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Machine learning

Capturing Electron Correlation with Machine Learning through a Data-Driven CASPT2 Framework

Research from the University of Tennessee has introduced a novel method for capturing dynamic electron correlation using machine learning. The study, published in the Journal of Chemical Theory and Computation, presents a data-driven approach, dubbed DDCASPT2, which leverages features generated from lower-level electronic structure methods to recover missing electron correlation.

Medical research

Breakthrough in Cell Biology: IQSEC2 Modulates Postsynaptic Density Assembly through Ca2+-induced Phase Separation

Scientists at the Southern University of Science and Technology (SUSTech) have made a significant discovery in the field of cell biology, revealing that IQSEC2 modulates postsynaptic density assembly through Ca2+-induced phase separation. This research, published in the Journal of Cell Biology, provides new insights into the mechanisms underlying synaptic