Autonomous high-throughput combinatorial experimentation is a key approach for accelerating materials discovery.However,achieving a fully closed-loop system remains a challenge due to the lack of effective optimizatio...Autonomous high-throughput combinatorial experimentation is a key approach for accelerating materials discovery.However,achieving a fully closed-loop system remains a challenge due to the lack of effective optimization strategies for combinatorial experimentation.Here,we developed a Bayesian optimization method specifically designed for composition-spread films,enabling the selection of promising composition-spread films and identifying which elements should be compositionally graded.Using this approach,we demonstrated an autonomous closed-loop exploration of composition-spread films to enhance the anomalous Hall effect(AHE).Our method optimized the composition of a five-element alloy system consisting of three 3d ferromagnetic elements of Fe,Co,and Ni and two 5d heavy elements from Ta,W,or Ir to maximize the AHE.Through our autonomous exploration,we achieved a maximum anomalous Hall resistivity of 10.9μΩcm in Fe_(44.9)Co_(27.9)Ni_(12.1)Ta_(3.3)Ir_(11.7)amorphous thin film on thermally oxidized Si substrates deposited at room temperature.展开更多
基金supported by JST CREST (Grant No. JPMJCR21O1)JST ERATO “Magnetic Thermal Management Materials Project” (Grant No. JPMJER2201)+3 种基金JST PRESTO (Grant No. JPMJPR24T8)MEXT Program: Data Creation and Utilization-Type Material Research and Development Project (Digital Transformation Initiative Center for Magnetic Materials, Grant No. JPMXP1122715503Digital Transformation Initiative for Green Energy Materials, Grant No. JPMXP1121467561)JSPS KAKENHI Grants-in-Aid for Scientific Research (B) (Grant No. JP21H01608).
文摘Autonomous high-throughput combinatorial experimentation is a key approach for accelerating materials discovery.However,achieving a fully closed-loop system remains a challenge due to the lack of effective optimization strategies for combinatorial experimentation.Here,we developed a Bayesian optimization method specifically designed for composition-spread films,enabling the selection of promising composition-spread films and identifying which elements should be compositionally graded.Using this approach,we demonstrated an autonomous closed-loop exploration of composition-spread films to enhance the anomalous Hall effect(AHE).Our method optimized the composition of a five-element alloy system consisting of three 3d ferromagnetic elements of Fe,Co,and Ni and two 5d heavy elements from Ta,W,or Ir to maximize the AHE.Through our autonomous exploration,we achieved a maximum anomalous Hall resistivity of 10.9μΩcm in Fe_(44.9)Co_(27.9)Ni_(12.1)Ta_(3.3)Ir_(11.7)amorphous thin film on thermally oxidized Si substrates deposited at room temperature.
