Banca de QUALIFICAÇÃO: GABRIEL RAVANHANI SCHLEDER
Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.DISCENTE : GABRIEL RAVANHANI SCHLEDER
DATA : 03/12/2019
HORA: 10:00
LOCAL: sala 304, 3º andar, Bloco B, Campus SA da Fundação Universidade Federal do ABC, localizada na Avenida dos Estados, 5001, Santa Terezinha, Santo André, SP
TÍTULO:
Machine learning for Materials Science: 2D Materials Discovery and Design
PÁGINAS: 128
GRANDE ÁREA: Ciências Exatas e da Terra
ÁREA: Física
SUBÁREA: Física da Matéria Condensada
RESUMO:
Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data.
Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated.
We show how these approaches to modern computational materials science can be used to investigate different two-dimensional (2D) materials properties, enabling the discovery and design of novel materials for diverse applications.
Specifically, the increasing interest and research on 2D materials has not yet translated into a reality of diverse materials applications. To go beyond graphene and transition metal dichalcogenides for several applications, suitable candidates with desirable properties must be proposed. We use machine learning techniques to identify thermodynamically stable 2D materials, which is the first essential requirement for any application. According to the formation energy and energy above the convex hull, we classify materials as having low, medium, or high stability. The proposed approach enables the stability evaluation of novel 2D compounds for further detailed investigation of promising candidates, using only composition properties and structural symmetry, without the need for information about atomic positions. We demonstrate the usefulness of the model generating more than a thousand novel compounds, corroborating with DFT calculations the classification for five of these materials. To illustrate the applicability of the stable materials, we then perform a screening of electronic materials suitable for photoelectrocatalytic water splitting, identifying the potential candidate Sn2SeTe generated by our model, and also PbTe, both not yet reported for this application.
Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field.
MEMBROS DA BANCA:
Membro Titular - Examinador(a) Interno ao Programa - 2605463 - JOSE ANTONIO SOUZA
Membro Titular - Examinador(a) Interno ao Programa - 2218228 - LUANA SUCUPIRA PEDROZA
Membro Titular - Examinador(a) Externo ao Programa - 1309493 - PEDRO ALVES DA SILVA AUTRETO
Presidente - Externo ao Programa - 730.580.685-49 - CAETANO RODRIGUES MIRANDA - USP
Membro Suplente - Examinador(a) Interno ao Programa - 1760527 - FLAVIO LEANDRO DE SOUZA
Membro Suplente - Examinador(a) Externo ao Programa - 1544403 - MAURICIO DOMINGUES COUTINHO NETO
Membro Suplente - Examinador(a) Externo à Instituição - ALEXANDRE REILY ROCHA - UNESP
Membro Suplente - Examinador(a) Externo à Instituição - EDSON ROBERTO LEITE - CNPEM