Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning Article Swipe
Sheng Gong
,
Shuo Wang
,
Tian Xie
,
Woo Hyun Chae
,
Runze Liu
,
Yang Shao‐Horn
,
Jeffrey C. Grossman
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1021/jacsau.2c00235
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1021/jacsau.2c00235
The application of machine learning to predict materials properties measured by experiments are valuable yet difficult due to the limited amount of experimental data. In this work, we use a multifidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the Perdew-Burke-Ernzerhof (PBE) functional with linear correction, PBEsol, and meta-generalized gradient approximation (meta-GGA) functionals (SCAN and r2SCAN), and it outperforms the hotly studied deep neural network-based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database and discover materials with underestimated stability. The multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output.
Related Topics
Concepts
Stability (learning theory)
Enthalpy
Representation (politics)
Artificial neural network
Work (physics)
Computer science
Density functional theory
Machine learning
Experimental data
Artificial intelligence
Standard enthalpy change of formation
Thermodynamics
Algorithm
Statistical physics
Chemistry
Mathematics
Computational chemistry
Physics
Statistics
Law
Politics
Political science
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/jacsau.2c00235
- OA Status
- gold
- Cited By
- 36
- References
- 107
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286895367
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4286895367Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1021/jacsau.2c00235Digital Object Identifier
- Title
-
Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-09Full publication date if available
- Authors
-
Sheng Gong, Shuo Wang, Tian Xie, Woo Hyun Chae, Runze Liu, Yang Shao‐Horn, Jeffrey C. GrossmanList of authors in order
- Landing page
-
https://doi.org/10.1021/jacsau.2c00235Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1021/jacsau.2c00235Direct OA link when available
- Concepts
-
Stability (learning theory), Enthalpy, Representation (politics), Artificial neural network, Work (physics), Computer science, Density functional theory, Machine learning, Experimental data, Artificial intelligence, Standard enthalpy change of formation, Thermodynamics, Algorithm, Statistical physics, Chemistry, Mathematics, Computational chemistry, Physics, Statistics, Law, Politics, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
36Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 14, 2023: 10, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
107Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4286895367 |
|---|---|
| doi | https://doi.org/10.1021/jacsau.2c00235 |
| ids.doi | https://doi.org/10.1021/jacsau.2c00235 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/36186569 |
| ids.openalex | https://openalex.org/W4286895367 |
| fwci | 4.42805598 |
| type | article |
| title | Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning |
| biblio.issue | 9 |
| biblio.volume | 2 |
| biblio.last_page | 1977 |
| biblio.first_page | 1964 |
| topics[0].id | https://openalex.org/T11948 |
| topics[0].field.id | https://openalex.org/fields/25 |
| topics[0].field.display_name | Materials Science |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2505 |
| topics[0].subfield.display_name | Materials Chemistry |
| topics[0].display_name | Machine Learning in Materials Science |
| topics[1].id | https://openalex.org/T12613 |
| topics[1].field.id | https://openalex.org/fields/25 |
| topics[1].field.display_name | Materials Science |
| topics[1].score | 0.9947999715805054 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2505 |
| topics[1].subfield.display_name | Materials Chemistry |
| topics[1].display_name | X-ray Diffraction in Crystallography |
| topics[2].id | https://openalex.org/T10211 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9873999953269958 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1703 |
| topics[2].subfield.display_name | Computational Theory and Mathematics |
| topics[2].display_name | Computational Drug Discovery Methods |
| funders[0].id | https://openalex.org/F4320316783 |
| funders[0].ror | |
| funders[0].display_name | Toyota Research Institute, North America |
| is_xpac | False |
| apc_list.value | 5000 |
| apc_list.currency | USD |
| apc_list.value_usd | 5000 |
| apc_paid.value | 5000 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 5000 |
| concepts[0].id | https://openalex.org/C112972136 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7174936532974243 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q7595718 |
| concepts[0].display_name | Stability (learning theory) |
| concepts[1].id | https://openalex.org/C3288061 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6723985075950623 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q161064 |
| concepts[1].display_name | Enthalpy |
| concepts[2].id | https://openalex.org/C2776359362 |
| concepts[2].level | 3 |
| concepts[2].score | 0.6106241941452026 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2145286 |
| concepts[2].display_name | Representation (politics) |
