Unverified60% confidenceFactExact time
The output of GNoME is essentially a prediction of thermodynamically potentially stable structures, and does not incorporate engineering considerations such as synthesis pathway design, kinetic stability, impurity tolerance, or process windows.
2
Sources
60%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
The Real Bottleneck in Materials Innovation: Why Scaling Up Is Harder Than Discovery
hackernewshackernews7/10/2026
Related Claims
UnverifiedGNoME基于图神经网络（GNN），通过学习Materials Project等密度泛函理论（DFT）数据库中已知晶体结构稳定性规律进行预测68% similarUnverifiedCAPABILITY declarations are preserved when creating a model from GGUF or safetensors, when inheriting via a model, and when exporting a Modelfile.66% similarUnverifiedUnsloth's Dynamic GGUF dynamically selects quantization precision based on the sensitivity of different layers and weights, achieving a balance between compression ratio and accuracy.63% similarUnverifiedOrchestration reliability is not a model capability that can be bought with more parameters, but a property of the loop structure itself.62% similarUnverifiedGNoME采用主动学习策略，迭代式地生成候选、预测稳定性、筛选最有价值结构送入DFT计算验证，再更新模型62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/487668API
curl https://kongchang.com/api/v1/knowledge/claims/487668MCP
get_claim(id=487668)