molecular design

Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts. Certain organic molecular thin films can be transitioned from initially prepared amorphous layers to large-scale crystalline films via abrupt thermal annealing. Ideally, these films crystallize as platelets with long-range-ordered domains on the scale of tens to hundreds of microns. However, other organic molecular thin films may instead crystallize as spherulites or resist crystallization entirely. Organic molecules that have the capability of transforming into a platelet morphology feature both high melting point (Tm) and crystallization driving force (ΔGc). In this work, we employed machine learning (ML) to identify candidate organic materials with the potential to crystallize into platelets by estimating the aforementioned thermal properties. Six organic molecules identified by the ML algorithm were experimentally evaluated; three crystallized as platelets, one crystallized as a spherulite, and two resisted thin film crystallization. These results demonstrate a successful application of ML in the scope of predicting thermal properties of organic molecules and reinforce the principles of Tm and ΔGc as metrics that aid in predicting the crystallization behavior of organic thin films.

Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.

The behavior of wave signals in the far zone is not only of theoretical interest but also of paramount practical importance in communications and other fields of applications of optical, electromagnetic, or acoustic waves. A long time ago, T. T. Wu [J. Appl. Phys.57, 2370 (1985)JAPIAU0021-897910.1063/1.335465] introduced models of "electromagnetic missiles" whose decay could be made arbitrarily slower than the usual inverse distance by an appropriate choice of the high-frequency portion of the source spectrum. Very recent work by Plachenov and Kiselev [Diff. Eqs.60, 1634 (2024)10.1134/S001226612460250X] introduced a finite-energy scalar wave solution, different from Wu's, decaying slower than inversely proportional with the distance. A physical explanation for the unusual asymptotic behavior of the latter will be given in this paper. Furthermore, two additional examples of scalar wave pulses characterized by abnormal slow decay in the far zone will be given, and their asymptotic behavior will be discussed. A proof of feasibility of acoustic and electromagnetic fields with the abnormal asymptotics will be described.