| concepts[3].id | https://openalex.org/C50644808 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5864148139953613 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[3].display_name | Artificial neural network |
| concepts[4].id | https://openalex.org/C18762648 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5608925819396973 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q42213 |
| concepts[4].display_name | Work (physics) |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.498288631439209 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C152365726 |
| concepts[6].level | 2 |
| concepts[6].score | 0.49537432193756104 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1048589 |
| concepts[6].display_name | Density functional theory |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.47883346676826477 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C55037315 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4593281149864197 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q5421151 |
| concepts[8].display_name | Experimental data |
| concepts[9].id | https://openalex.org/C154945302 |
| concepts[9].level | 1 |
| concepts[9].score | 0.45437806844711304 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[9].display_name | Artificial intelligence |
| concepts[10].id | https://openalex.org/C59801894 |
| concepts[10].level | 3 |
| concepts[10].score | 0.4240446388721466 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q721706 |
| concepts[10].display_name | Standard enthalpy change of formation |
| concepts[11].id | https://openalex.org/C97355855 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3422868251800537 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11473 |
| concepts[11].display_name | Thermodynamics |
| concepts[12].id | https://openalex.org/C11413529 |
| concepts[12].level | 1 |
| concepts[12].score | 0.33661743998527527 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[12].display_name | Algorithm |
| concepts[13].id | https://openalex.org/C121864883 |
| concepts[13].level | 1 |
| concepts[13].score | 0.3326820731163025 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q677916 |
| concepts[13].display_name | Statistical physics |
| concepts[14].id | https://openalex.org/C185592680 |
| concepts[14].level | 0 |
| concepts[14].score | 0.25940752029418945 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[14].display_name | Chemistry |
| concepts[15].id | https://openalex.org/C33923547 |
| concepts[15].level | 0 |
| concepts[15].score | 0.2228688895702362 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[15].display_name | Mathematics |
| concepts[16].id | https://openalex.org/C147597530 |
| concepts[16].level | 1 |
| concepts[16].score | 0.1807056963443756 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q369472 |
| concepts[16].display_name | Computational chemistry |
| concepts[17].id | https://openalex.org/C121332964 |
| concepts[17].level | 0 |
| concepts[17].score | 0.17625859379768372 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[17].display_name | Physics |
| concepts[18].id | https://openalex.org/C105795698 |
| concepts[18].level | 1 |
| concepts[18].score | 0.10149809718132019 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[18].display_name | Statistics |
| concepts[19].id | https://openalex.org/C199539241 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[19].display_name | Law |
| concepts[20].id | https://openalex.org/C94625758 |
| concepts[20].level | 2 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q7163 |
| concepts[20].display_name | Politics |
| concepts[21].id | https://openalex.org/C17744445 |
| concepts[21].level | 0 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[21].display_name | Political science |
| keywords[0].id | https://openalex.org/keywords/stability |
| keywords[0].score | 0.7174936532974243 |
| keywords[0].display_name | Stability (learning theory) |
| keywords[1].id | https://openalex.org/keywords/enthalpy |
| keywords[1].score | 0.6723985075950623 |
| keywords[1].display_name | Enthalpy |
| keywords[2].id | https://openalex.org/keywords/representation |
| keywords[2].score | 0.6106241941452026 |
| keywords[2].display_name | Representation (politics) |
| keywords[3].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[3].score | 0.5864148139953613 |
| keywords[3].display_name | Artificial neural network |
| keywords[4].id | https://openalex.org/keywords/work |
| keywords[4].score | 0.5608925819396973 |
| keywords[4].display_name | Work (physics) |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.498288631439209 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/density-functional-theory |
| keywords[6].score | 0.49537432193756104 |
| keywords[6].display_name | Density functional theory |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.47883346676826477 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/experimental-data |
| keywords[8].score | 0.4593281149864197 |
| keywords[8].display_name | Experimental data |
| keywords[9].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[9].score | 0.45437806844711304 |
| keywords[9].display_name | Artificial intelligence |
| keywords[10].id | https://openalex.org/keywords/standard-enthalpy-change-of-formation |
| keywords[10].score | 0.4240446388721466 |
| keywords[10].display_name | Standard enthalpy change of formation |
| keywords[11].id | https://openalex.org/keywords/thermodynamics |
