Optimization of the Bulk Heterojunction of All-Small-Molecule Organic Photovoltaics Using Design of Experiment and Machine Learning Approaches

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Rural Architectural Planning and Landscape Optimization Design under the Background of Ecological Environment Protection

The ecological problems faced by China’s environmental protection are becoming more and more serious. Serious haze occurs frequently in some areas. Water pollution, soil pollution, and other new types of pollution are still relatively prominent problems. Therefore, rural architectural planning and landscape optimization design should be based on the premise of ecological environmental protection. This paper puts forward the evaluation of rural architectural planning and landscape in the context of ecological environment protection and uses the analytic hierarchy process to analyze and obtain the evaluation results. This method has a comprehensive and scientific powerful evaluation function. The experimental results of this paper show that after the evaluation of the analytic hierarchy process, it is found that the comprehensive score of the architectural planning and landscape of village A is not very high. The highest weight is 0.3210, the landscape diversity score of street A is 1.28, and the landscape diversity score of street D is 1.76. This is the highest score, indicating that the architectural planning and landscape of the village cannot meet the needs of contemporary ecological environmental protection. Aiming at the problems existing in the landscape, the corresponding measures are also given at the end of the experiment, which has certain significance for the landscape optimization design.

Machine Learning-Assisted Polymer Design for Improving the Performance of Non-Fullerene Organic Solar Cells

Despite the progress in machine learning (ML) in terms of prediction of power conversion efficiency (PCE) in organic photovoltaics (OPV), the effectiveness of ML in practical applications is still lacking owing to the complex structure-property relationship. Therefore, verifying the potential of ML through experiments can amplify the use of ML models. Herein, we developed a new series of π-conjugated polymers comprising benzodithiophene and thiazolothiazole with fluorination and alkylthio chains (PBDTTzBO, PFSBDTTzBO, and PFBDTTzBO) for non-fullerene (NF) acceptors based on our random-forest ML model for OPVs. Notably, the order of the ML-predicted PCEs of these polymers with IT-4F (9.93, 11.35, and 11.47%) was in good agreement with their experimental PCEs (5.24, 7.35, and 10.30%). In contrast, an inverse correlation was observed between the predicted (9.20, 12.29, and 12.20%) and experimental (11.98, 1.57, and 6.53%) PCEs with Y6. Both the findings are interpreted in terms of surface morphology, transient photoconductivity, charge carrier mobility, polymer orientation, and miscibility, quantified by the Flory-Huggins parameters. Herein, we present an ML-assisted polymer design for high-performance non-fullerene organic photovoltaics (NFOPVs) and elucidate the importance of the subtle alterations in the morphology of NFOPVs.

A self-driving laboratory advances the Pareto front for material properties

Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between these conflicting objectives. Here we use a self-driving laboratory, Ada, to define the Pareto front of conductivities and processing temperatures for palladium films formed by combustion synthesis. Ada discovers new synthesis conditions that yield metallic films at lower processing temperatures (below 200 °C) relative to the prior art for this technique (250 °C). This temperature difference makes possible the coating of different commodity plastic materials (e.g., Nafion, polyethersulfone). These combustion synthesis conditions enable us to to spray coat uniform palladium films with moderate conductivity (1.1 × 105 S m-1) at 191 °C. Spray coating at 226 °C yields films with conductivities (2.0 × 106 S m-1) comparable to those of sputtered films (2.0 to 5.8 × 106 S m-1). This work shows how a self-driving laboratoy can discover materials that provide optimal trade-offs between conflicting objectives.

Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks

Nonfullerene, a small molecular electron acceptor, has substantially improved the power conversion efficiency of organic photovoltaics (OPVs). However, the large structural freedom of π-conjugated polymers and molecules makes it difficult to explore with limited resources. Machine learning, which is based on rapidly growing artificial intelligence technology, is a high-throughput method to accelerate the speed of material design and process optimization; however, it suffers from limitations in terms of prediction accuracy, interpretability, data collection, and available data (particularly, experimental data). This recognition motivates the present Perspective, which focuses on utilizing the experimental data set for ML to efficiently aid OPV research. This Perspective discusses the trends in ML-OPV publications, the NFA category, and the effects of data size and explanatory variables (fingerprints or Mordred descriptors) on the prediction accuracy and explainability, which broadens the scope of ML and would be useful for the development of next-generation solar cell materials.

Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials

Designing efficient organic photovoltaic (OPV) materials purposefully is still challenging and time-consuming. It is of paramount importance in material development to identify basic functional units that play the key roles in material performance and subsequently establish the substructure-property relationship. Herein, we describe an automatic design framework based on an in-house designed La FREMD Fingerprint and machine learning (ML) algorithms for highly efficient OPV donor molecules. The key building blocks are identified, and a library consisting of 18 960 new molecules is generated within this framework. Through investigating the chemical structures of materials with different performance, a guidance on designing efficient OPV materials is proposed. Furthermore, the most promising candidates exhibit a predicted power conversion efficiency (PCE) value of over 15% when combined with acceptor Y6. Density functional theory (DFT) studies show these candidate materials possess exceptional potential for efficient charge carrier transport. The proposed framework demonstrates the ability to design new materials based on the substructure-property relationship built by ML, which provides an alternative methodology for applying ML in new material discovery.

Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of Block Copolymers Using Machine Learning Approaches

Self-assembly of block copolymers (BCPs) is an alternative patterning technique that promises high resolution and density multiplication with lower costs. The defectivity of the resulting nanopatterns remains too high for many applications in microelectronics and is exacerbated by small variations of processing parameters, such as film thickness, and fluctuations of solvent vapor pressure and temperature, among others. In this work, a solvent vapor annealing (SVA) flow-controlled system is combined with design of experiments (DOE) and machine learning (ML) approaches. The SVA flow-controlled system enables precise optimization of the conditions of self-assembly of the high Flory-Huggins interaction parameter (χ) hexagonal dot-array forming BCP, poly(styrene-b-dimethylsiloxane) (PS-b-PDMS). The defects within the resulting patterns at various length scales are then characterized and quantified. The results show that the defectivity of the resulting nanopatterned surfaces is highly dependent upon very small variations of the initial film thicknesses of the BCP, as well as the degree of swelling under the SVA conditions. These parameters also significantly contribute to the quality of the resulting pattern with respect to grain coarsening, as well as the formation of different macroscale phases (single and double layers and wetting layers). The results of qualitative and quantitative defect analyses are then compiled into a single figure of merit (FOM) and are mapped across the experimental parameter space using ML approaches, which enable the identification of the narrow region of optimum conditions for SVA for a given BCP. The result of these analyses is a faster and less resource intensive route toward the production of low-defectivity BCP dot arrays via rational determination of the ideal combination of processing factors. The DOE and machine learning-enabled approach is generalizable to the scale-up of self-assembly-based nanopatterning for applications in electronic microfabrication.