Toward design of novel materials for organic electronics
Materials for organic electronics are presently used in prominent applications, such as
displays in mobile devices, while being intensely researched for other purposes, such as …
displays in mobile devices, while being intensely researched for other purposes, such as …
Integrating computational and experimental workflows for accelerated organic materials discovery
RL Greenaway, KE Jelfs - Advanced Materials, 2021 - Wiley Online Library
Organic materials find application in a range of areas, including optoelectronics, sensing,
encapsulation, molecular separations, and photocatalysis. The discovery of materials is …
encapsulation, molecular separations, and photocatalysis. The discovery of materials is …
Data-driven strategies for accelerated materials design
R Pollice, G dos Passos Gomes… - Accounts of Chemical …, 2021 - ACS Publications
Conspectus The ongoing revolution of the natural sciences by the advent of machine
learning and artificial intelligence sparked significant interest in the material science …
learning and artificial intelligence sparked significant interest in the material science …
A high throughput molecular screening for organic electronics via machine learning: present status and perspective
A Saeki, K Kranthiraja - Japanese Journal of Applied Physics, 2020 - iopscience.iop.org
Organic electronics such as organic field-effect transistors (OFET), organic light-emitting
diodes (OLED), and organic photovoltaics (OPV) have flourished over the last three …
diodes (OLED), and organic photovoltaics (OPV) have flourished over the last three …
Machine learning for the discovery, design, and engineering of materials
Machine learning (ML) has become a part of the fabric of high-throughput screening and
computational discovery of materials. Despite its increasingly central role, challenges …
computational discovery of materials. Despite its increasingly central role, challenges …
How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics
Most discoveries in materials science have been made empirically, typically through one-
variable-at-a-time (Edisonian) experimentation. The characteristics of materials-based …
variable-at-a-time (Edisonian) experimentation. The characteristics of materials-based …
Machine learning in materials science: From explainable predictions to autonomous design
G Pilania - Computational Materials Science, 2021 - Elsevier
The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …
[HTML][HTML] Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design
Materials development has historically been driven by human needs and desires, and this is
likely to continue in the foreseeable future. The global population is expected to reach ten …
likely to continue in the foreseeable future. The global population is expected to reach ten …
Opportunities and challenges for machine learning in materials science
Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …
the discovery of novel materials and the improvement of molecular simulations, with likely …
[HTML][HTML] Machine learning in materials chemistry: An invitation
D Packwood, LTH Nguyen, P Cesana, G Zhang… - Machine Learning with …, 2022 - Elsevier
Materials chemistry is being profoundly influenced by the uptake of machine learning
methodologies. Machine learning techniques, in combination with established techniques …
methodologies. Machine learning techniques, in combination with established techniques …