Gaming self-consistent field theory: Generative block polymer phase discovery

Abstract

Block polymers are an attractive platform for uncovering the factors that give rise to self-assembly in soft matter owing to their relatively simple thermodynamic description, as captured in self-consistent field theory (SCFT). SCFT historically has found great success explaining experimental data, allowing one to construct phase diagrams from a set of candidate phases, and there is now strong interest in deploying SCFT as a screening tool to guide experimental design. However, using SCFT for phase discovery leads to a conundrum: How does one discover a new morphology if the set of candidate phases needs to be specified in advance? This long-standing challenge was surmounted by training a deep convolutional generative adversarial network (GAN) with trajectories from converged SCFT solutions, and then deploying the GAN to generate input fields for subsequent SCFT calculations. The power of this approach is demonstrated for network phase formation in neat diblock copolymer melts via SCFT. A training set of only five networks produced 349 candidate phases spanning known and previously unexplored morphologies, including a chiral network. This computational pipeline, constructed here entirely from open-source codes, should find widespread application in block polymer phase discovery and other forms of soft matter.

Robust Multifunctional Ultrathin 2 Nanometer Organic Nanofibers

“…The past several decades have witnessed great advances in molecular self-assembly of amphiphiles in solution into nanostructures, including micelles, vesicles, fibers, tubes, and sheets. − The ability to precisely control the compositions, dimensions, and architectures of these nanostructures renders their applications in catalysis, electronics, sensors, and the biomedical field. ,− Among various artificially synthesized amphiphiles, sequence-controlled poly- N -substituted glycines (i.e., peptoids) stand out as attractive building blocks for hierarchical self-assembly into various biomimetic nanomaterials due to facile synthesis, rich side-chain diversity, high peripheral stability, and outstanding biocompatibility of peptoids. − In spite of nanofibers yielded from self-assembly of conventional block copolymers and peptoids with fine diameters dictated by the length of entire polymer chains, stable yet dynamic UTONFs of exceedingly small diameters (i.e., ≤ 2 nm) have yet to be explored.…”

Desperately seeking soft structures

“…Computational methods face an even more daunting challenge: How should the molecules be arranged at the beginning of a computer simulation so that diverse sets of competitive equilibrium or near-equilibrium structures are obtained? This is the problem that is taken up in a recent PNAS article by Chen and Dorfman ( 2 ).…”

“…Small differences in the initial configuration used in SCFT relaxation can lead to dramatically different outcomes (arrows 2 and 3). Double gyroid and O 70 are known block copolymer mesophases; H 181 is a new chiral mesophase discovered by Chen and Dorfman ( 2 ).…”

“…The phase discovery problem addressed by Chen and Dorfman ( 2 ) is less ambitious than finding the globally stable structure for a specified model of a block polymer system. Instead, they devise a procedure to automatically generate diverse libraries of candidate mesophases, each with its own associated SCFT solution.…”

Automated Search Strategy for Novel Ordered Structures of Block Copolymers

“…Searching such a high-dimensional space presents a challenging task. In recent years, artificial intelligence (AI) has been applied to the study of BCP self-assembly, − including searching for novel ordered structures. ,,, Tsai et al combined genetic algorithms (GA) and SCFT to search for globally stable structures in such a high-dimensional space . This GA-SCFT method successfully recovered some common structures, but encountered difficulties in searching for complex phases (e.g., double-gyroid).…”