New method improves the accuracy of machine-learned potentials for simulating catalysts
Peer-Reviewed Publication
Updates every hour. Last Updated: 17-Sep-2025 02:11 ET (17-Sep-2025 06:11 GMT/UTC)
In Chaos, researchers in Istanbul develop a more efficient and flexible algorithm to model traffic. The model, which they call the data-driven macroscopic mobility model, relies on simple observations that city planners routinely collect, like how packed the streets are. The researchers tested their model on both synthetic benchmarks and real-world traffic data, and the faster simulation speeds and easier data requirements mean city planners may have the tools to design better, smarter cities.
In August 2017, the National Natural Science Foundation of China (NSFC) launched the Major Research Plan “Dynamic Modifications and Chemical Interventions of Biomacromolecules” (implementation period 2017–2025). Through interdisciplinary research that integrates chemistry, life sciences, medicine, mathematics, materials science, and information science, its aim is to develop specific labeling methods and detection techniques for dynamic chemical modifications of biomacromolecules, elucidate the recognition mechanisms and biological functions of dynamic modifications in the regulation of cellular traits, and discover potential drug targets and corresponding lead compounds related to dynamic biomacromolecular modifications. Since its establishment, this Major Research Plan has achieved significant progress and original results in many aspects such as the dynamic properties of biomacromolecular chemical modifications, regulatory mechanisms, and chemical interventions. Recently, members of the expert group, management group, and secretariat of the program collaborated to systematically review representative research achievements obtained since the program’s implementation, and jointly published a review article in CCS Chemistry. This review provides important references for promoting development in related frontier fields, as well as for the future trend of integration between chemistry, life sciences, and medicine.
A research team has developed a novel high-throughput phenotyping platform, the Multispectral Automated Dynamic Imager (MADI), to monitor plant growth and stress in real time.