Phase (matter)

Engineering

Construct (python library)

Materials science

Theoretical computer science

Set (abstract data type)

Computer science

Filter (signal processing)

Polymer

Mechanical engineering

Nanotechnology

Organic chemistry

Mathematics

Block diagram

Sampling (signal processing)

Copolymer

Block (permutation group theory)

Phase diagram

Artificial intelligence

Geometry

Composite material

Rational design

Chemistry

Programming language

Blueprint

Active learning (machine learning)

Computer vision

Electrical engineering

ACS Macro Letters

Equilibrium phase diagrams serve as blueprints for rational design of nanostructured materials of block copolymers, but their construction is time-consuming and requires profound expertise. Herein, by virtue of the knowledge of self-consistent field theory (SCFT), the active-learning method is developed to autonomously construct the phase diagrams of block copolymers. Without human intervention, the SCFT-assisted active-learning method can rapidly search the undetected phases and efficiently reproduce the complicated phase diagrams of diblock copolymers and multiblock terpolymers via decreasing the number of sampling points to about 20%. It is clearly demonstrated that the combined uncertainty sampling/random selection scheme in the active-learning method shows the outperformance in spite of a small amount of initial data set. This work highlights the promising integration of theoretical modeling with machine learning and represents a crucial step toward rational design of nanostructured materials.

Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning