torch-molecule is a deep learning package for molecular discovery, designed with an sklearn-style interface for property prediction, inverse design and representation learning.

gRNAde: Geometric Deep Learning for 3D RNA inverse design (ICLR 2025 Spotlight)

[NeurIPS 2020] Diversity-Guided Efficient Multi-Objective Optimization With Batch Evaluations

This repository hosts a simple demonstration of a deep learning approach for the inverse design of patch antennas. The goal is to explore energy-efficient designs and to significantly reduce simulation cost compared to conventional methods.

A PyTorch Implementation of "Optimization of Molecules via Deep Reinforcement Learning".

Optimization and inverse design of photonic crystals using deep reinforcement learning

Silicon Photonics Design Tools.

Differentiable wave optics simulation library built on PyTorch

A collection of inverse design challenges

Efficient GPU-computing simulation for differentiable crystal plasticity finite element method

[ICLR24] CinDM uses compositional generative models to design boundaries and initial states significantly more complex than the ones seen in training for physical simulation

PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design

Differentiable Flexible Mechanical Metamaterials

Simple GUI for PROFOIL

BC-Design: A Biochemistry-Aware Framework for High-Precision Inverse Protein Folding

A Generic Framework for Optical Inverse Design

Mapping properties to molecules in QM7-X

Fast and easy electromagnetic simulation and inverse design. ✨

IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics, KDD'23

Codes of the custom edge-guided analog-and-digital optimization algorithm for the inverse design of silicon photonic devices. These are supporting materials for submitted paper "Edge-guided inverse design of digital metamaterials for high-capacity multi-dimensional optical interconnect" @nature Communications.