136 citations · 364 across the 12 of their papers we have counts for
12 papers
Shotgun crystal structure prediction using machine-learned formation energies
Chang Liu, Hiromasa Tamaki, Tomoyasu Yokoyama +5
Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configuration…
Transfer learning with affine model transformation
Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi +1
Supervised transfer learning has received considerable attention due to its potential to boost the predictive power of machine learning in scenarios where data are scarce. Generall…
RadonPy: Automated Physical Property Calculation using All-atom Classical Molecular Dynamics Simulations for Polymer Informatics
Yoshihiro Hayashi, Junichiro Shiomi, Junko Morikawa +1
The rapid growth of data-driven materials research has made it necessary to develop systematically designed, open databases of material properties. However, there are few open data…
Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks
Qi Zhang, Chang Liu, Stephen Wu +1
In the last few years, de novo molecular design using machine learning has made great technical progress but its practical deployment has not been as successful. This is mostly owi…
Crystal structure prediction with machine learning-based element substitution
Minoru Kusaba, Chang Liu, Ryo Yoshida
The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of…
Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach
Pol Torres, Stephen Wu, Shenghong Ju +4
Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The h…
Potentials and challenges of polymer informatics: exploiting machine learning for polymer design
Stephen Wu, Hironao Yamada, Yoshihiro Hayashi +2
There has been rapidly growing demand of polymeric materials coming from different aspects of modern life because of the highly diverse physical and chemical properties of polymers…
A General Class of Transfer Learning Regression without Implementation Cost
Shunya Minami, Song Liu, Stephen Wu +2
We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task,…
A Bayesian algorithm for retrosynthesis
Zhongliang Guo, Stephen Wu, Mitsuru Ohno +1
The identification of synthetic routes that end with a desired product has been an inherently time-consuming process that is largely dependent on expert knowledge regarding a limit…
Recreation of the Periodic Table with an Unsupervised Machine Learning Algorithm
Minoru Kusaba, Chang Liu, Yukinori Koyama +2
In 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev. In terms of data science, his achievement can be viewed as a successful example of…
Exploring diamond-like lattice thermal conductivity crystals via feature-based transfer learning
Shenghong Ju, Ryo Yoshida, Chang Liu +3
Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using mac…
SPF-CellTracker: Tracking multiple cells with strongly-correlated moves using a spatial particle filter
Osamu Hirose, Shotaro Kawaguchi, Terumasa Tokunaga +6
Tracking many cells in time-lapse 3D image sequences is an important challenging task of bioimage informatics. Motivated by a study of brain-wide 4D imaging of neural activity in C…