Revisão das Tecnologias de Inteligência Artificial e Machine/Deep Learning: Restrições, Oportunidades, Estado da Arte e Desafios

  • Hugo G. Machado Universidade Federal de Goiás (UFG)
  • Kleber Mundim
Palavras-chave: aprendizagem de máquina, química, redes neurais artificiais


A utilização de algoritmos de aprendizagem de máquina tem aumentadoexponencialmente na pesquisa científica, especialmente devido a avanços recentes emtécnicas de aprendizado profundo. Aqui, serão discutidas aplicações desses algoritmos naquímica e em outras áreas da ciência, com foco em redes neurais artificiais. Essas redestêm a capacidade de automatizar todas as etapas do processo de aprendizado demáquina,incluindo a classificação e a predição de propriedades químicas. Será fornecida uma visãohistórica do desenvolvimento desses algoritmos, desde a década de 1940 até os dias atuais,com destaque para aplicações em áreas como desenvolvimento de medicamentos, ciência de materiais e técnicas de análise autônomas. Aspectos importantes desses algoritmos serãodiscutidos em detalhes. Além disso, será abordado o processo de vetorização molecular, essencial para o tratamento de dados químicos, e alguns caracterizadores moleculares serão discutidos em particular. Em conclusão, será fornecida uma visão abrangente das aplicaçõesdos algoritmos de aprendizado de máquina na química, juntamente com suas limitações e desafios associados à sua implementação, destacando seu potencial transformador quando
utilizado de maneira responsável e ética.


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Como Citar
Machado, H. G., & Mundim, K. (2023). Revisão das Tecnologias de Inteligência Artificial e Machine/Deep Learning: Restrições, Oportunidades, Estado da Arte e Desafios. Revista Processos Químicos, 16(32), 9-22.