With the rapid advancement of AI technologies,generative models have been increasingly employed in the exploration of novel materials.By integrating traditional computational approaches such as density functional theo...With the rapid advancement of AI technologies,generative models have been increasingly employed in the exploration of novel materials.By integrating traditional computational approaches such as density functional theory(DFT)and molecular dynamics(MD),existing generative models—including diffusion models and autoregressive models—have demonstrated remarkable potential in the discovery of novel materials.However,their efficiency in goal-directed materials design remains suboptimal.In this work we developed a highly transferable,efficient and robust conditional generation framework,PODGen,by integrating a general generative model with multiple property prediction models.Based on PODGen,we designed a workflow for the high-throughput crystals conditional generation which is used to search new topological insulators(TIs).Our results show that the success rate of generating TIs using our framework is approximately 5 times higher than that of the unconstrained approach.This demonstrates that conditional generation significantly enhances the efficiency of targeted material discovery.Using this method,we generated tens of thousands of new topological materials and conducted further first-principles calculations on those with promising application potential.Furthermore,we identified promising,synthesizable topological(crystalline)insulators such as CsHgSb,NaLaB_(12),Bi_(4)Sb_(2)Se_(3),Be_(3)Ta_(2)Si and Be_(2)W.展开更多
基金supported by the Science Center of the National Natural Science Foundation of China(Grant No.12188101)the National Key Research and Development Program of China(Grant No.2023YFA1607400,2022YFA1403800)+1 种基金the National Natural Science Foundation of China(Grant No.12274436,11921004,11925408)H.W.acknowledge support from the New Cornerstone Science Foundation through the XPLORER PRIZE.The AI-driven experiments,simulations and model training were performed on the robotic AI-Scientist platform of Chinese Academy of Science.
文摘With the rapid advancement of AI technologies,generative models have been increasingly employed in the exploration of novel materials.By integrating traditional computational approaches such as density functional theory(DFT)and molecular dynamics(MD),existing generative models—including diffusion models and autoregressive models—have demonstrated remarkable potential in the discovery of novel materials.However,their efficiency in goal-directed materials design remains suboptimal.In this work we developed a highly transferable,efficient and robust conditional generation framework,PODGen,by integrating a general generative model with multiple property prediction models.Based on PODGen,we designed a workflow for the high-throughput crystals conditional generation which is used to search new topological insulators(TIs).Our results show that the success rate of generating TIs using our framework is approximately 5 times higher than that of the unconstrained approach.This demonstrates that conditional generation significantly enhances the efficiency of targeted material discovery.Using this method,we generated tens of thousands of new topological materials and conducted further first-principles calculations on those with promising application potential.Furthermore,we identified promising,synthesizable topological(crystalline)insulators such as CsHgSb,NaLaB_(12),Bi_(4)Sb_(2)Se_(3),Be_(3)Ta_(2)Si and Be_(2)W.
