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Keeping an eye on important news in the domain of AI / ML / Chemistry
A Living Review Pipeline for AI/ML Applications in Accelerator Physics
Oct 2025
This publication presents a continuous-update “living review” system for tracking how AI and ML are being applied in accelerator physics. It introduces automated pipelines for collecting, categorizing, and summarizing research-essentially using AI to monitor AI research. The authors showcase how ML models improve experiment optimization and diagnostics while maintaining reproducibility in scientific workflows. Beyond its physics focus, the piece reflects a broader 2025 trend: applying AI to meta-research and scientific automation.
Neural Networks Learn Quantum Chemistry at Exascale
Oct 2025
Researchers using China’s Sunway supercomputer have scaled a neural-network quantum-state (NNQS) approach to simulate molecular systems with up to 120 spin orbitals — a size that begins to “matter” for real-world chemistry. The team achieved very high efficiency at massive scale (37 million processor cores, ~92% strong scaling, ~98% weak scaling). Their work shows that classical AI/ML-style hardware and algorithms can now tackle quantum-chemistry problems previously thought to require specialized quantum computers. It also suggests that exascale supercomputing may be a practical pathway toward materials and drug discovery by bridging machine learning, chemistry, and physics.
AI, ML and Data Engineering Trends Report – 2025
Sept 2025
This comprehensive trends report, synthesizes developments that defined Q1 -Q3 of 2025, focusing on the convergence of AI engineering and data infrastructure. It covers the increasing adoption of vector databases, LLM-Ops pipelines, and multi-modal learning frameworks that combine text, images, and structured data. The report also highlights how teams are moving from experimentation to production-grade AI systems, emphasizing reliability, governance, and cost-efficient scaling. It serves as a benchmark for where enterprise AI engineering stood mid-2025.
Stanford AI Index Report 2025
Apr 2025
A comprehensive annual analysis of global AI progress – covering research output, model performance, funding, and policy trends. The 2025 edition shows a major drop in training costs and rising adoption of smaller, efficient models. It also highlights increasing focus on AI governance and regulation.
Five Trends in AI and Data Science for 2025
Jan 2025
This article by Thomas H. Davenport and Randy Bean outlines the key directions shaping the AI and data landscape at the start of 2025. It emphasizes the rise of agentic AI systems—models capable of autonomous decision-making—and the growing integration of unstructured data like video and text into enterprise analytics. The authors also stress a renewed push toward measurable ROI and governance as organizations mature in their AI adoption. It paints the picture of a year focused on practical scaling rather than hype.
ChemEval
Sept 2024
The paper “ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models” introduces ChemEval, a benchmark for testing LLMs on chemistry tasks. It spans four levels and 42 expert-designed tasks to measure reasoning and domain knowledge. Results show that general models like GPT-4 excel at broad reasoning but falter in deep molecular understanding, while specialized chemistry models perform better on technical details. The authors highlight ChemEval as a step toward improving AI’s competence in chemical research.
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