| keywords[11].score | 0.3422868251800537 |
| keywords[11].display_name | Thermodynamics |
| keywords[12].id | https://openalex.org/keywords/algorithm |
| keywords[12].score | 0.33661743998527527 |
| keywords[12].display_name | Algorithm |
| keywords[13].id | https://openalex.org/keywords/statistical-physics |
| keywords[13].score | 0.3326820731163025 |
| keywords[13].display_name | Statistical physics |
| keywords[14].id | https://openalex.org/keywords/chemistry |
| keywords[14].score | 0.25940752029418945 |
| keywords[14].display_name | Chemistry |
| keywords[15].id | https://openalex.org/keywords/mathematics |
| keywords[15].score | 0.2228688895702362 |
| keywords[15].display_name | Mathematics |
| keywords[16].id | https://openalex.org/keywords/computational-chemistry |
| keywords[16].score | 0.1807056963443756 |
| keywords[16].display_name | Computational chemistry |
| keywords[17].id | https://openalex.org/keywords/physics |
| keywords[17].score | 0.17625859379768372 |
| keywords[17].display_name | Physics |
| keywords[18].id | https://openalex.org/keywords/statistics |
| keywords[18].score | 0.10149809718132019 |
| keywords[18].display_name | Statistics |
| language | en |
| locations[0].id | doi:10.1021/jacsau.2c00235 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210207119 |
| locations[0].source.issn | 2691-3704 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2691-3704 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | JACS Au |
| locations[0].source.host_organization | https://openalex.org/P4310320006 |
| locations[0].source.host_organization_name | American Chemical Society |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320006 |
| locations[0].source.host_organization_lineage_names | American Chemical Society |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | JACS Au |
| locations[0].landing_page_url | https://doi.org/10.1021/jacsau.2c00235 |
| locations[1].id | pmid:36186569 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | JACS Au |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/36186569 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:9516701 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | JACS Au |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/9516701 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5037666565 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-7457-7959 |
| authorships[0].author.display_name | Sheng Gong |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| authorships[0].institutions[0].id | https://openalex.org/I63966007 |
| authorships[0].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Sheng Gong |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| authorships[1].author.id | https://openalex.org/A5100400173 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-7907-9676 |
| authorships[1].author.display_name | Shuo Wang |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I66946132 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United States |
| authorships[1].institutions[0].id | https://openalex.org/I66946132 |
| authorships[1].institutions[0].ror | https://ror.org/047s2c258 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I66946132 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | University of Maryland, College Park |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Shuo Wang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United States |
| authorships[2].author.id | https://openalex.org/A5112667444 |
| authorships[2].author.orcid | https://orcid.org/0009-0002-2795-021X |
| authorships[2].author.display_name | Tian Xie |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[2].affiliations[0].raw_affiliation_string | Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| authorships[2].institutions[0].id | https://openalex.org/I63966007 |
| authorships[2].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Tian Xie |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| authorships[3].author.id | https://openalex.org/A5034044899 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-8385-9491 |
| authorships[3].author.display_name | Woo Hyun Chae |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| authorships[3].institutions[0].id | https://openalex.org/I63966007 |
| authorships[3].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Woo Hyun Chae |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| authorships[4].author.id | https://openalex.org/A5026504592 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6040-6940 |
| authorships[4].author.display_name | Runze Liu |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| authorships[4].institutions[0].id | https://openalex.org/I63966007 |
| authorships[4].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Runze Liu |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| authorships[5].author.id | https://openalex.org/A5100601237 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-8714-2121 |
| authorships[5].author.display_name | Yang Shao‐Horn |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| authorships[5].institutions[0].id | https://openalex.org/I63966007 |
| authorships[5].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Yang Shao-Horn |
| authorships[5].is_corresponding | True |
