Piezoelectric transformers have a high potential for use in power supplies due to large power density and flexibility in design. A lot of work has been done in piezoelectric transformer design and analysis of their operation in different regimes and in different electronic circuits. In this paper an attempt has been made to investigate the influence of temperature, mechanical and electrical field stress, properties of piezoceramic material and regimes of piezoelectric transformer operation on the value of its power density. The results of the presented calculations of piezoelectric transformer parameters show a good correlation with measured data on experimental transformers. A substantial improvement of piezoelectric transformer power density seems feasible, making them more attractive for a wider range of applications in power supplies. In addition, useful guidelines for piezoelectric transformer design and selection criteria for piezoelectric materials for transformers have been established.

The equations for the output power in table 1 contained an error. The product of frequency and output capacitance belongs to the numerator rather than the denominator. The corrected equations are provided in the PDF file.

Due to their computational efficiency, 2D fingerprints are typically used in similarity-based high-content screening. The interaction of a ligand with its target protein, however, relies on its physicochemical interactions in 3D space. Thus, ligands with different 2D scaffolds can bind to the same protein if these ligands share similar interaction patterns. Molecular fields can represent those interaction profiles. For efficiency, the extrema of those molecular fields, named field points, are used to quantify the ligand similarity in 3D. The calculation of field points involves the evaluation of the interaction energy between the ligand and a small probe shifted on a fine grid representing the molecular surface. These calculations are computationally prohibitive for large datasets of ligands, making field point representations of molecules intractable for high-content screening. Here, we overcome this roadblock by one-shot prediction of field points using generative neural networks based on the molecular structure alone. Field points are predicted by training an SE(3)-Transformer, an equivariant, attention-based graph neural network architecture, on a large set of ligands with field point data. Resulting data demonstrates the feasibility of this approach to precisely generate negative, positive and hydrophobic field points within 0.5 Å of the ground truth for a diverse set of drug-like molecules.