·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
VisionQuest is what happens when AI really goes rogue2h◆Build specialized multimodal models with LFM32h◆Frontier AI labs say their models refuted the Exponential Time Hypothesis. What does that mean?2h◆Anthropic’s elaborate plan to keep Wall Street at bay2h◆Google’s Genie 4 world model adds audio, more realistic worlds, and real-time steering3h◆Firefox’s Smart Window adds AI form filling and webpage monitoring3h◆Can we trust LLMs to judge whether a research idea is novel?3h◆Google’s next Genie AI model generates more realistic experiences3h◆Meta AI director Rohit Patel leaves to take on Microsoft Office and Google Workspace4h◆Apple is adding an AI chatbot to Pages, Keynote, and Numbers5h◆Japan internet giant GMO says hackers posted stolen customer data6h◆Opera introduces a new web standard to let users deploy AI locally in their browser6h◆General Compute raises $100 million Series A, countering the neocloud disaster narrative6h◆Industrial Intelligence: The next growth opportunity for manufacturing8h◆Nvidia backs Indian startup helping companies run AI models with fewer GPUs8h◆Data center backlash is uniting America9h◆OpenAI expands partnership with Oracle to bring its apps to ChatGPT12h◆When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems15h◆Query-Driven Learning for Lifted Reasoning15h◆The Social World Model Evaluation Gap15h◆VisionQuest is what happens when AI really goes rogue2h◆Build specialized multimodal models with LFM32h◆Frontier AI labs say their models refuted the Exponential Time Hypothesis. What does that mean?2h◆Anthropic’s elaborate plan to keep Wall Street at bay2h◆Google’s Genie 4 world model adds audio, more realistic worlds, and real-time steering3h◆Firefox’s Smart Window adds AI form filling and webpage monitoring3h◆Can we trust LLMs to judge whether a research idea is novel?3h◆Google’s next Genie AI model generates more realistic experiences3h◆Meta AI director Rohit Patel leaves to take on Microsoft Office and Google Workspace4h◆Apple is adding an AI chatbot to Pages, Keynote, and Numbers5h◆Japan internet giant GMO says hackers posted stolen customer data6h◆Opera introduces a new web standard to let users deploy AI locally in their browser6h◆General Compute raises $100 million Series A, countering the neocloud disaster narrative6h◆Industrial Intelligence: The next growth opportunity for manufacturing8h◆Nvidia backs Indian startup helping companies run AI models with fewer GPUs8h◆Data center backlash is uniting America9h◆OpenAI expands partnership with Oracle to bring its apps to ChatGPT12h◆When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems15h◆Query-Driven Learning for Lifted Reasoning15h◆The Social World Model Evaluation Gap15h◆
News/Model Agnostic Graph Prompt Learning for Crystal Property Prediction
arxiv
PublishedJuly 13, 2026 at 4:00 AM
▲bullish

Model Agnostic Graph Prompt Learning for Crystal Property Prediction

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.08996v1 Announce Type: cross Abstract: Graph Neural Networks have emerged as a powerful tool for the fast and accurate prediction of various crystal properties. These models often encode domain-specific knowledge into their graph encoding modules, which increases their parameter size and

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Mentioned models
01
  • 01
    Graph Neural Networks
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#graph-neural-networks#crystal-properties#prompt-learning#cross-property-knowledge-transfer

No replies yet. Be first.

Mentioned models
01
  • 01
    Graph Neural Networks
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#graph-neural-networks#crystal-properties#prompt-learning#cross-property-knowledge-transfer

Related coverage

More from ARXIV
arxivWhen Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems15harxivQuery-Driven Learning for Lifted Reasoning15harxivThe Social World Model Evaluation Gap15h
The Bubble Brief
MON · WED · FRI

Read graph-neural-networks insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews