Results 161 to 170 of about 5,428,738 (277)

Search for high-capacity oxygen storage materials by materials informatics. [PDF]

RSC Adv, 2019
Ohba N, Yokoya T, Kajita S, Takechi K.
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Modeling the Structure-Property Linkages Between the Microstructure and Thermodynamic Properties of Ceramic Particle-Reinforced Metal Matrix Composites Using a Materials Informatics Approach. [PDF]

Materials (Basel)
Xie R, Li G, Cao P, Tan Z, Wang J.
europepmc   +1 more source

Materials informatics for the screening of multi-principal elements and high-entropy alloys. [PDF]

Nat Commun, 2019
Rickman JM, Chan HM, Harmer MP, Smeltzer JA, Marvel CJ, Roy A, Balasubramanian G.   +6 more
europepmc   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

Partnerships and collaboration drive innovative graduate training in materials informatics. [PDF]

Sci Adv
Slates AM, L McAlexander S, Nolan J, de Pablo J, Chen J, Johnson HT, Brinson LC.   +6 more
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Designing High-Refractive Index Polymers Using Materials Informatics. [PDF]

Polymers (Basel), 2018
Venkatraman V, Alsberg BK.
europepmc   +1 more source

Generative and Experimental Validation of High Refractive Index Polymers via Domain Knowledge Approach with Small Data

Advanced Intelligent Discovery, EarlyView.
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo, Seigo Watanabe, Hana Matsunaga, Kenichi Oyaizu   +3 more
wiley   +1 more source

A materials informatics driven fine-tuning of triazine-based electron-transport layer for organic light-emitting devices. [PDF]

Sci Rep
Sato K, Hattori K, Uehara F, Kitaguni T, Nishiura T, Yamagata T, Nomura K, Matsumoto N, Tanaka T, Aihara H.   +9 more
europepmc   +1 more source