| authorships[5].raw_affiliation_strings | Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| authorships[6].author.id | https://openalex.org/A5083309623 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Jeffrey C. Grossman |
| authorships[6].countries | US |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| authorships[6].institutions[0].id | https://openalex.org/I63966007 |
| authorships[6].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[6].institutions[0].country_code | US |
| authorships[6].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Jeffrey C. Grossman |
| authorships[6].is_corresponding | True |
| authorships[6].raw_affiliation_strings | Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1021/jacsau.2c00235 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11948 |
| primary_topic.field.id | https://openalex.org/fields/25 |
| primary_topic.field.display_name | Materials Science |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2505 |
| primary_topic.subfield.display_name | Materials Chemistry |
| primary_topic.display_name | Machine Learning in Materials Science |
| related_works | https://openalex.org/W2521638480, https://openalex.org/W2086822573, https://openalex.org/W2386699723, https://openalex.org/W2024628231, https://openalex.org/W2520546147, https://openalex.org/W2024548941, https://openalex.org/W2091081872, https://openalex.org/W4387375957, https://openalex.org/W2026408255, https://openalex.org/W2378669746 |
| cited_by_count | 36 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 10 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 14 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 10 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1021/jacsau.2c00235 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210207119 |
| best_oa_location.source.issn | 2691-3704 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2691-3704 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | JACS Au |
| best_oa_location.source.host_organization | https://openalex.org/P4310320006 |
| best_oa_location.source.host_organization_name | American Chemical Society |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320006 |
| best_oa_location.source.host_organization_lineage_names | American Chemical Society |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | JACS Au |
| best_oa_location.landing_page_url | https://doi.org/10.1021/jacsau.2c00235 |
| primary_location.id | doi:10.1021/jacsau.2c00235 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210207119 |
| primary_location.source.issn | 2691-3704 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2691-3704 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | JACS Au |
| primary_location.source.host_organization | https://openalex.org/P4310320006 |
| primary_location.source.host_organization_name | American Chemical Society |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320006 |
| primary_location.source.host_organization_lineage_names | American Chemical Society |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | JACS Au |
| primary_location.landing_page_url | https://doi.org/10.1021/jacsau.2c00235 |
| publication_date | 2022-09-09 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2230728100, https://openalex.org/W1992985800, https://openalex.org/W2278970271, https://openalex.org/W2117363206, https://openalex.org/W2797406032, https://openalex.org/W3119113486, https://openalex.org/W2559612475, https://openalex.org/W3034692868, https://openalex.org/W2952587573, https://openalex.org/W2593252896, https://openalex.org/W2097365790, https://openalex.org/W2258843399, https://openalex.org/W2966683548, https://openalex.org/W3002779457, https://openalex.org/W1981368803, https://openalex.org/W2765453854, https://openalex.org/W3041359432, https://openalex.org/W2801403847, https://openalex.org/W2551323975, https://openalex.org/W2976102057, https://openalex.org/W2901005646, https://openalex.org/W2949015218, https://openalex.org/W2778051509, https://openalex.org/W2831812383, https://openalex.org/W2978707075, https://openalex.org/W2509907061, https://openalex.org/W2990015413, https://openalex.org/W2775811982, https://openalex.org/W2921533983, https://openalex.org/W2464725281, https://openalex.org/W2766856748, https://openalex.org/W2963207368, https://openalex.org/W2913702098, https://openalex.org/W2913594191, https://openalex.org/W2313966941, https://openalex.org/W2902452488, https://openalex.org/W2016168218, https://openalex.org/W2963657244, https://openalex.org/W3082215798, https://openalex.org/W2069313223, https://openalex.org/W2413434998, https://openalex.org/W2792216393, https://openalex.org/W4226120291, https://openalex.org/W3083599250, https://openalex.org/W2162653998, https://openalex.org/W3163995736, https://openalex.org/W2900502776, https://openalex.org/W2800722845, https://openalex.org/W2954088480, https://openalex.org/W3155038719, https://openalex.org/W3176473176, https://openalex.org/W3118334075, https://openalex.org/W2563751252, https://openalex.org/W2978032524, https://openalex.org/W1971044734, https://openalex.org/W2804431384, https://openalex.org/W2793091350, https://openalex.org/W2110644663, https://openalex.org/W3023937119, https://openalex.org/W2950890616, https://openalex.org/W2565052313, https://openalex.org/W2329908465, https://openalex.org/W2891639453, https://openalex.org/W2734437663, https://openalex.org/W1982210646, https://openalex.org/W2065583574, https://openalex.org/W3039218083