machine learning

This post explores machine learning models for classifying policy area of congressional bills.

Explore the pivotal role of Coulomb matrices in representing molecular structures for machine learning. Learn about their calculation and application as a molecular descriptor in advancing computational chemistry.

This repository contains scripts for generating trajectories of mini proteins using GROMACS. Mini proteins are small proteins with only a few amino acids, and they are frequently used as test cases for new methods in molecular dynamics, from new force fields to new sampling algorithms. This repository contains scripts for generating trajectories of mini proteins, including alanine dipeptide, glycine dipeptide, isoleucine dipeptide, leucine dipeptide, methionine dipeptide, phenylalanine dipeptide, proline dipeptide, tryptophan dipeptide, and valine dipeptide.

Discover HyperNEAT, the advanced neuroevolution strategy for generating complex neural networks.

Dive into the basics of NEAT, a method for evolving artificial neural networks through genetic algorithms and the evolution of network structure.