Scalar coupling constant (SCC), directly measured by nuclear magnetic resonance (NMR) spectroscopy, is a key parameter for molecular structure analysis, and widely used to predict unknown molecular structure. Restricted by the high cost of NMR experiments, it is impossible to measure the SCC of unknown molecules on a large scale. Using density functional theory (DFT) to theoretically calculate the SCC of molecules is incredibly challenging, due to the cost of substantial computational time and space. Graph neural networks (GNN) of artificial intelligence (AI) have great potential in constructing molecul ar-like topology models, which endows them the ability to rapidly predict SCC through data-driven machine learning methods, and avoiding time-consuming quantum chemical calculations. With a priori knowledge of angles, we propose a graph angle-attention neural network (GAANN) model to predict SCC by means of some easily accessible related information. GAANN, with a multilayer message-passing network and a self-attention mechanism, can accurately simulate the molecular-like topological structure and predict molecular properties. Our simulations show that the prediction accuracy by GAANN, with the log(MAE) = −2.52, is close to that by DFT calculations. Different from conventional AI methods, GAANN combining the AI method with quantum chemistry theory (Karplus equation) has a strong physicochemical interpretability about angles. From an AI perspective, we find that bond angle has the highest correlation with the SCC among all angle features (dihedral angle, bond angle, geometric angles) about multiple coupling types in the small molecule datasets.

No abstract

The influence of aging treatment on microstructure, mechanical and electrical properties of 7A52 alloy was investigated by mechanical property test, electrical conductivity measurement, XRD and TEM analysis. The results showed that the alloy was a typical aging hardening alloy. During aging supersaturated solution decomposed, G.P. zones and η'(MgZn2) precipitated. The precipitates of the alloy aging at 100°C were mainly G.P. zones with the size of about 5nm. During aging at 120°C, η’ precipitates nucleated from the G.P. zones. With increasing aging temperature and time, the size and amount of η' increased. As aged at 120°C for 24h the tensile strength, yield strength, elongation and electrical conductivity of the alloy were 496 MPa, 445 MPa, 9.5% and 26 %IACS, respectively. The G.P. zones and η' dispersion strengthening are the major strengthening mechanisms for the studied alloy at the single-stage peak aging.

No abstract