BestHub
Sign in
Tagged articles
11 articles
Page 1 of 1
Data Party THU
Data Party THU
May 2, 2026 · Artificial Intelligence

Training an 11.5 B‑parameter Universal Interatomic Potential in Hours on Exascale Supercomputers

A Chinese Academy of Sciences team introduced the MatRIS‑MoE model and the Janus training framework, enabling a 11.5 billion‑parameter universal machine‑learning interatomic potential to be trained on two exascale systems at 1.2 EFLOPS, compressing weeks‑long training into a few hours.

AI for ScienceExascale trainingHigh‑performance computing
0 likes · 8 min read
Training an 11.5 B‑parameter Universal Interatomic Potential in Hours on Exascale Supercomputers
Machine Heart
Machine Heart
Apr 25, 2026 · Artificial Intelligence

Open‑Source Models Dominate 21 Scientific Discovery Tasks with SimpleTES

The SimpleTES framework decomposes trial‑and‑error into three scalable dimensions—Concurrency, Length, and Candidates—enabling test‑time scaling that lets open‑source models outperform closed‑source rivals across 21 diverse scientific benchmarks, from LASSO regression to quantum circuit compilation.

AI for ScienceOpen-source modelsScientific Discovery
0 likes · 13 min read
Open‑Source Models Dominate 21 Scientific Discovery Tasks with SimpleTES
AI Explorer
AI Explorer
Mar 2, 2026 · Operations

Huawei Team’s LLM‑Enhanced Algorithm Wins CVRP Challenge, Redefining Optimization Design

A joint Huawei and City University of Hong Kong team combined large language models with evolutionary computation to solve the capacity‑constrained vehicle routing problem, winning the CVRPLib BKS Global Challenge and demonstrating how AI can automate and transform algorithm design, heralding a new paradigm for operations optimization.

AI for ScienceCVRPEvolutionary Algorithms
0 likes · 7 min read
Huawei Team’s LLM‑Enhanced Algorithm Wins CVRP Challenge, Redefining Optimization Design
PaperAgent
PaperAgent
Nov 29, 2025 · Industry Insights

NeurIPS 2025 Insights: AI Agents, Reasoning, and the Shift to Real-World Systems

An analysis of the 5,984 papers accepted at NeurIPS 2025 shows a decisive move from ever‑larger models toward agents, reasoning‑focused LLMs, efficiency engineering, AI for Science, and trustworthy AI, signaling the transition from a research‑toy era to an engineering‑driven AI ecosystem.

AI for ScienceAI trendsLLM
0 likes · 7 min read
NeurIPS 2025 Insights: AI Agents, Reasoning, and the Shift to Real-World Systems
HyperAI Super Neural
HyperAI Super Neural
Nov 3, 2025 · Artificial Intelligence

Demis Hassabis Shifts DeepMind from Pure Research to AI4S, Facing Ethical Tests

The article traces Demis Hassabis’s journey from chess prodigy to DeepMind CEO, detailing the company’s transition from game‑playing breakthroughs like AlphaGo to scientific initiatives such as AlphaFold and AI4S, while examining ethical debates, Nobel‑prize controversy, and calls for global AI safety standards.

AI SafetyAI for ScienceAlphaFold
0 likes · 13 min read
Demis Hassabis Shifts DeepMind from Pure Research to AI4S, Facing Ethical Tests
HyperAI Super Neural
HyperAI Super Neural
Oct 31, 2025 · Industry Insights

Former OpenAI VP and DeepMind Scientist Launch AI‑Powered Science Startup with $300M Funding

Former OpenAI research VP Liam Fedus and DeepMind veteran Ekin Dogus Cubuk founded Periodic Labs to build an AI‑driven scientific platform that combines autonomous robotic labs, high‑fidelity simulations, and LLM assistants, secured $300 million in seed funding, and assembled a team of over 20 elite researchers to accelerate discovery of room‑temperature superconductors and other materials.

AI for ScienceAI startupAutonomous Lab
0 likes · 11 min read
Former OpenAI VP and DeepMind Scientist Launch AI‑Powered Science Startup with $300M Funding
DataFunSummit
DataFunSummit
Sep 14, 2025 · Artificial Intelligence

How AI is Revolutionizing Chemistry and Drug Discovery: From Data to Breakthroughs

This article explores how AI-driven models and data pipelines are transforming the chemistry and pharmaceutical sectors by accelerating drug design, improving protein‑antibody predictions, automating patent data extraction, and outlining future goals for end‑to‑end AI‑enabled scientific discovery.

AI for ScienceChemistry AILarge Language Models
0 likes · 13 min read
How AI is Revolutionizing Chemistry and Drug Discovery: From Data to Breakthroughs
HyperAI Super Neural
HyperAI Super Neural
Nov 28, 2024 · Artificial Intelligence

Why Implementing AI for Science Feels More Rewarding – Insights from Prof. Hong Liang

In an in‑depth interview, Prof. Hong Liang of Shanghai Jiao Tong University discusses the evolution of AI for Science, the challenges of turning research breakthroughs into real‑world protein‑engineering solutions, the importance of industry‑academia collaboration, and how luck, timing, and focused problem definition drive successful AI adoption.

AI for ScienceAlphaFoldIndustry-Academia Collaboration
0 likes · 13 min read
Why Implementing AI for Science Feels More Rewarding – Insights from Prof. Hong Liang
AntTech
AntTech
Sep 7, 2023 · Artificial Intelligence

Scientific AI: Transforming Weather Forecasting and Accelerating Research

The article discusses how AI for Science, exemplified by a 4.5‑billion‑parameter weather model and growing international initiatives, is reshaping scientific research, fostering interdisciplinary collaboration, and driving policy and institutional investments to accelerate innovation across domains.

AI for ScienceBig Modelsresearch innovation
0 likes · 4 min read
Scientific AI: Transforming Weather Forecasting and Accelerating Research
DataFunTalk
DataFunTalk
Jan 27, 2023 · Artificial Intelligence

GNN for Science: Foundations, Applications, and Recent Advances in Equivariant Graph Neural Networks

This article reviews the role of graph neural networks in AI for science, covering background, the evolution of GNN models, applications in physics and biomedicine, recent advances in Euclidean equivariant GNNs, and the authors' own contributions such as GMN and GROVER, concluding with key distinctions between traditional GNNs and science‑focused approaches.

AI for ScienceMolecular Representationequivariant GNN
0 likes · 16 min read
GNN for Science: Foundations, Applications, and Recent Advances in Equivariant Graph Neural Networks