Advances in materials are an important contributor to our technological progress,and yet the process of materials discovery and development itself is slow.Our current research process is human-centred,where human rese...Advances in materials are an important contributor to our technological progress,and yet the process of materials discovery and development itself is slow.Our current research process is human-centred,where human researchers design,conduct,analyse and interpret experiments,and then decide what to do next.We have built an Autonomous Research System(ARES)—an autonomous research robot capable of first-of-its-kind closed-loop iterative materials experimentation.ARES exploits advances in autonomous robotics,artificial intelligence,data sciences,and high-throughput and in situ techniques,and is able to design,execute and analyse its own experiments orders of magnitude faster than current research methods.We applied ARES to study the synthesis of singlewalled carbon nanotubes,and show that it successfully learned to grow them at targeted growth rates.ARES has broad implications for the future roles of humans and autonomous research robots,and for human-machine partnering.We believe autonomous research robots like ARES constitute a disruptive advance in our ability to understand and develop complex materials at an unprecedented rate.展开更多
The diameters of single-walled carbon nanotubes(SWCNTs)are directly related to their electronic properties,making diameter control highly desirable for a number of applications.Here we utilized a machine learning plan...The diameters of single-walled carbon nanotubes(SWCNTs)are directly related to their electronic properties,making diameter control highly desirable for a number of applications.Here we utilized a machine learning planner based on the Expected Improvement decision policy that mapped regions where growth was feasible vs.not feasible and further optimized synthesis conditions to selectively grow SWCNTs within a narrow diameter range.We maximized two ranges corresponding to Raman radial breathing mode frequencies around 265 and 225 cm^(−1)(SWCNT diameters around 0.92 and 1.06 nm,respectively),and our planner found optimal synthesis conditions within a hundred experiments.Extensive post-growth characterization showed high selectivity in the optimized growth experiments compared to the unoptimized growth experiments.Remarkably,our planner revealed significantly different synthesis conditions for maximizing the two diameter ranges in spite of their relative closeness.Our study shows the promise for machine learning-driven diameter optimization and paves the way towards chirality-controlled SWCNT growth.展开更多
文摘Advances in materials are an important contributor to our technological progress,and yet the process of materials discovery and development itself is slow.Our current research process is human-centred,where human researchers design,conduct,analyse and interpret experiments,and then decide what to do next.We have built an Autonomous Research System(ARES)—an autonomous research robot capable of first-of-its-kind closed-loop iterative materials experimentation.ARES exploits advances in autonomous robotics,artificial intelligence,data sciences,and high-throughput and in situ techniques,and is able to design,execute and analyse its own experiments orders of magnitude faster than current research methods.We applied ARES to study the synthesis of singlewalled carbon nanotubes,and show that it successfully learned to grow them at targeted growth rates.ARES has broad implications for the future roles of humans and autonomous research robots,and for human-machine partnering.We believe autonomous research robots like ARES constitute a disruptive advance in our ability to understand and develop complex materials at an unprecedented rate.
基金We acknowiedge funding fom the Air Force Office of Sdenmhc Research(LRIR16KCOR322)。
文摘The diameters of single-walled carbon nanotubes(SWCNTs)are directly related to their electronic properties,making diameter control highly desirable for a number of applications.Here we utilized a machine learning planner based on the Expected Improvement decision policy that mapped regions where growth was feasible vs.not feasible and further optimized synthesis conditions to selectively grow SWCNTs within a narrow diameter range.We maximized two ranges corresponding to Raman radial breathing mode frequencies around 265 and 225 cm^(−1)(SWCNT diameters around 0.92 and 1.06 nm,respectively),and our planner found optimal synthesis conditions within a hundred experiments.Extensive post-growth characterization showed high selectivity in the optimized growth experiments compared to the unoptimized growth experiments.Remarkably,our planner revealed significantly different synthesis conditions for maximizing the two diameter ranges in spite of their relative closeness.Our study shows the promise for machine learning-driven diameter optimization and paves the way towards chirality-controlled SWCNT growth.
