README.md

SG-Structure Generation

1.Introduction

The structure generation (SG) task tackles the inverse-design challenge of creating entirely new crystal structures that satisfy stability and functional constraints without exhaustive enumeration. Models first embed known crystals into symmetry-aware latent spaces—fractional-coordinate graphs, Wyckoff-sequence tokens, or E(3)-equivariant voxel fields. Generators—diffusion models, graph-autoregressive Transformers, or symmetry-equivariant GANs—sample this space. Running on a single GPU, the framework can propose over a thousand candidate crystal structures per minute, dramatically lowering the trial-and-error cost of discovering scintillators, solid-state electrolytes, and high-entropy compounds. Combined with a rapid, tiered screening funnel—machine-learning potential relaxation, energy threshold filtering, and final DFT refinement—this keeps computation affordable and tightly couples theory with experiment.

2.Models Matrix

Supported Functions	DiffCSP	MatterGen
Support Material Types
Inorganic Materials	✅	✅
Structure Generation
Random Sample	✅	✅
Condition Sample	✅	✅
ML Capabilities · Training
Single-GPU	✅	✅
Distributed Train	✅	✅
Mixed Precision	-	-
Fine-tuning	✅	✅
Uncertainty / Active-Learning	-	-
Dynamic→Static	-	-
Compiler CINN	-	-
ML Capabilities · Predict
Distillation / Pruning	-	-
Standard inference	✅	✅
Distributed inference	-	-
Compiler CINN	-	-
Dataset
Materials Project
MP20	✅	✅
Hrbrid
ALEX MP20	-	✅
ML2DDB🌟	-	✅

Notice:🌟 represent originate research work published from paddlematerials toolkit