Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Yee‐Fun Lim"'
Autor:
Andre K. Y. Low, Flore Mekki-Berrada, Abhishek Gupta, Aleksandr Ostudin, Jiaxun Xie, Eleonore Vissol-Gaudin, Yee-Fun Lim, Qianxiao Li, Yew Soon Ong, Saif A. Khan, Kedar Hippalgaonkar
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-11 (2024)
Abstract The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental
Externí odkaz:
https://doaj.org/article/0fb91db8811d4d46a6d55d2c7d7fdeed
Autor:
Yonghao Xiao, Khokan Choudhuri, Adisak Thanetchaiyakup, Wei Xin Chan, Xinwen Hu, Mansour Sadek, Ying Hern Tam, Ryan Guanying Loh, Sharifah Nadhirah Binte Shaik Mohammed, Kendric Jian Ying Lim, Ju Zheng Ten, Felipe Garcia, Vijila Chellappan, Tej S. Choksi, Yee‐Fun Lim, Han Sen Soo
Publikováno v:
Advanced Science, Vol 11, Iss 29, Pp n/a-n/a (2024)
Abstract Lead‐free metal halide perovskites can potentially be air‐ and water‐stable photocatalysts for organic synthesis, but there are limited studies on them for this application. Separately, machine learning (ML), a critical subfield of art
Externí odkaz:
https://doaj.org/article/060bff8f6bca4f3aaaa29d37700f403a
Autor:
Shakti P. Padhy, Varun Chaudhary, Yee-Fun Lim, Ruiming Zhu, Muang Thway, Kedar Hippalgaonkar, Raju V. Ramanujan
Publikováno v:
iScience, Vol 27, Iss 5, Pp 109723- (2024)
Summary: This study presents a machine learning (ML) framework aimed at accelerating the discovery of multi-property optimized Fe-Ni-Co alloys, addressing the time-consuming, expensive, and inefficient nature of traditional methods of material discov
Externí odkaz:
https://doaj.org/article/f92cef09f5714bfe9b0d70727d955d3a
Autor:
Carina Yi Jing Lim, Meltem Yilmaz, Juan Manuel Arce-Ramos, Albertus D. Handoko, Wei Jie Teh, Yuangang Zheng, Zi Hui Jonathan Khoo, Ming Lin, Mark Isaacs, Teck Lip Dexter Tam, Yang Bai, Chee Koon Ng, Boon Siang Yeo, Gopinathan Sankar, Ivan P. Parkin, Kedar Hippalgaonkar, Michael B. Sullivan, Jia Zhang, Yee-Fun Lim
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-11 (2023)
Abstract Intensive research in electrochemical CO2 reduction reaction has resulted in the discovery of numerous high-performance catalysts selective to multi-carbon products, with most of these catalysts still being purely transition metal based. Her
Externí odkaz:
https://doaj.org/article/2c718d61d24d45b8abee949e2f302094
Autor:
Carina Yi Jing Lim, Meltem Yilmaz, Juan Manuel Arce-Ramos, Albertus D. Handoko, Wei Jie Teh, Yuangang Zheng, Zi Hui Jonathan Khoo, Ming Lin, Mark Isaacs, Teck Lip Dexter Tam, Yang Bai, Chee Koon Ng, Boon Siang Yeo, Gopinathan Sankar, Ivan P. Parkin, Kedar Hippalgaonkar, Michael B. Sullivan, Jia Zhang, Yee-Fun Lim
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-1 (2023)
Externí odkaz:
https://doaj.org/article/0dc2c85a2fcc49108c18f2dbb71c86b5
Publikováno v:
Advanced Intelligent Systems, Vol 3, Iss 11, Pp n/a-n/a (2021)
Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of experimental parameters in automated active learning driven high throughput experiments in materials science and chemistry. Previous studies suggest that o
Externí odkaz:
https://doaj.org/article/7b396f6d97e843f0b445409a937ba3a1
Autor:
Chit Siong Lau, Jing Yee Chee, Dickson Thian, Hiroyo Kawai, Jie Deng, Swee Liang Wong, Zi En Ooi, Yee-Fun Lim, Kuan Eng Johnson Goh
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-2 (2021)
Externí odkaz:
https://doaj.org/article/f844dd18ca694d869d9168987864c213
Autor:
Roozbeh Siavash Moakhar, Somayeh Gholipour, Saeid Masudy‐Panah, Ashkan Seza, Ali Mehdikhani, Nastaran Riahi‐Noori, Saeede Tafazoli, Nazanin Timasi, Yee‐Fun Lim, Michael Saliba
Publikováno v:
Advanced Science, Vol 7, Iss 13, Pp n/a-n/a (2020)
Abstract Perovskite solar cells (PSCs) have emerged recently as promising candidates for next generation photovoltaics and have reached power conversion efficiencies of 25.2%. Among the various methods to advance solar cell technologies, the implemen
Externí odkaz:
https://doaj.org/article/e027d93174934592962347d5d82c50b0
With advancements in automated experimental setups, material optimisation and discovery can scale to higher throughput with larger evaluation budgets. Two state-of-the-art algorithms with conceptually different multi-objective optimisation strategies
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ebeed658d3ed3027bfe2255565d17cd5
https://doi.org/10.36227/techrxiv.21154537.v1
https://doi.org/10.36227/techrxiv.21154537.v1
Autor:
Chang Jie Leong, Kai Yuan Andre Low, Jose Recatala-Gomez, Pablo Quijano Velasco, Eleonore Vissol-Gaudin, Jin Da Tan, Balamurugan Ramalingam, Riko I Made, Shreyas Dinesh Pethe, Saumya Sebastian, Yee-Fun Lim, Zi Hui Jonathan Khoo, Yang Bai, Jayce Jian Wei Cheng, Kedar Hippalgaonkar
Progress in data-driven self-driving laboratories for solving materials grand challenges has accelerated with the advent of machine learning, robotics, and automation, but they are usually designed with specific materials and processes in mind. To de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a50c32598d051421e17566edcd29cc2
https://doi.org/10.26434/chemrxiv-2022-prrfh
https://doi.org/10.26434/chemrxiv-2022-prrfh