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Keisuke Takahashi
Keisuke Takahashi
E-mailová adresa ověřena na: sci.hokudai.ac.jp - Domovská stránka
Název
Citace
Citace
Rok
High-throughput experimentation and catalyst informatics for oxidative coupling of methane
TN Nguyen, TTP Nhat, K Takimoto, A Thakur, S Nishimura, J Ohyama, ...
Acs Catalysis 10 (2), 921-932, 2019
1612019
Materials informatics: a journey towards material design and synthesis
K Takahashi, Y Tanaka
Dalton Transactions 45 (26), 10497-10499, 2016
1122016
The rise of catalyst informatics: towards catalyst genomics
K Takahashi, L Takahashi, I Miyazato, J Fujima, Y Tanaka, T Uno, ...
ChemCatChem 11 (4), 1146-1152, 2019
972019
Searching for hidden perovskite materials for photovoltaic systems by combining data science and first principle calculations
K Takahashi, L Takahashi, I Miyazato, Y Tanaka
ACS Photonics 5 (3), 771-775, 2018
902018
Unveiling hidden catalysts for the oxidative coupling of methane based on combining machine learning with literature data
K Takahashi, I Miyazato, S Nishimura, J Ohyama
ChemCatChem 10 (15), 3223-3228, 2018
892018
Material synthesis and design from first principle calculations and machine learning
K Takahashi, Y Tanaka
Computational materials science 112, 364-367, 2016
732016
Rapid estimation of activation energy in heterogeneous catalytic reactions via machine learning
K Takahashi, I Miyazato
Journal of computational chemistry 39 (28), 2405-2408, 2018
642018
Graphene decorated with Fe nanoclusters for improving the hydrogen sorption kinetics of MgH 2–experimental and theoretical evidence
MSL Hudson, K Takahashi, A Ramesh, S Awasthi, AK Ghosh, ...
Catalysis Science & Technology 6 (1), 261-268, 2016
602016
Data driven determination of reaction conditions in oxidative coupling of methane via machine learning
J Ohyama, S Nishimura, K Takahashi
ChemCatChem 11 (17), 4307-4313, 2019
552019
Creating machine learning-driven material recipes based on crystal structure
K Takahashi, L Takahashi
The journal of physical chemistry letters 10 (2), 283-288, 2019
512019
Learning catalyst design based on bias-free data set for oxidative coupling of methane
TN Nguyen, S Nakanowatari, TP Nhat Tran, A Thakur, L Takahashi, ...
ACS Catalysis 11 (3), 1797-1809, 2021
492021
2D/1D MoS2/TiO2 Heterostructure Photocatalyst with a Switchable CO2 Reduction Product
A Hezam, K Alkanad, MA Bajiri, J Strunk, K Takahashi, QA Drmosh, ...
Small methods 7 (1), 2201103, 2023
472023
Catalyst Acquisition by Data Science (CADS): a web-based catalyst informatics platform for discovering catalysts
J Fujima, Y Tanaka, I Miyazato, L Takahashi, K Takahashi
Reaction Chemistry & Engineering 5 (5), 903-911, 2020
422020
Reversible Hydrogen Uptake by BN and BC3 Monolayers Functionalized with Small Fe Clusters: A Route to Effective Energy Storage
T Hussain, DJ Searles, K Takahashi
The Journal of Physical Chemistry A 120 (12), 2009-2013, 2016
422016
Catalysts informatics: paradigm shift towards data-driven catalyst design
K Takahashi, J Ohyama, S Nishimura, J Fujima, L Takahashi, T Uno, ...
Chemical Communications 59 (16), 2222-2238, 2023
412023
Accelerating the discovery of hidden two-dimensional magnets using machine learning and first principle calculations
I Miyazato, Y Tanaka, K Takahashi
Journal of Physics: Condensed Matter 30 (6), 06LT01, 2018
402018
Revisiting machine learning predictions for oxidative coupling of methane (OCM) based on literature data
S Nishimura, J Ohyama, T Kinoshita, S Dinh Le, K Takahashi
ChemCatChem 12 (23), 5888-5892, 2020
392020
The value of negative results in data-driven catalysis research
T Taniike, K Takahashi
Nature Catalysis 6 (2), 108-111, 2023
362023
Low temperature pollutant trapping and dissociation over two-dimensional tin
L Takahashi, K Takahashi
Physical Chemistry Chemical Physics 17 (33), 21394-21396, 2015
362015
Multidimensional classification of catalysts in oxidative coupling of methane through machine learning and high-throughput data
K Takahashi, L Takahashi, TN Nguyen, A Thakur, T Taniike
The Journal of Physical Chemistry Letters 11 (16), 6819-6826, 2020
332020
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Články 1–20