Predicting new research directions in materials science using large language models and concept graphs - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFB4bkczZlNBWVNwTkF3UFR1VkpiTGE4U0drSnNHa3J3WnlnOGtMbEdaLTE5VUhsaGpCUHFieWZWNjZ1UEE3Qk9nb1NEbklNNEE4aERhWVUyWndLLUZNYkdR?oc=5" target="_blank">Predicting new research directions in materials science using large language models and concept graphs</a> <font color="#6f6f6f">Nature</font>
Could not retrieve the full article text.
Read on Google News: LLM →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
modellanguage modelresearch
The Minds Shaping AI: Meet the Keynote Speakers at ODSC AI East 2026
If you want to understand where AI is actually going, not just what’s trending, you look at who’s building it, scaling it, and questioning its limits. That’s exactly what the ODSC AI East 2026 keynote speakers lineup delivers. This year’s speakers span the full spectrum of AI: from foundational theory and cutting-edge research to enterprise deployment, governance, and workforce transformation. These are the people defining how AI moves from hype to real-world impact. Here’s who you’ll hear from and why missing them would mean missing where AI is headed next. The ODSC AI East 2026 Keynote Speakers Matt Sigelman, President at Burning Glass Institute Matt Sigelman is one of the foremost experts on labor market dynamics and the future of work. As President of the Burning Glass Institute, he ha
15 Datasets for Training and Evaluating AI Agents
Datasets for training and evaluating AI agents are the foundation of reliable agentic systems. Agents don’t magically work — they need structured data that teaches action-taking: tool calling, web interaction, and multi-step planning. Just as importantly, they need evaluation datasets that catch regressions before those failures hit production. This is where most teams struggle. A chat model can sound correct while failing at execution, like returning invalid JSON, calling the wrong API, clicking the wrong element, or generating code that doesn’t actually fix the issue. In agentic workflows, those small failures compound across steps, turning minor errors into broken pipelines. That’s why datasets for training and evaluating AI agents should be treated as infrastructure, not a one-time res
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
Semantic matching in graph space without matrix computation and hallucinations and no GPU
Hello AI community,For the past few months, I’ve been rethinking how AI should process language and logic. Instead of relying on heavy matrix multiplications (Attention mechanisms) to statistically guess the next word inside an unexplainable black box, I asked a different question: What if concepts existed in a physical, multi-dimensional graph space where logic is visually traceable?I am excited to share our experimental architecture. To be absolutely clear: this is not a GraphRAG system built on top of an existing LLM. This is a standalone Native Graph Cognitive Engine.The Core Philosophy:Zero-Black-Box (Total Explainability): Modern LLMs are black boxes; you never truly know why they chose a specific token. Our engine is a “glass brain.” Every logical leap and every generated sentence i
b8679
llama-bench: add -fitc and -fitt to arguments ( #21304 ) llama-bench: add -fitc and -fitt to arguments update README.md address review comments update compare-llama-bench.py macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.1 DLLs Windows x64 (Vulkan) Windows x64 (SYCL) Windows x64 (HIP) openEuler: openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph)
15 Datasets for Training and Evaluating AI Agents
Datasets for training and evaluating AI agents are the foundation of reliable agentic systems. Agents don’t magically work — they need structured data that teaches action-taking: tool calling, web interaction, and multi-step planning. Just as importantly, they need evaluation datasets that catch regressions before those failures hit production. This is where most teams struggle. A chat model can sound correct while failing at execution, like returning invalid JSON, calling the wrong API, clicking the wrong element, or generating code that doesn’t actually fix the issue. In agentic workflows, those small failures compound across steps, turning minor errors into broken pipelines. That’s why datasets for training and evaluating AI agents should be treated as infrastructure, not a one-time res
The Minds Shaping AI: Meet the Keynote Speakers at ODSC AI East 2026
If you want to understand where AI is actually going, not just what’s trending, you look at who’s building it, scaling it, and questioning its limits. That’s exactly what the ODSC AI East 2026 keynote speakers lineup delivers. This year’s speakers span the full spectrum of AI: from foundational theory and cutting-edge research to enterprise deployment, governance, and workforce transformation. These are the people defining how AI moves from hype to real-world impact. Here’s who you’ll hear from and why missing them would mean missing where AI is headed next. The ODSC AI East 2026 Keynote Speakers Matt Sigelman, President at Burning Glass Institute Matt Sigelman is one of the foremost experts on labor market dynamics and the future of work. As President of the Burning Glass Institute, he ha
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!