Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and...Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and experiments is presented for accelerating the discovery of novel energetic materials.A high-throughput virtual screening(HTVS)system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established.With the proposed HTVS system,candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25112 molecules.Furthermore,a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results,thus verifying the effectiveness of the proposed methodology.This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.展开更多
Data-driven technique is a powerful and efficient tool for guiding materials design,which could supply as an alternative to trial-and-error experiments.In order to accelerate composition design for low-cost rare-earth...Data-driven technique is a powerful and efficient tool for guiding materials design,which could supply as an alternative to trial-and-error experiments.In order to accelerate composition design for low-cost rare-earth permanent magnets,an approach using composition to estimate coercivity（H（cj）） and maximum magnetic energy product（BH）（max） via machine learning has been applied to（PrNd–La–Ce）2Fe（14）B melt-spun magnets.A set of machine learning algorithms are employed to build property prediction models,in which the algorithm of Gradient Boosted Regression Trees is the best for predicting both H（cj） and（BH）（max）,with high accuracies of R^2= 0.88 and 0.89,respectively.Using the best models,predicted datasets of H（cj） or（BH）max in high-dimensional composition space can be constructed.Exploring these virtual datasets could provide efficient guidance for materials design,and facilitate the composition optimization of 2：14：1 structure melt-spun magnets.Combined with magnets＇ cost performance,the candidate cost-effective magnets with targeted properties can also be accurately and rapidly identified.Such data analytics,which involves property prediction and composition design,is of great time-saving and economical significance for the development and application of La Ce-containing melt-spun magnets.展开更多
基金the Science Challenge Project(TZ2018004)the National Natural Science Foundation of China(21875228 and 21702195)for financial support。
文摘Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and experiments is presented for accelerating the discovery of novel energetic materials.A high-throughput virtual screening(HTVS)system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established.With the proposed HTVS system,candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25112 molecules.Furthermore,a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results,thus verifying the effectiveness of the proposed methodology.This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.
基金Project supported by the National Basic Research Program of China(Grant No.2014CB643702)the National Natural Science Foundation of China(Grant No.51590880)+1 种基金the Knowledge Innovation Project of the Chinese Academy of Sciences(Grant No.KJZD-EW-M05)the National Key Research and Development Program of China(Grant No.2016YFB0700903)
文摘Data-driven technique is a powerful and efficient tool for guiding materials design,which could supply as an alternative to trial-and-error experiments.In order to accelerate composition design for low-cost rare-earth permanent magnets,an approach using composition to estimate coercivity（H（cj）） and maximum magnetic energy product（BH）（max） via machine learning has been applied to（PrNd–La–Ce）2Fe（14）B melt-spun magnets.A set of machine learning algorithms are employed to build property prediction models,in which the algorithm of Gradient Boosted Regression Trees is the best for predicting both H（cj） and（BH）（max）,with high accuracies of R^2= 0.88 and 0.89,respectively.Using the best models,predicted datasets of H（cj） or（BH）max in high-dimensional composition space can be constructed.Exploring these virtual datasets could provide efficient guidance for materials design,and facilitate the composition optimization of 2：14：1 structure melt-spun magnets.Combined with magnets＇ cost performance,the candidate cost-effective magnets with targeted properties can also be accurately and rapidly identified.Such data analytics,which involves property prediction and composition design,is of great time-saving and economical significance for the development and application of La Ce-containing melt-spun magnets.
