Chemformer: a pre-trained transformer for computational chemistry

A Pre-Trained Ensemble Model for Breast Cancer Grade Detection Based on Small Datasets

Background and Purpose: Nowadays, breast cancer is reported as one of the most common cancers amongst women. Early detection of the cancer type is essential to aid in informing subsequent treatments. The newest proposed breast cancer detectors are based on deep learning. Most of these works focus on large-datasets and are not developed for small datasets. Although the large datasets might lead ...

Methylene: a paradigm for computational quantum chemistry.

The year 1970 has been suggested as a starting date for the "third age of quantum chemistry," in which theory takes on not only qualitative but also quantitative value. In fact, each of the years 1960, 1970, 1972, and 1977 is of historical value in the unraveling of the structure and energetics of the CH(2) molecule, methylene. What took place for methylene, namely the establishment of credibil...

Numerical Differential Protection of Power Transformer using GA Trained ANN

This paper presents the use of ANN as a pattern classifier for differential protection of power transformer, which makes the discrimination among normal, magnetizing inrush, over-excitation, external fault and internal fault currents. The Back Propagation Neural Network Algorithm and Genetic Algorithm are used to train the multi-layered feed forward neural network and simulated results are comp...

Computational chemistry

Computational chemistry has come of age. With significant strides in computer hardware and software over the last few decades, computational chemistry has achieved full partnership with theory and experiment as a tool for understanding and predicting the behavior of a broad range of chemical, physical, and biological phenomena. The Nobel Prize award to John Pople and Walter Kohn in 1998 highlig...

Database Support for Computational Chemistry