Enhancing coarse-grained models through machine learning. [Editorial] (2024)
- Authors:
- Autor USP: SILVA, THEREZA AMÉLIA SOARES DA - FFCLRP
- Unidade: FFCLRP
- DOI: 10.1021/acs.jcim.4c00537
- Subjects: APRENDIZADO COMPUTACIONAL; SIMULAÇÃO; MODELAGEM MOLECULAR; NANOPARTÍCULAS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Washington
- Date published: 2024
- Source:
- Título: Journal of Chemical Information and Modeling
- ISSN: 1549-960X
- Volume/Número/Paginação/Ano: v. 64, n. 8, p. 2931-2932, 2024
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: hybrid
- Licença: other-oa
-
ABNT
KARMAKAR, Tarak e SOARES, Thereza Amélia e MERZ JR, Kenneth M. Enhancing coarse-grained models through machine learning. [Editorial]. Journal of Chemical Information and Modeling. Washington: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1021/acs.jcim.4c00537. Acesso em: 26 dez. 2025. , 2024 -
APA
Karmakar, T., Soares, T. A., & Merz Jr, K. M. (2024). Enhancing coarse-grained models through machine learning. [Editorial]. Journal of Chemical Information and Modeling. Washington: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1021/acs.jcim.4c00537 -
NLM
Karmakar T, Soares TA, Merz Jr KM. Enhancing coarse-grained models through machine learning. [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64( 8): 2931-2932.[citado 2025 dez. 26 ] Available from: https://doi.org/10.1021/acs.jcim.4c00537 -
Vancouver
Karmakar T, Soares TA, Merz Jr KM. Enhancing coarse-grained models through machine learning. [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64( 8): 2931-2932.[citado 2025 dez. 26 ] Available from: https://doi.org/10.1021/acs.jcim.4c00537 - Exploring the molecular dynamics of a lipid-A vesicle at the atom level: morphology and permeation mechanism
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Informações sobre o DOI: 10.1021/acs.jcim.4c00537 (Fonte: oaDOI API)
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