Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers.

The rapid progress of machine learning (ML) in predicting molecular properties enables high-precision predictions being routinely achieved. However, many ML models, such as conventional molecular graph, cannot differentiate stereoisomers of certain types, particularly conformational and chiral ones that share the same bonding connectivity but differ in spatial arrangement. Here, we designed a hybrid molecular graph network, Chemical Feature Fusion Network (CFFN), to address the issue by integrating planar and stereo information of molecules in an interweaved fashion. The three-dimensional (3D, i.e., stereo) modality guarantees precision and completeness by providing unabridged information, while the two-dimensional (2D, i.e., planar) modality brings in chemical intuitions as prior knowledge for guidance. The zipper-like arrangement of 2D and 3D information processing promotes cooperativity between them, and their synergy is the key to our model's success. Experiments on various molecules or conformational datasets including a special newly created chiral molecule dataset comprised of various configurations and conformations demonstrate the superior performance of CFFN. The advantage of CFFN is even more significant in datasets made of small samples. Ablation experiments confirm that fusing 2D and 3D molecular graphs as unambiguous molecular descriptors can not only effectively distinguish molecules and their conformations, but also achieve more accurate and robust prediction of quantum chemical properties.

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.

Cavity Manipulation of Attosecond Charge Migration in Conjugated Dendrimers.

Dendrimers are branched polymers with wide applications to photosensitization, photocatalysis, photodynamic therapy, photovoltaic conversion, and light sensor amplification. The primary step of numerous photophysical and photochemical processes in many molecules involves ultrafast coherent electronic dynamics and charge oscillations triggered by photoexcitation. This electronic wavepacket motion at short times where the nuclei are frozen is known as attosecond charge migration. We show how charge migration in a dendrimer can be manipulated by placing it in an optical cavity and monitored by time-resolved X-ray diffraction. Our simulations demonstrate that the dendrimer charge migration modes and the character of photoexcited wave function can be significantly influenced by the strong light-matter interaction in the cavity. This presents a new avenue for modulating initial ultrafast charge dynamics and subsequently controlling coherent energy transfer in dendritic nanostructures.

Spectroscopy-Guided Deep Learning Predicts Solid-Liquid Surface Adsorbate Properties in Unseen Solvents.

Accurately and rapidly acquiring the microscopic properties of a material is crucial for catalysis and electrochemistry. Characterization tools, such as spectroscopy, can be a valuable tool to infer these properties, and when combined with machine learning tools, they can theoretically achieve fast and accurate prediction results. However, on the path to practical applications, training a reliable machine learning model is faced with the challenge of uneven data distribution in a vast array of non-negligible solvent types. Herein, we employ a combination of the first-principles-based approach and data-driven model. Specifically, we utilize density functional theory (DFT) to calculate theoretical spectral data of CO-Ag adsorption in 23 different solvent systems as a data source. Subsequently, we propose a hierarchical knowledge extraction multiexpert neural network (HMNN) to bridge the knowledge gaps among different solvent systems. HMNN undergoes two training tiers: in tier I, it learns fundamental quantitative spectra-property relationships (QSPRs), and in tier II, it inherits the fundamental QSPR knowledge from previous steps through a dynamic integration of expert modules and subsequently captures the solvent differences. The results demonstrate HMNN's superiority in estimating a range of molecular adsorbate properties, with an error range of less than 0.008 eV for zero-shot predictions on unseen solvents. The findings underscore the usability, reliability, and convenience of HMNN and could pave the way for real-time access to microscopic properties by exploiting QSPR.

Repairing Noise-Contaminated Low-Frequency Vibrational Spectra with an Attention U-Net.

