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)
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.
[NeurIPS 2020] Diversity-Guided Efficient Multi-Objective Optimization With Batch Evaluations
Differentiable wave optics simulation library built on PyTorch
Optimization and inverse design of photonic crystals using deep reinforcement learning
A PyTorch Implementation of "Optimization of Molecules via Deep Reinforcement Learning".
Differentiable electromagnetics simulation library for moving point charges built on JAX
Silicon Photonics Design Tools.
Efficient GPU-computing simulation for differentiable crystal plasticity finite element method
A collection of inverse design challenges
[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
AI-assisted Photonic Device Inverse Design Framework, MAPS DATE 2025
A Generic Framework for Optical Inverse Design
Differentiable Flexible Mechanical Metamaterials
Fast and easy electromagnetic simulation and inverse design. ✨
BC-Design: A Biochemistry-Aware Framework for High-Precision Inverse Protein Folding https://www.biorxiv.org/content/10.1101/2024.10.28.620755v2
Mapping properties to molecules in QM7-X
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