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Self Introduction @ Interdisciplinary Researcher
Computing physics (Complex dynamics system, MultiObjective optimization learning)
Computing chemistry (Graph probability, search algorithm, supervised/unsupervised learning)
Computing Material (Multiscale modeling, Monte carlo, GraphNN)
Interdisciplinary Research:Solve problems whose solutions are beyond the scope of a single discipline or area of research practice.
Learning approach: “Training my own neural network?”
Cognition Science
Physics
Chemistry
Material
Life Science
Human behavior
Math&Computer Science&Philosophy
Magnetic Refrigeration
Satellite
@Chiba University@NIMS, Cooperated with Toshiba, Samsung, LG, NASA, JAXA.
4K
Complex dynamics system, MultiObjective optimization learning(CNN)
Combustion @Mitsubishi Heavy Industry, Cooperated with Georgia Tech, UC berkeley
Graph Probability Learning
Polymer
Theoretical physics + Quantum chemistry + Material information (Multiscale modeling, Monte carlo, GraphNN)
Thermoset resin* (network)
@Tohoku University, visiting University of Washington, cooperated with Tokyo University
“Often these studies are not found out to be inaccurate until there's another real big dataset that someone applies these techniques to and says ‘oh my goodness, the results of these two studies don't overlap‘"
BBC NEWS
Quantum Chemistry Calculation Tool: Gaussian1970s❏ Based on rapidly developing computer technologies❏ Purely from the fundamental laws of physics
John A. Pople
Patterns at multiple spatial scales
https://ajw-group.mit.edu/multiscale-modeling-clays
・SVM (One layer) ・DNN (Multi layers)
Patterns at multiple time scales
Liu, Quan-Xing, et al. "Pattern formation at multiple spatial scales drives the resilience of mussel bed ecosystems." Nature communications 5 (2014): 5234.
August 2009
September 2009
400 days later~
・LSTM
Fundamental scientific challenges
24
❏ Is it possible to develop new materials without understanding underlying physical principles?
❏ Understand physical limitations of different materials and design.
Database +Math +Computer science
Fundamental elements:Electrons, Atoms,Molecules
Physics approach
Machine learning approach
Example: Molecular dynamics + statistical mechanics
Develop new materials