Extrapolation performance improvement by quantum chemical calculations for machine-learning-based predictions of flow-synthesized binary copolymers
Shogo Takasuka, Shunto Oikawa, Takayoshi Yoshimura, Shô Itô, Yosuke Harashima, Tomoaki Takayama, Shigehito Asano, Akira Kurosawa, Tetsunori Sugawara, Miho Hatanaka, Tomoyuki Miyao, Takamitsu Matsubara, Yu‐ya Ohnishi, Hiroharu Ajiro, Mikiya Fujii
Abstract
The study utilized machine learning to predict highly accurate polymer properties, mainly when quantum chemical calculation values were included as variables, thus suggesting a promising tool for accelerating polymer development with new monomers.
Topics & Concepts
ExtrapolationCopolymerBinary numberQuantum chemicalPolymerMonomerFlow (mathematics)QuantumComputer scienceMaterials scienceStatistical physicsThermodynamicsApplied mathematicsAlgorithmMathematicsPhysicsMechanicsQuantum mechanicsMoleculeMathematical analysisComposite materialArithmeticMachine Learning in Materials ScienceInnovative Microfluidic and Catalytic Techniques InnovationFuel Cells and Related Materials