https://github.com/facebookresearch/all-atom-diffusion-transformer
공식 코드All-atom Diffusion Transformers: Unified generative modelling of molecules and materials
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
This task evaluates the ability of generative models to sample valid and realistic crystal structures.
The training dataset, MP20 (Xie et al., 2022), contains 45,231 metastable crystal structures from the Materials Project (Jain et al., 2013), each with up to 20 atoms and spanning 89 different element types.
Following prior work (Xie et al., 2022), we sample 10,000 crystals and compute validity, stability, uniqueness and novelty rates using density functional theory (DFT). Data is generally split following prior work (Xie et al., 2022) to ensure fair comparisons.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
All-atom Diffusion Transformers: Unified generative modelling of molecules and materials
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.