Using Machine Learning to Predict and Understand Complex Self-Assembly Behaviors of a Multicomponent Nanocomposite

Understanding multicomponent low molecular weight gels from gelators to networks

BACKGROUND

The construction of gels from low molecular weight gelators (LMWG) has been extensively studied in the fields of bio-nanotechnology and other fields. However, the understanding gaps still prevent the prediction of LMWG from the full design of those gel systems. Gels with multicomponent become even more complicated because of the multiple interference effects coexist in the composite gel systems.

AIM OF REVIEW

This review emphasizes systems view on the understanding of multicomponent low molecular weight gels (MLMWGs), and summarizes recent progress on the construction of desired networks of MLMWGs, including self-sorting and co-assembly, as well as the challenges and approaches to understanding MLMWGs, with the hope that the opportunities from natural products and peptides can speed up the understanding process and close the gaps between the design and prediction of structures.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review is focused on three key concepts. Firstly, understanding the complicated multicomponent gels systems requires a systems perspective on MLMWGs. Secondly, several protocols can be applied to control self-sorting and co-assembly behaviors in those multicomponent gels system, including the certain complementary structures, chirality inducing and dynamic control. Thirdly, the discussion is anchored in challenges and strategies of understanding MLMWGs, and some examples are provided for the understanding of multicomponent gels constructed from small natural products and subtle designed short peptides.

Functional composites by programming entropy-driven nanosheet growth

Nanomaterials must be systematically designed to be technologically viable1-5. Driven by optimizing intermolecular interactions, current designs are too rigid to plug in new chemical functionalities and cannot mitigate condition differences during integration6,7. Despite extensive optimization of building blocks and treatments, accessing nanostructures with the required feature sizes and chemistries is difficult. Programming their growth across the nano-to-macro hierarchy also remains challenging, if not impossible8-13. To address these limitations, we should shift to entropy-driven assemblies to gain design flexibility, as seen in high-entropy alloys, and program nanomaterial growth to kinetically match target feature sizes to the mobility of the system during processing14-17. Here, following a micro-then-nano growth sequence in ternary composite blends composed of block-copolymer-based supramolecules, small molecules and nanoparticles, we successfully fabricate high-performance barrier materials composed of more than 200 stacked nanosheets (125 nm sheet thickness) with a defect density less than 0.056 µm-2 and about 98% efficiency in controlling the defect type. Contrary to common perception, polymer-chain entanglements are advantageous to realize long-range order, accelerate the fabrication process (<30 min) and satisfy specific requirements to advance multilayered film technology3,4,18. This study showcases the feasibility, necessity and unlimited opportunities to transform laboratory nanoscience into nanotechnology through systems engineering of self-assembly.

Template Design for Complex Block Copolymer Patterns Using a Machine Learning Method

This study represents the first attempt to address the inverse design problem of the guiding template for directed self-assembly (DSA) patterns using solely machine learning methods. By formulating the problem as a multi-label classification task, the study shows that it is possible to predict templates without requiring any forward simulations. A series of neural network (NN) models, ranging from the basic two-layer convolutional neural network (CNN) to the large NN models (32-layer CNN with 8 residual blocks), have been trained using simulated pattern samples generated by thousands of self-consistent field theory (SCFT) calculations; a number of augmentation techniques, especially suitable for predicting morphologies, have been also proposed to enhance the performance of the NN model. The exact match accuracy of the model in predicting the template of simulated patterns was significantly improved from 59.8% for the baseline model to 97.1% for the best model of this study. The best model also demonstrates an excellent generalization ability in predicting the template for human-designed DSA patterns, while the simplest baseline model is ineffective in this task.

Machine learning facilitating the rational design of nanozymes

As a component substitute for natural enzymes, nanozymes have the advantages of easy synthesis, convenient modification, low cost, and high stability, and are widely used in many fields. However, their application is seriously restricted by the difficulty of rapidly creating high-performance nanozymes. The use of machine learning techniques to guide the rational design of nanozymes holds great promise to overcome this difficulty. In this review, we introduce the recent progress of machine learning in assisting the design of nanozymes. Particular attention is given to the successful strategies of machine learning in predicting the activity, selectivity, catalytic mechanisms, optimal structures and other features of nanozymes. The typical procedures and approaches for conducting machine learning in the study of nanozymes are also highlighted. Moreover, we discuss in detail the difficulties of machine learning methods in dealing with the redundant and chaotic nanozyme data and provide an outlook on the future application of machine learning in the nanozyme field. We hope that this review will serve as a useful handbook for researchers in related fields and promote the utilization of machine learning in nanozyme rational design and related topics.

From Nanoscopic to Macroscopic Materials by Stimuli-Responsive Nanoparticle Aggregation

Stimuli-responsive nanoparticle (NP) aggregation plays an increasingly important role in regulating NP assembly into microscopic superstructures, macroscopic 2D, and 3D functional materials. Diverse external stimuli are widely used to adjust the aggregation of responsive NPs, such as light, temperature, pH, electric, and magnetic fields. Many unique structures based on responsive NPs are constructed including disordered aggregates, ordered superlattices, structural droplets, colloidosomes, and bulk solids. In this review, the strategies for NP aggregation by external stimuli, and their recent progress ranging from nanoscale aggregates, microscale superstructures to macroscale bulk materials along the length scales as well as their applications are summarized. The future opportunities and challenges for designing functional materials through NP aggregation at different length scales are also discussed.