In 2026, AI big models are changing the paradigm of scientific research at an unprecedented speed. From DeepMind's AlphaFold series to Meta's ESMFold, AI has achieved experimental level accuracy in protein structure prediction, covering over 98% of known protein sequences. This breakthrough has directly accelerated the process of new drug development - the target discovery stage, which traditionally took 3-5 years, has now been shortened to just a few months.
In the field of materials science, the GNOME model jointly developed by MIT and Google Research can quickly screen out new materials with specific properties from millions of candidate material combinations. The discovery cycle of battery materials, catalysts, and semiconductor materials has been shortened from an average of 10 years to 18 months. In the first half of 2026, three new solid-state electrolyte materials were discovered through AI assistance, which are expected to increase the energy density of lithium batteries by 40%.
The field of physics has also seen breakthroughs. DeepMind's AlphaQ model has achieved unprecedented accuracy in quantum chemistry simulations, accurately predicting the energy of molecular excited states, which is of great significance for the development of photovoltaic materials and photocatalysts. At the same time, the European Organization for Nuclear Research (CERN) is using AI models to accelerate the analysis of data from the Large Hadron Collider, increasing the efficiency of signal filtering in massive experimental data by 10 times.
In addition, AI models in the field of meteorological science, such as the Huawei Pangu Meteorological Model and Google GraphCast, have exceeded the accuracy of traditional numerical forecasting methods in predicting typhoon paths and extreme weather warnings. In 2026, AI driven scientific research is expanding from a single discipline to interdisciplinary integration, and scientists are starting to build "AI scientists" systems to achieve closed-loop automation from hypothesis generation to experimental verification.