Low-frequency vibrational modes in infrared (IR) and Raman spectra, often termed molecular fingerprints, are sensitive probes of subtle structural changes and chemical interactions. However, their inherent weakness and susceptibility to environmental interference make them challenging to detect and analyze. To tackle this issue, we developed a deep learning denoising protocol based on an attention-enhanced U-net architecture. This model leverages the inherent correlations between high- and low-frequency vibrational modes within a molecule, effectively reconstructing low-frequency spectral features from their high-frequency counterparts. We demonstrate the effectiveness of this method by recovering low-frequency signals of trans-1,2-bis(4-pyridyl)ethylene (BPE) adsorbed on an Ag surface, a representative system for surface enhancement Raman spectroscopy (SERS). Notably, the trained model exhibits promising transferability to SERS spectra acquired under different surface and external field conditions. Furthermore, we applied this method to experimental IR and Raman spectra of BPE, achieving high-quality, low-frequency spectral recovery.

Water-Ice Microstructures and Hydration States of Acridinium Iodide Studied by Phosphorescence Spectroscopy.

Ice has been suggested to have played a significant role in the origin of life partly owing to its ability to concentrate organic molecules and promote reaction efficiency. However, the techniques for studying organic molecules in ice are absorption-based, which limits the sensitivity of measurements. Here we introduce an emission-based method to study organic molecules in water ice: the phosphorescence displays high sensitivity depending on the hydration state of an organic salt probe, acridinium iodide (ADI). The designed ADI aqueous system exhibits phosphorescence that can be severely perturbed when the temperature is higher than 110 K at a concentration of the order of 10-5 M, indicating changes in hydration for ADI. Using the ADI phosphorescent probe, it is found that the microstructures of water ice, i.e., crystalline vs. glassy, can be strongly dictated by a trace amount (as low as 10-5 M) of water-soluble organic molecules. Consistent with cryoSEM images and temperature-dependent Raman spectral data, the ADI is dehydrated in more crystalline ice and hydrated in more glassy ice. The current investigation serves as a starting point for using more sensitive spectroscopic techniques for studying water-organics interactions at a much lower concentration and wider temperature range.

Disorder-Enhanced Charge-Transfer-Mediated Room-Temperature Phosphorescence in Polymer Media.

Room-temperature phosphorescence (RTP) polymers have important applications for biological imaging, oxygen sensing, data encryption, and photodynamic therapy. Despite the many advantages polymeric materials offer such as great control over gas permeability and processing flexibility, disorder is traditionally considered as an intrinsic negative impact on the efficiency for embedded RTP luminophores, as various allowed thermal motions could quench the emitting states. However, we propose that such disorder-enabled freedoms of microscopic motions can be beneficial for charge-transfer-mediated RTP, which is facilitated by molecular conformational changes among different electronic transition states. Using the "classic" pyrene-aniline exciplex as an example, we demonstrate the mutual enhancement of red/near-infrared and green RTP emissions from the pyrene and aniline moieties, respectively, upon doping the aniline polymer with trace pyrene derivatives. In comparison, a pyrene-doped crystal formed with the same aniline structure exhibits only charge-transfer fluorescence with no red or green RTP observed, suggesting that order suppresses the RTP channels. The proposed polymerization strategy may be used as a unified method to generate multi-emissive polymeric RTP materials from a vast pool of known and unknown exciplexes and charge-transfer complexes.

Origin of Red-Shifted Phosphorescence from Triphenylamines: Triplet Excimer or Impurity?

Triphenylamine (TPA) was reported to exhibit temperature-dependent dual phosphorescence, where the red-shifted band was assigned as the excimeric phosphorescence with an energy shift of >3000 cm-1 (J. Phys. Chem. 1991, 95, 7189). Here we show that purified TPA (purity: >99.97 %) shows a single phosphorescence band with a small energy shift of <200 cm-1 under the same experimental conditions. The new experimental results, along with theoretical calculations, suggest that the previously reported triplet excimer of TPA is probably not valid and is most likely due to an unidentified impurity. As-received TPA samples, however, do exhibit temperature-dependent dual phosphorescence bands, and the wavelength, relative intensity, and temperature dependence of the lower-energy phosphorescence band vary significantly depending on dopant structures. It was found that dopant phosphorescence could still become dominant even in dilute third-party solutions of the host at low temperature.

External Heavy-Atom Effect via Orbital Interactions Revealed by Single-Crystal X-ray Diffraction.

