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. " https://developer.nvidia.com/blog/revolutionizing-ai-driven-material-discovery-using-nvidia-alchemi/ 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 https://iopscience.iop.org/article/10.3847/1538-4357/ad865b 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 https://huggingface.co/blog/rubenohana/the-well-collection https://t.co/6XLJA5lJnI https://arxiv.org/abs/2412.10849 https://deepmind.google/discover/blog/discovering-novel-algorithms-with-alphatensor/ quantum neural networks x materials science https://www.nature.com/articles/s41598-024-59276-0 https://techxplore.com/news/2025-01-ai-unveils-strange-chip-functionalities.html https://www.nature.com/articles/s41467-024-54178-1 https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/ https://x.com/emollick/status/1892269913894420743?t=LcmwnYQ7wCK5rP1vWipnHw&s=19 AI research 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://fxtwitter.com/vant_ai/status/1903070297991110657?t=cVfLLmlITL9Xk4ozgO48dw&s=19 https://www.vant.ai/neo-1 ai scientist future house platform https://x.com/SGRodriques/status/1917960862071152811 https://ai4science101.deepmodeling.com/en/latest/chapters/announcement/announcement.html 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 LLM combo (GPT4.1 + o3-mini-high + Gemini 2.0 Flash) delivers superhuman performance by completing 12 work-years of systematic reviews in just 2 days, offering scalable, mass reproducibility across the systematic review literature field Automation of Systematic Reviews with Large Language Models https://www.medrxiv.org/content/10.1101/2025.06.13.25329541v1 https://www.reddit.com/r/singularity/comments/1lb6lel/llm_combo_gpt41_o3minihigh_gemini_20_flash/ Scientists have enlisted generative artificial intelligence to complete the missing data on the distances between pairs of genes in DNA. Generative inpainting of incomplete Euclidean distance matrices of trajectories generated by a fractional Brownian motion https://www.nature.com/articles/s41598-025-97893-5 AI for scientific discovery requires robust neurosymbolic mathematical reasoning with strong out of distribution reasoning and evolutionary divergent novelty search AI x physics intersection is an endless rabbithole. You can try to: - make AI systems model physics better than other AI systems (ferminet, lagrangian neural networks https://arxiv.org/abs/2003.04630 latent space squeezing https://www.youtube.com/watch?v=XRL56YCfKtA ,...), - study existing AI systems using methods from physics (the principles of deep learning theory book https://arxiv.org/abs/2106.10165 statistical physics https://arxiv.org/abs/2506.04374 ,...), - design better AI architectures using insights and methods from physics (liquid neural networks https://arxiv.org/abs/2006.04439 ,...), - advance many AI architectures that are applied physics (diffusion models https://en.wikipedia.org/wiki/Diffusion_model#Non-equilibrium_thermodynamics ,...), - make neurosymbolic systems to do physics algebraically (DreamCoder https://arxiv.org/abs/2006.08381 , formal theorem proving https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/ https://arxiv.org/abs/2405.14333 ,...), - do machine designed physics experiments https://www.youtube.com/watch?v=T_2ZoMNzqHQ - help machine learning in CERN https://atlas.cern/Updates/Feature/Machine-Learning etc. Breaking bonds, breaking ground: Advancing the accuracy of computational chemistry with deep learning https://www.microsoft.com/en-us/research/blog/breaking-bonds-breaking-ground-advancing-the-accuracy-of-computational-chemistry-with-deep-learning/ https://singularityhub.com/2025/06/26/the-dream-of-an-ai-scientist-is-closer-than-ever/ https://fxtwitter.com/sundarpichai/status/1978507110477332582 <https://blog.google/technology/ai/google-gemma-ai-cancer-therapy-discovery/> <https://github.com/vandijklab/cell2sentence> https://github.com/ai-boost/awesome-ai-for-science https://x.com/goodfireai/status/2016563911508840623 https://www.goodfire.ai/research/interpretability-for-alzheimers-detection# https://openai.com/index/new-result-theoretical-physics/ https://deepmind.google/blog/accelerating-mathematical-and-scientific-discovery-with-gemini-deep-think/ Terrence Tao is one of the best geniuses of our age I love his nuanced perspectives on everything, including AI, and how it relates to math and science So much intelligence juice in him https://www.youtube.com/watch?v=Q8Fkpi18QXU