This construction provides greater control than the typical construction of random Voronoi lattices based only on the minimum internuclei distance (see, e.g., ref. 40). Note that sampling one Voronoi network requires 5000–10,000 MCMC iterations, depending on the λi-values (i.e., the ...
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing ...
This activity has the potential to exhaust energy reserves in metabolically active tumor cells and trigger apoptosis. Furthermore, Daporinad may hinder the production of vascular endothelial growth factor (VEGF) in tumor cells, thereby inhibiting tumor angiogenesis. Daporinad has been clinically tested...
Carbonaceous aerosols such as black carbon (BC) are important short-lived climate forcers1,2. To understand their impact on climate, accurate predictions of the optical properties of absorbing aerosols such as BC are needed in atmospheric models and observational retrievals: for estimating the top-o...
Also, soft matching provides each vertex with a ranked list of potential correspondents instead of a single proposed correspondent, which leaves the practitioner with recourse in the event of discovery (by other means) that a proposed correspondent is not correct. As mentioned in Section 1, the ...
The construction of flexible models of the interatomic potential energy based on machine learning, and in particular neural networks, has shown great promise in providing a way to move past this dilemma, promising to learn high-fidelity potentials from ab-initio reference calculations while retaining ...
Using the equation of hyperbolic distance (X), the loss function, Lrec for reconstructing the hierarchical structure is defined. The set of connected node pairs in Hie is denoted as A={(i,j)|ci,cj∈Hie}, and nodes not connected to ci are in the set N(i)={j′|(ci,cj′)∉A}....
Average distance: 这个很好理解,就是所有两两节点之间的最短距离的平均值,最直接的描述了图的紧密程度。 Eccentricity:这个参数描述的是从任意一个节点,到达其他节点的最大距离 Diameter:图中的最大两个节点间的距离 Radius:图中的最小两个节点间的距离 ...
This process enables the amalgamation of skeleton features into higher-level representations, effectively capturing the abstract characteristics of various actions. Subsequently, these refined features are mapped to various potential action categories, yielding probability distributions for each category. Further...
After generating the various graph data from both CEFEM simulations and HEDM results (see: Sections “Polycrystal anisotropic elasticity data” and “High-energy X-ray diffraction microscopy data”) for GNN surrogate model training, a study was completed to examine the performance of the GNNs predict...