Enhanced spin-orbit coupling through external heavy-atom effect (EHE) has been routinely used to induce room-temperature phosphorescence (RTP) for purely organic molecular materials. Therefore, understanding the nature of EHE, i.e., the specific orbital interactions between the external heavy atom and the luminophore, is of essential importance in molecular design. For organic systems, halogens (e.g., Cl, Br, and I) are the most commonly seen heavy atoms serving to realize the EHE-related RTP. In this report, we conduct an investigation on how heavy-atom perturbers and aromatic luminophores interact on the basis of data obtained from crystallography. We synthesized two classes of molecular systems including N-haloalkyl-substituted carbazoles and quinolinium halides, where the luminescent molecules are considered as "base" or "acid" relative to the heavy-atom perturbers, respectively. We propose that electron donation from a π molecular orbital (MO) of the carbazole to the σ* MO of the C-X bond (π/σ*) and n electron donation to a π* MO of the quinolinium moiety (n/π*) are responsible for the EHE (RTP) in the solid state, respectively.

Rapid room-temperature phosphorescence chiral recognition of natural amino acids.

Chiral recognition of amino acids is very important in both chemical and life sciences. Although chiral recognition with luminescence has many advantages such as being inexpensive, it is usually slow and lacks generality as the recognition module relies on structural complementarity. Here, we show that one single molecular-solid sensor, L-phenylalanine derived benzamide, can manifest the structural difference between the natural, left-handed amino acid and its right-handed counterpart via the difference of room-temperature phosphorescence (RTP) irrespective of the specific chemical structure. To realize rapid and reliable sensing, the doped samples are obtained as nanocrystals from evaporation of the tetrahydrofuran solutions, which allows for efficient triplet-triplet energy transfer to the chiral analytes generated in situ from chiral amino acids. The results show that L-analytes induce strong RTP, whereas the unnatural D-analytes produce barely any afterglow. The method expands the scope of luminescence chiral sensing by lessening the requirement for specific molecular structures.

Label-Free Data Mining of Scientific Literature by Unsupervised Syntactic Distance Analysis.

Label-free data mining can efficiently feed large amounts of data from the vast scientific literature into artificial intelligence (AI) processing systems. Here, we demonstrate an unsupervised syntactic distance analysis (SDA) approach that is capable of mining chemical substances, functions, properties, and operations without annotation. This SDA approach was evaluated in several areas of research from the physical sciences and achieved performance in information mining comparable to that of supervised learning, as shown by its satisfactory scores of 0.62-0.72, 0.60-0.82, and 0.86-0.95 in precision, recall, and accuracy, respectively. We also showcase how our approach can assist robotic chemists programmed to perform research focused on double-perovskite colloidal nanocrystals, gold colloidal nanocrystals, oxygen evolution reaction catalysts, and enzyme-like catalysts by designing materials, formulations, and synthesis parameters based on data mined from 1.1 million literature references.

Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor.

The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities. WOA-LFGA is tested with 29 mathematical optimization problems and a WSN coverage optimization model. Simulation results demonstrate that the improved algorithm is highly competitive compared with mainstream algorithms. Moreover, the practicality and the effectiveness of the improved algorithm in optimizing wireless sensor network coverage are confirmed.

A Quick Method for Predicting Reflectance Spectra of Nanophotonic Devices via Artificial Neural Network.

Nanophotonics use the interaction between light and subwavelength structures to design nanophotonic devices and to show unique optical, electromagnetic, and acoustic properties that natural materials do not have. However, this usually requires considerable expertise and a lot of time-consuming electromagnetic simulations. With the continuous development of artificial intelligence, people are turning to deep learning for designing nanophotonic devices. Deep learning models can continuously fit the correlation function between the input parameters and output, using models with weights and biases that can obtain results in milliseconds to seconds. In this paper, we use finite-difference time-domain for simulations, and we obtain the reflectance spectra from 2430 different structures. Based on these reflectance spectra data, we use neural networks for training, which can quickly predict unseen structural reflectance spectra. The effectiveness of this method is verified by comparing the predicted results to the simulation results. Almost all results maintain the main trend, the MSE of 94% predictions are below 10-3, all are below 10-2, and the MAE of 97% predictions are below 2 × 10-2. This approach can speed up device design and optimization, and provides reference for scientific researchers.

