TorchANI 2.0 is an open-source library that supports training, development, and research of ANI-style neural network interatomic potentials. It was originally developed and is currently maintained by the Roitberg group.
Die Hauptfunktionen von aiqm/torchani sind: Interatomic Potentials, Data and Graph Processing, Developer Tools, To be Classified.
Open-Source-Alternativen zu aiqm/torchani sind unter anderem: cornellius-gp/gpytorch — GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process… dmlc/dgl — DGL is a Python library for building and training graph neural networks. It functions as a graph message passing… bachili/redner — Differentiable rendering without approximation. bharathgs/nalu. cvxgrp/cvxpylayers — CVXPYlayers is a Python library for constructing differentiable convex optimization layers in PyTorch, JAX, and MLX… ferrine/geoopt — Riemannian Adaptive Optimization Methods with pytorch optim.
GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process models. It provides a system for Gaussian process modeling and uncertainty estimation, designed to perform efficient matrix operations on graphics hardware. The framework features a modular kernel system for constructing custom covariance functions and modeling complex data dependencies. It specifically integrates Gaussian processes with deep neural networks to create hybrid models for regression and classification. The system employs numerical linear algebra techniques, inclu
Differentiable rendering without approximation.
CVXPYlayers is a Python library for constructing differentiable convex optimization layers in PyTorch, JAX, and MLX using CVXPY. A convex optimization layer solves a parametrized convex optimization problem in the forward pass to produce a solution. It computes the derivative of the solution…