, https://openalex.org/W2904527725, https://openalex.org/W1977411113, https://openalex.org/W2144625753, https://openalex.org/W2087645345, https://openalex.org/W2057677237, https://openalex.org/W2921415619, https://openalex.org/W1972401082, https://openalex.org/W2121640891, https://openalex.org/W1060035774, https://openalex.org/W2028328856, https://openalex.org/W2028239764, https://openalex.org/W3047639115, https://openalex.org/W2061455647, https://openalex.org/W1450903355, https://openalex.org/W2036234214, https://openalex.org/W2981672083, https://openalex.org/W2994650566, https://openalex.org/W2768278023, https://openalex.org/W2073019757, https://openalex.org/W3134405767, https://openalex.org/W2067050734, https://openalex.org/W2268791407, https://openalex.org/W2792575172, https://openalex.org/W2999615587, https://openalex.org/W3112780182, https://openalex.org/W2107024505, https://openalex.org/W2315306802, https://openalex.org/W4214870278, https://openalex.org/W4319162181, https://openalex.org/W2015197254, https://openalex.org/W3101477835, https://openalex.org/W3100220443, https://openalex.org/W3121984752, https://openalex.org/W3099236691, https://openalex.org/W3106400375, https://openalex.org/W3100710928, https://openalex.org/W3103227378, https://openalex.org/W3099803468, https://openalex.org/W3113009196, https://openalex.org/W3104644561 |
| referenced_works_count | 107 |
| abstract_inverted_index.a | 29, 107 |
| abstract_inverted_index.In | 24 |
| abstract_inverted_index.We | 78 |
| abstract_inverted_index.as | 106 |
| abstract_inverted_index.by | 10, 117 |
| abstract_inverted_index.in | 89 |
| abstract_inverted_index.is | 103 |
| abstract_inverted_index.it | 65 |
| abstract_inverted_index.of | 2, 21, 40 |
| abstract_inverted_index.to | 5, 17, 34, 83, 110 |
| abstract_inverted_index.we | 27 |
| abstract_inverted_index.DFT | 86, 113 |
| abstract_inverted_index.The | 0, 100 |
| abstract_inverted_index.and | 55, 62, 64, 75, 94 |
| abstract_inverted_index.are | 12 |
| abstract_inverted_index.due | 16 |
| abstract_inverted_index.how | 112 |
| abstract_inverted_index.the | 18, 36, 47, 67, 81, 85, 90, 119 |
| abstract_inverted_index.use | 28, 80 |
| abstract_inverted_index.yet | 14 |
| abstract_inverted_index.also | 104 |
| abstract_inverted_index.deep | 70 |
| abstract_inverted_index.find | 111 |
| abstract_inverted_index.from | 115 |
| abstract_inverted_index.than | 46 |
| abstract_inverted_index.then | 79 |
| abstract_inverted_index.this | 25 |
| abstract_inverted_index.used | 105 |
| abstract_inverted_index.with | 42, 51, 97 |
| abstract_inverted_index.(PBE) | 49 |
| abstract_inverted_index.(SCAN | 61 |
| abstract_inverted_index.data. | 23 |
| abstract_inverted_index.hotly | 68 |
| abstract_inverted_index.learn | 35 |
| abstract_inverted_index.model | 33, 82, 102, 120 |
| abstract_inverted_index.work, | 26 |
| abstract_inverted_index.amount | 20 |
| abstract_inverted_index.forest | 32 |
| abstract_inverted_index.higher | 45 |
| abstract_inverted_index.linear | 52 |
| abstract_inverted_index.neural | 71 |
| abstract_inverted_index.random | 31 |
| abstract_inverted_index.PBEsol, | 54 |
| abstract_inverted_index.Project | 92 |
| abstract_inverted_index.limited | 19 |
| abstract_inverted_index.machine | 3 |
| abstract_inverted_index.output. | 121 |
| abstract_inverted_index.predict | 6 |
| abstract_inverted_index.studied | 69 |
| abstract_inverted_index.accuracy | 44 |
| abstract_inverted_index.approach | 109 |
| abstract_inverted_index.database | 93 |
| abstract_inverted_index.deviates | 114 |
| abstract_inverted_index.discover | 95 |
| abstract_inverted_index.enthalpy | 39, 88 |
| abstract_inverted_index.gradient | 57 |
| abstract_inverted_index.learning | 4, 74 |
| abstract_inverted_index.measured | 9 |
| abstract_inverted_index.transfer | 76 |
| abstract_inverted_index.valuable | 13 |
| abstract_inverted_index.Materials | 91 |
| abstract_inverted_index.calibrate | 84 |
| abstract_inverted_index.difficult | 15 |
| abstract_inverted_index.formation | 38, 87 |
| abstract_inverted_index.learning. | 77 |
| abstract_inverted_index.materials | 7, 41, 96 |
| abstract_inverted_index.(meta-GGA) | 59 |
| abstract_inverted_index.explaining | 118 |
| abstract_inverted_index.functional | 50 |
| abstract_inverted_index.prediction | 43 |
| abstract_inverted_index.properties | 8 |
| abstract_inverted_index.stability. | 99 |
| abstract_inverted_index.application | 1 |
| abstract_inverted_index.correction, | 53 |
| abstract_inverted_index.data-mining | 108 |
| abstract_inverted_index.experiments | 11, 116 |
| abstract_inverted_index.functionals | 60 |
| abstract_inverted_index.outperforms | 66 |
| abstract_inverted_index.experimental | 22, 37 |
| abstract_inverted_index.approximation | 58 |
| abstract_inverted_index.multifidelity | 30, 101 |
| abstract_inverted_index.network-based | 72 |
| abstract_inverted_index.representation | 73 |
| abstract_inverted_index.underestimated | 98 |
| abstract_inverted_index.meta-generalized | 56 |
| abstract_inverted_index.r<sup>2</sup>SCAN), | 63 |
| abstract_inverted_index.Perdew-Burke-Ernzerhof | 48 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5083309623, https://openalex.org/A5100601237 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I63966007 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.91884301 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |