec449e484a93f6c83189927
40 schema:url https://link.springer.com/10.1007%2Fs10562-019-02705-x
41 sgo:license sg:explorer/license/
42 sgo:sdDataset articles
43 rdf:type schema:ScholarlyArticle
44 N108c5880b68948ad8091da7454979e37 rdf:first Ne52ef2ed52c0478d92e7dddcf02315ef
45 rdf:rest N4f7b34a7ff9544c48ef42fdd911cf5ce
46 N25e20c037312499686d7d0b9ab34e501 schema:name doi
47 schema:value 10.1007/s10562-019-02705-x
48 rdf:type schema:PropertyValue
49 N284fe4d65a6e42088dd582b1853bd524 schema:affiliation https://www.grid.ac/institutes/grid.445003.6
50 schema:familyName Singh
51 schema:givenName Aayush R.
52 rdf:type schema:Person
53 N42ceecf181134880952c7e0916b9d6b7 rdf:first N284fe4d65a6e42088dd582b1853bd524
54 rdf:rest N83b6bfb5a5bf4c0ca2521067928d36fc
55 N4f7b34a7ff9544c48ef42fdd911cf5ce rdf:first N6626ccc3c4254496ac9ec433f72d2e6d
56 rdf:rest rdf:nil
57 N59e4160b1ec449e484a93f6c83189927 schema:name Springer Nature - SN SciGraph project
58 rdf:type schema:Organization
59 N653df3e263e24ff9bfbc8a37aea65f40 schema:name dimensions_id
60 schema:value pub.1112829675
61 rdf:type schema:PropertyValue
62 N6626ccc3c4254496ac9ec433f72d2e6d schema:affiliation https://www.grid.ac/institutes/grid.5170.3
63 schema:familyName Nørskov
64 schema:givenName Jens K.
65 rdf:type schema:Person
66 N83b6bfb5a5bf4c0ca2521067928d36fc rdf:first Nfbf486f26a5f490b8cf00af8975df6c2
67 rdf:rest N108c5880b68948ad8091da7454979e37
68 N9fa7508835cf4d63b806e4093d0af67a schema:name readcube_id
69 schema:value f90947a055a0e3b7dbb51e5d0bb51cca06b95f7c7899ed3e74bb44137e38f704
70 rdf:type schema:PropertyValue
71 Ne52ef2ed52c0478d92e7dddcf02315ef schema:affiliation https://www.grid.ac/institutes/grid.445003.6
72 schema:familyName Gauthier
73 schema:givenName Joseph A.
74 rdf:type schema:Person
75 Nfbf486f26a5f490b8cf00af8975df6c2 schema:affiliation https://www.grid.ac/institutes/grid.445003.6
76 schema:familyName Rohr
77 schema:givenName Brian A.
78 rdf:type schema:Person
79 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
80 schema:name Chemical Sciences
81 rdf:type schema:DefinedTerm
82 anzsrc-for:0306 schema:inDefinedTermSet anzsrc-for:
83 schema:name Physical Chemistry (incl. Structural)
84 rdf:type schema:DefinedTerm
85 sg:journal.1126974 schema:issn 1011-372X
86 1572-879X
87 schema:name Catalysis Letters
88 rdf:type schema:Periodical
89 sg:pub.10.1038/ncomms14621 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084128853
90 https://doi.org/10.1038/ncomms14621
91 rdf:type schema:CreativeWork
92 sg:pub.10.1038/s41467-017-00839-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092102030
93 https://doi.org/10.1038/s41467-017-00839-3
94 rdf:type schema:CreativeWork
95 https://doi.org/10.1016/j.cpc.2016.05.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009522604
96 rdf:type schema:CreativeWork
97 https://doi.org/10.1016/j.jcat.2006.02.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021157589
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1016/s0360-0564(02)45013-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031250828
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1021/acs.jpclett.5b01660 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055113977
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1021/acs.jpclett.6b01254 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055114528
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1021/acscatal.7b01648 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090971920
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1021/ct400195d schema:sameAs https://app.dimensions.ai/details/publication/pub.1055424727
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1039/c1cp20547a schema:sameAs https://app.dimensions.ai/details/publication/pub.1024156140
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1039/c7cp00375g schema:sameAs https://app.dimensions.ai/details/publication/pub.1084915296
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1063/1.1323224 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057694620
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1063/1.1329672 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057695436
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1063/1.480097 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058073990
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1063/1.5023563 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104438967
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1103/physrevb.41.7892 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060554329
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1103/physrevlett.119.150601 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092172333
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1103/physrevmaterials.2.083802 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106063218
126 rdf:type schema:CreativeWork
127 https://doi.org/10.2307/2283900 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069860493
128 rdf:type schema:CreativeWork
129 https://www.grid.ac/institutes/grid.445003.6 schema:alternateName SLAC National Accelerator Laboratory
130 schema:name Department of Chemical Engineering, SUNCAT Center for Interface Science and Catalysis, Stanford University, 94305, Stanford, CA, USA
131 SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, 2575 Sand Hill Road, 94025, Menlo Park, CA, USA
132 rdf:type schema:Organization
133 https://www.grid.ac/institutes/grid.5170.3 schema:alternateName Technical University of Denmark
134 schema:name Department of Chemical Engineering, SUNCAT Center for Interface Science and Catalysis, Stanford University, 94305, Stanford, CA, USA
135 Department of Physics, Denmark Technical University, 2800, Kgs. Lyngby, Denmark
136 SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, 2575 Sand Hill Road, 94025, Menlo Park, CA, USA
137 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...