Title: Machine Learning and Self Consistent Field Theory to Accelerate the Discovery of Soft Materials.

Hector D. Ceniceros, Professor

Department of Mathematics

University of California

Santa Barbara, USA

Abstract: Numerical simulations using self consistency field theory (SCFT) have been a powerful tool to study soft materials like polymers. However, SCFT simulations are a complex and computationally costly process and exploring the vast design space of polymers via SCFT is impractical. We will discuss in this talk our recent efforts to leverage SCFT with Deep Learning (we design specific architectures of convolutional neural networks and generative adversarial networks) to accelerate the exploration of parameter space and to effectively predict polymer structures.

17:00 BR
3:00 PM COL
1:00 PM USA

Organizer Commitee

Catalina M. Rúa - catalina.rua@udenar.edu.co
Departamento de Matemáticas y Estadística
Universidad de Nariño, Colombia

Priscila Cardoso Calegari - priscila.calegari@ufsc.br
Departamento de Computação
Universidade Federal de Santa Cantarina Araranguá, Brasil

Wellington Carlos de Jesus - wellingtonjesus@id.uff.br
Departamento de Matemática de Volta Redonda
Universidade Federal Fluminense, Brasil

Organizers