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
Optimization and inverse design of photonic crystals using deep reinforcement learning
A PyTorch Implementation of "Optimization of Molecules via Deep Reinforcement Learning".
Differentiable wave optics simulation library built on PyTorch
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
Differentiable Flexible Mechanical Metamaterials
A Generic Framework for Optical Inverse Design
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
Inverse design of Plasmonic Metasurfaces with Bidirectional Adversarial AutoEncoder
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