Optical neural networks capable of executing large-scale operations in parallel at the speed of light play an ever more prominent role in energy-efficient artificial intelligence. Towards this goal, scientists in China, inspired by the human brain, invented a backpropagation-free framework (B-ONN) for local learning. The technique will open new avenues for deploying physically robust, large-scale optical computing systems in real-world edge devices and green infrastructures.

A research team from the School of Physics at Renmin University of China has developed PhononBench, a large-scale phonon-based benchmark for evaluating the dynamical stability of AI-generated crystal materials. By combining the MatterSim-v1 universal machine-learning interatomic potential with Phonopy, the study enables high-throughput phonon-spectrum calculations for more than 130,000 AI-generated crystal structures. The benchmark systematically evaluates the dynamical stability of crystals generated by different AI models, identifies 32,995 MatterSim-labeled dynamically stable crystal structures under a strict phonon-frequency criterion, and provides a web-based platform for rapid phonon-spectrum calculation and dynamical-stability assessment.

Researchers at the Institute of Computing Technology, Chinese Academy of Sciences, have developed AtomFlow, a flow-matching model that predicts three-dimensional reactant structures from a product structure. It preserves the number and types of atoms throughout generation. Tested on small organic molecules with no more than seven heavy atoms in the Transition1x benchmark, its MatRIS version achieved 96.2% Top-10 accuracy, meaning that a matching reactant was found among the ten highest-ranked candidates in 96.2% of evaluated cases.

Working backwards from a target molecule to possible precursors, known as retrosynthesis, helps chemists plan syntheses for drug discovery and materials research. Many AI approaches represent molecules as SMILES strings and learn to translate products into reactants. These representations do not explicitly encode three-dimensional atomic coordinates, and sequence generation alone does not guarantee conservation of each element’s atom count. AtomFlow addresses this constraint by keeping the atoms fixed in identity while learning how their coordinates change.

Researchers from the Institute of Coal Chemistry, Chinese Academy of Sciences, have identified the nickel dimer (Ni₂) play vital role inCO₂ methanation. Encapsulated in MFI zeolite with MnOₓ promotion at only ~0.86% Ni loading, the catalyst achieves ~76% CO₂ conversion, ~98% CH₄ selectivity and CH₄ space time yield of ~450 mol·mol Ni^-1·h^-1 at 400°C.

A research team has identified the odor-active compounds responsible for the soapy and metallic off-flavors when cold-pressed rapeseed oil undergoes alkali refining. 

China is the world’s largest tea producer, and the tea industry is an important specialty industry for increasing agricultural efficiency and farmers’ income. Frequent tea diseases severely affect yield and quality. Traditional manual diagnosis is inefficient and error-prone, while conventional plant protection operations rely mainly on large-scale pesticide application, resulting in high costs and serious pollution. Current deep learning-based disease detection technologies are easily affected by complex environments in real tea plantations, such as lighting, occlusion, and rainy weather, and their accuracy and stability are insufficient to meet real-time field detection needs, becoming a key bottleneck in the development of smart tea plantations.

A research team has identified a purified fucoidan fraction from the edible brown alga Laminaria japonica that shows strong anti-allergic activity in laboratory cell models. 

With the widespread application of radar in space target detection and identification, acquiring radar images of non-cooperative targets at different attitudes has become the data foundation for supporting deep learning-based automatic target recognition algorithms. However, the uncontrollable motion trajectories of non-cooperative targets and limited illumination time render the acquisition of measured images extremely difficult; although anechoic chamber measurements and electromagnetic computations can obtain multi-attitude images, they are respectively constrained by high costs and computational time, making it difficult to meet the demand for large-scale data. Existing image generation methods based on Generative Adversarial Networks (GANs) can augment data scales, but most can only control the azimuth angle without precisely constraining the elevation angle, resulting in generated images whose projection morphology does not match reality. Therefore, how to achieve radar image generation with precise dual-dimensional control over both azimuth and elevation angles has become a key technical challenge in data augmentation for radar recognition of non-cooperative targets.