A robotic AI-Chemist system for multi-modal AI-ready database.

By fusing literature data mining, high-performance simulations, and high-accuracy experiments, robotic AI-Chemist can achieve automated high-throughput production, classification, cleaning, association and fusion of data, and thus develop a multi-modal AI-ready database.

Research Progress of p-Type Oxide Thin-Film Transistors.

The development of transparent electronics has advanced metal-oxide-semiconductor Thin-Film transistor (TFT) technology. In the field of flat-panel displays, as basic units, TFTs play an important role in achieving high speed, brightness, and screen contrast ratio to display information by controlling liquid crystal pixel dots. Oxide TFTs have gradually replaced silicon-based TFTs owing to their field-effect mobility, stability, and responsiveness. In the market, n-type oxide TFTs have been widely used, and their preparation methods have been gradually refined; however, p-Type oxide TFTs with the same properties are difficult to obtain. Fabricating p-Type oxide TFTs with the same performance as n-type oxide TFTs can ensure more energy-efficient complementary electronics and better transparent display applications. This paper summarizes the basic understanding of the structure and performance of the p-Type oxide TFTs, expounding the research progress and challenges of oxide transistors. The microstructures of the three types of p-Type oxides and significant efforts to improve the performance of oxide TFTs are highlighted. Finally, the latest progress and prospects of oxide TFTs based on p-Type oxide semiconductors and other p-Type semiconductor electronic devices are discussed.

An all-round AI-Chemist with a scientific mind.

The realization of automated chemical experiments by robots unveiled the prelude to an artificial intelligence (AI) laboratory. Several AI-based systems or robots with specific chemical skills have been demonstrated, but conducting all-round scientific research remains challenging. Here, we present an all-round AI-Chemist equipped with scientific data intelligence that is capable of performing basic tasks generally required in chemical research. Based on a service platform, the AI-Chemist is able to automatically read the literatures from a cloud database and propose experimental plans accordingly. It can control a mobile robot in-house or online to automatically execute the complete experimental process on 14 workstations, including synthesis, characterization and performance tests. The experimental data can be simultaneously analysed by the computational brain of the AI-Chemist through machine learning and Bayesian optimization, allowing a new hypothesis for the next iteration to be proposed. The competence of the AI-Chemist has been scrutinized by three different chemical tasks. In the future, the more advanced all-round AI-Chemists equipped with scientific data intelligence may cause changes to the landscape of the chemical laboratory.

Microstructure and mechanical properties of a newly developed low Young's modulus Ti-15Zr-5Cr-2Al biomedical alloy.

The Ti-15Zr-5Cr-2Al alloy has been developed and various heat treatments have been investigated to develop new biomedical materials. It is found that the heat treatment conditions strongly affect the phase constitutions and mechanical properties. The as-cast specimen is comprised of ß phase and a small fraction of α phase, which is attributed to the suppression of ω phase caused by adding Al. A high yield strength of 1148±36MPa and moderate Young's modulus of 96±3GPa are obtained in the as-cast specimen. Besides the ß phase and α phase, ω phase is also detected in the air cooled and liquid nitrogen quenched specimens, which increases the Young's modulus and lowers the ductility. In contrast, only ß phase is detected after ice water quenching. The ice water quenched specimen exhibits a good combination of mechanical properties with a high microhardness of 302±10HV, a large plastic strain of 23±2%, a low Young's modulus of 58±4GPa, a moderate yield strength of 625±32MPa and a high compressive strength of 1880±59MPa. Moreover, the elastic energies of the ice water quenched specimen (3.22MJ/m3) and as-cast specimen (6.86MJ/m3) are higher than that of c.p. Ti (1.25MJ/m3). These results demonstrate that as-cast and ice water quenched Ti-15Zr-5Cr-2Al alloys with a superior combination of mechanical properties are potential materials for biomedical applications.