FAIR data enabling new horizons for materials research

M Scheffler, M Aeschlimann, M Albrecht, T Bereau… - Nature, 2022 - nature.com
The prosperity and lifestyle of our society are very much governed by achievements in
condensed matter physics, chemistry and materials science, because new products for …

A systematic review of machine learning methods applied to fuel cells in performance evaluation, durability prediction, and application monitoring

W Ming, P Sun, Z Zhang, W Qiu, J Du, X Li… - International Journal of …, 2023 - Elsevier
A fuel cell is a power generation device that directly converts chemical energy into electrical
energy through chemical reactions; fuel cells are widely used in aerospace, electric vehicle …

Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors

LT Zhu, XZ Chen, B Ouyang, WC Yan… - Industrial & …, 2022 - ACS Publications
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Unsupervised learning methods for molecular simulation data

A Glielmo, BE Husic, A Rodriguez, C Clementi… - Chemical …, 2021 - ACS Publications
Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Molecular characterization of polymer networks

SPO Danielsen, HK Beech, S Wang… - Chemical …, 2021 - ACS Publications
Polymer networks are complex systems consisting of molecular components. Whereas the
properties of the individual components are typically well understood by most chemists …

Emerging trends in machine learning: a polymer perspective

TB Martin, DJ Audus - ACS Polymers Au, 2023 - ACS Publications
In the last five years, there has been tremendous growth in machine learning and artificial
intelligence as applied to polymer science. Here, we highlight the unique challenges …

[HTML][HTML] Unleashing the power of artificial intelligence in materials design

S Badini, S Regondi, R Pugliese - Materials, 2023 - mdpi.com
The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing
the field of materials engineering thanks to their power to predict material properties, design …

Emerging materials intelligence ecosystems propelled by machine learning

R Batra, L Song, R Ramprasad - Nature Reviews Materials, 2021 - nature.com
The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …

[HTML][HTML] Machine-learning predictions of polymer properties with Polymer Genome

H Doan Tran, C Kim, L Chen… - Journal of Applied …, 2020 - pubs.aip.org
Polymer Genome is a web-based machine-learning capability to perform near-
instantaneous predictions of a variety of polymer properties. The prediction models are …

Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

M Reis, F Gusev, NG Taylor, SH Chung… - Journal of the …, 2021 - ACS Publications
Modern polymer science suffers from the curse of multidimensionality. The large chemical
space imposed by including combinations of monomers into a statistical copolymer …