Toward design of novel materials for organic electronics

P Friederich, A Fediai, S Kaiser, M Konrad… - Advanced …, 2019 - Wiley Online Library
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 …

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 …

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 …

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 …

Machine learning for the discovery, design, and engineering of materials

C Duan, A Nandy, HJ Kulik - Annual Review of Chemical and …, 2022 - annualreviews.org
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 …

How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics

B Cao, LA Adutwum, AO Oliynyk, EJ Luber, BC Olsen… - ACS …, 2018 - ACS Publications
Most discoveries in materials science have been made empirically, typically through one-
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 …

[HTML][HTML] Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design

T Zhou, Z Song, K Sundmacher - Engineering, 2019 - Elsevier
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 …

[HTML][HTML] Opportunities and challenges for machine learning in materials science

D Morgan, R Jacobs - Annual Review of Materials Research, 2020 - annualreviews.org
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 …

[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 …