AI x chemistry Revolutionizing AI-Driven Material And Chemical Discovery Using NVIDIA ALCHEMI "Using the NVIDIA Batched Geometry Relaxation NIM resulted in a 800x acceleration in MLIP calculations. This acceleration, close to three orders of magnitude, opens the door to high-throughput simulations of millions of candidates, enabling next-generation foundation models trained with high-quality data and improving downstream property prediction capabilities. It also enables simulation of more complex and realistic systems, unlocking new chemistries and applications. " [Revolutionizing AI-Driven Material Discovery Using NVIDIA ALCHEMI | NVIDIA Technical Blog](https://developer.nvidia.com/blog/revolutionizing-ai-driven-material-discovery-using-nvidia-alchemi/) [How Google AI is advancing science](https://blog.google/technology/ai/google-ai-big-scientific-breakthroughs-2024/) 9 ways AI is advancing science 1. Cracking the 50-year “grand challenge” of protein structure prediction 2. Showing the human brain in unprecedented detail, to support health research 3. Saving lives with accurate flood forecasting 4. Spotting wildfires earlier to help firefighters stop them faster 5. Predicting weather faster and with more accuracy 6. Advancing the frontier of mathematical reasoning 7. Using quantum computing to accurately predict chemical reactivity and kinetics 8. Accelerating materials science and the potential for more sustainable solar cells, batteries and superconductors 9. Taking a meaningful step toward nuclear fusion — and abundant clean energy https://phys.org/news/2024-11-ai-astronomy-neural-networks-simulate.html [Radware Bot Manager Captcha](https://iopscience.iop.org/article/10.3847/1538-4357/ad865b) [Large language models surpass human experts in predicting neuroscience results | Nature Human Behaviour](https://www.nature.com/articles/s41562-024-02046-9) The Well: 16 datasets (15TB) for Machine Learning, from astrophysics to fluid dynamics and biology. https://x.com/oharub/status/1863616236497633596?t=_WEh81jMGZqxXP5rPDc8Jw&s=19 [Bridging the Gap Between Physical Numerical Simulations and Machine Learning: Introducing The Well](https://huggingface.co/blog/rubenohana/the-well-collection) [https://openreview.net/pdf?id=00Sx577BT3](https://t.co/6XLJA5lJnI) [[2412.10849] Superhuman performance of a large language model on the reasoning tasks of a physician](https://arxiv.org/abs/2412.10849) [Discovering novel algorithms with AlphaTensor - Google DeepMind](https://deepmind.google/discover/blog/discovering-novel-algorithms-with-alphatensor/) quantum neural networks x materials science [Practical application of quantum neural network to materials informatics | Scientific Reports](https://www.nature.com/articles/s41598-024-59276-0) https://techxplore.com/news/2025-01-ai-unveils-strange-chip-functionalities.html [Deep-learning enabled generalized inverse design of multi-port radio-frequency and sub-terahertz passives and integrated circuits | Nature Communications](https://www.nature.com/articles/s41467-024-54178-1) [Accelerating scientific breakthroughs with an AI co-scientist](https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/) https://x.com/emollick/status/1892269913894420743?t=LcmwnYQ7wCK5rP1vWipnHw&s=19 AI research [The AI Scientist Generates its First Peer-Reviewed Scientific Publication](https://sakana.ai/ai-scientist-first-publication/) "However, real scientific breakthroughs will come not from answering known questions, but from asking challenging new questions and questioning common conceptions and previous ideas. We're currently building very obedient students, not revolutionaries. This is perfect for today’s main goal in the field of creating great assistants and overly compliant helpers. But until we find a way to incentivize them to question their knowledge and propose ideas that potentially go against past training data, they won't give us scientific revolutions yet. If we want scientific breakthroughs, we should probably explore how we’re currently measuring the performance of AI models and move to a measure of knowledge and reasoning able to test if scientific AI models can for instance: Challenge their own training data knowledge Take bold counterfactual approaches Make general proposals based on tiny hints Ask non-obvious questions that lead to new research paths We don't need an A+ student who can answer every question with general knowledge. We need a B student who sees and questions what everyone else missed. https://x.com/Thom_Wolf/status/1897630495527104932 " Physicist Mario Krenn uses artificial intelligence to inspire and accelerate scientific progress. He runs the Artificial Scientist Lab at the Max Planck Institute for the Science of Light, where he develops machine-learning algorithms that discover new experimental techniques at the frontiers of physics and microscopy. He also develops algorithms that predict and suggest personalized research questions and ideas. [https://www.youtube.com/watch?v=T_2ZoMNzqHQ](https://www.youtube.com/watch?v=T_2ZoMNzqHQ) https://fxtwitter.com/vant_ai/status/1903070297991110657?t=cVfLLmlITL9Xk4ozgO48dw&s=19 [https://www.vant.ai/neo-1](https://www.vant.ai/neo-1) ai scientist future house platform https://x.com/SGRodriques/status/1917960862071152811 [Announcing AI for Science Blog Series — AI4Science101 documentation](https://ai4science101.deepmodeling.com/en/latest/chapters/announcement/announcement.html) [GitHub - deepmodeling/AI4Science101: AI for Science](https://github.com/deepmodeling/AI4Science101) this is apparently ai generated and accepted paper https://x.com/Zochi_AS/status/1927767904742736039 "The 1st fully AI-generated scientific discovery to pass the highest level of peer review – the main track of an A* conference (ACL 2025)." https://x.com/IntologyAI/status/1927770849181864110