Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors

Visible light to the second near-infrared light-harvesting donor-acceptor1-donor-acceptor2-type terpolymers for boosted photocatalytic hydrogen evolution via dual-sulfone-acceptor engineering

Developing visible to near-infrared light-absorbing conjugated polymer photocatalysts is crucial for enhancing solar energy utilization efficiency, as most conjugated organic polymers only absorb light in the visible range. In this work, we firstly developed a novel thiophene S,S-dioxide (TDO) monomer with the stronger electron-withdrawing character, and then prepared a series of donor-acceptor1-donor-acceptor2-type (D-A1-D-A2-type) conjugated terpolymers (THTDB-1-THTDB-5) by statistically adjusting the molar ratio of two sulfone-based acceptor monomers, dibenzothiophene-S,S-dioxide (BTDO, A1) and TDO (A2). These terpolymers demonstrate a gradually expanding absorption range from visible light to the second near-infrared (Vis-to-NIR-II) region with the gradual increase of the TDO contents in the polymer skeleton, showcasing excellent absorption properties and efficient light-capturing capabilities. The optimized D-A1-D-A2 polymer photocatalyst THTDB-4 exhibits a high hydrogen evolution rate of 21.27 mmol g-1 h-1 under visible light without any co-catalyst. The dual-sulfone-acceptor engineering offers a viable approach for developing efficient the longer Vis-to-NIR-II light-harvesting polymer photocatalysts.

Machine learning integrated photocatalysis: progress and challenges

Discovering efficient photocatalysts has long been the goal of photocatalysis, which has traditionally been driven by serendipitous or try-and-error strategies. Recent developments in photocatalysis integrated with machine learning techniques promise to accelerate the discovery of photocatalysts, but are also facing significant challenges. In this review, advances in machine learning integrated photocatalysis are first presented from the perspective of three main photocatalytic processes: light harvesting, charge generation and separation, and surface redox reactions. Next, progress in using machine learning to understand complex photoactivity-structure relationships and identify the factors governing activity follows. A future photocatalysis paradigm is then provided with the integration of artificial intelligence, robots and automation. Lastly, we discuss the current challenges in machine learning integrated photocatalysis. This review aims to provide a systematic overview and guidelines to the broad scientific community interested in photocatalysis and artificial intelligence for solar fuel synthesis.

Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects

Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.