Molecular machine learning (ML) has gained considerable attention in recent years. Developing ML algorithms for chemical reaction prediction is a formidable task, due to the small-sized reaction data it often presents, besides the sparsity and skewed distribution. While previous ML studies offered effective

We introduce MindlessGen, a Python-based generator for creating chemically diverse, “mindless” molecules through random atomic placement and subsequent geometry optimization. Using this framework, we constructed the MB2061 benchmark set, containing 2061 molecules with high-level PNO-LCCSD(T)-F12 reference data for dissociation reactions. This set provides a challenging benchmark for testing, validation, and training of density functional approximations (DFAs), semiempirical methods, force fields, and machine learning potentials using molecular structures beyond the conventional chemical space. For DFAs, we initially hypothesized that highly parameterized functionals might perform poorly on this set. However, no consistent relationship between fitting strategy and accuracy was observed. A clear Jacob’s ladder trend emerges, with ωB97X-2 achieving the lowest mean absolute error (MAE) of 8.4 kcal·mol−1 and r²SCAN-3c offering a robust cost-efficient alternative (19.6 kcal·mol−1). Furthermore, we discuss the performance of selected semiempirical methods and contemporary machine learned interatomic potentials.

I just got back from the big centenary meeting for quantum mechanics on Helgoland. But this year is also a bicentenary: of Michael Faraday's discovery of benzene. Here I consider what it meant, and why we might celebrate it. https://www.chemistryworld.com/opinion/benzenes-200-year-legacy-of-transformation/4021524.article

Thermally activated delayed fluorescence (TADF) is a hot research topic in view of its impressive applications in a wide variety of fields from organic LEDs to photodynamic therapy and metal-free photocatalysis. TADF is a rare and fragile phenomenon that requires a delicate equilibrium between tiny singlet–t

As the number of Internet of Things devices is rapidly increasing, there is an urgent need for sustainable and efficient energy sources and management practices in ambient environments. In response, we developed a high-efficiency ambient photovoltaic based on sustainable non-toxic materials and present a ful

Visualization of weak interactions is now a very popular kind of analysis methods, which allows chemists to recognize existence, strength, and type of interactions in different regions of a chemical system easily and intuitively. Among these analysis methods, the independent gradient model (IGM) and its variant with better graphical effect, namely IGM based on Hirshfeld partition of molecular density (IGMH), are particularly useful. They can clearly reveal various interactions between specific fragments, and become widely popular in recent years. This chapter first briefly reviews the definition and characteristics of the two methods, and then introduces a new variant of IGM, called modified IGM (mIGM), which, like IGM, only requires atomic coordinate information to perform the analysis and thus the computational cost is fairly low, while its graphical quality in exhibiting weak interactions is almost as good as the markedly more expensive IGMH. This chapter then introduces the averaged mIGM (amIGM) method, which extends mIGM to visual analysis of the weak interactions involved in molecular dynamics trajectories. A series of examples fully demonstrate that amIGM can well display all kinds of weak interactions in dynamic environments, and its image quality and flexibility are all superior to the previously proposed averaged noncovalent interaction (aNCI) method with similar uses.