Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery.In this work,we develop a deep learning synthesizability model(SynthNN)...Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery.In this work,we develop a deep learning synthesizability model(SynthNN)that leverages the entire space of synthesized inorganic chemical compositions.By reformulating material discovery as a synthesizability classification task,SynthNN identifies synthesizable materials with 7×higher precision than with DFT-calculated formation energies.In a head-to-head material discovery comparison against 20 expert material scientists,SynthNN outperforms all experts,achieves 1.5×higher precision and completes the task five orders of magnitude faster than the best human expert.Remarkably,without any prior chemical knowledge,our experiments indicate that SynthNN learns the chemical principles of charge-balancing,chemical family relationships and ionicity,and utilizes these principles to generate synthesizability predictions.The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.展开更多
This paper presents a review on the recent research and technical progress of electric motor systems and electric powertrains for new energy vehicles.Through the analysis and comparison of direct current motor,inducti...This paper presents a review on the recent research and technical progress of electric motor systems and electric powertrains for new energy vehicles.Through the analysis and comparison of direct current motor,induction motor,and synchronous motor,it is found that permanent magnet synchronous motor has better overall performance;by comparison with converters with Si-based IGBTs,it is found converters with SiC MOSFETs show significantly higher efficiency and increase driving mileage per charge.In addition,the pros and cons of different control strategies and algorithms are demonstrated.Next,by comparing series,parallel,and power split hybrid powertrains,the series-parallel compound hybrid powertrains are found to provide better fuel economy.Different electric powertrains,hybrid powertrains,and range-extended electric systems are also detailed,and their advantages and disadvantages are described.Finally,the technology roadmap over the next 15 years is proposed regarding traction motor,power electronic converter and electric powertrain as well as the key materials and components at each time frame.展开更多
基金This work was performed under the auspices of the U.S.Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.We would like to thank Prof.Tony Heinz for the original project inspiration and the human participants of the Synthesizability Quiz.
文摘Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery.In this work,we develop a deep learning synthesizability model(SynthNN)that leverages the entire space of synthesized inorganic chemical compositions.By reformulating material discovery as a synthesizability classification task,SynthNN identifies synthesizable materials with 7×higher precision than with DFT-calculated formation energies.In a head-to-head material discovery comparison against 20 expert material scientists,SynthNN outperforms all experts,achieves 1.5×higher precision and completes the task five orders of magnitude faster than the best human expert.Remarkably,without any prior chemical knowledge,our experiments indicate that SynthNN learns the chemical principles of charge-balancing,chemical family relationships and ionicity,and utilizes these principles to generate synthesizability predictions.The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.
文摘This paper presents a review on the recent research and technical progress of electric motor systems and electric powertrains for new energy vehicles.Through the analysis and comparison of direct current motor,induction motor,and synchronous motor,it is found that permanent magnet synchronous motor has better overall performance;by comparison with converters with Si-based IGBTs,it is found converters with SiC MOSFETs show significantly higher efficiency and increase driving mileage per charge.In addition,the pros and cons of different control strategies and algorithms are demonstrated.Next,by comparing series,parallel,and power split hybrid powertrains,the series-parallel compound hybrid powertrains are found to provide better fuel economy.Different electric powertrains,hybrid powertrains,and range-extended electric systems are also detailed,and their advantages and disadvantages are described.Finally,the technology roadmap over the next 15 years is proposed regarding traction motor,power electronic converter and electric powertrain as well as the key materials and components at each time frame.
