Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
Emerging materials intelligence ecosystems propelled by machine learning
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 …
successes and promises, several AI ecosystems are blossoming, many of them within the …
Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods
A Ludwig - NPJ Computational Materials, 2019 - nature.com
This perspective provides an experimentalist's view on materials discovery in multinary
materials systems—from nanoparticles over thin films to bulk—based on combinatorial thin …
materials systems—from nanoparticles over thin films to bulk—based on combinatorial thin …
Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis
Accelerating the experimental cycle for new materials development is vital for addressing
the grand energy challenges of the 21 st century. We fabricate and characterize 75 unique …
the grand energy challenges of the 21 st century. We fabricate and characterize 75 unique …
A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns
Here we report a facile, prompt protocol based on deep-learning techniques to sort out
intricate phase identification and quantification problems in complex multiphase inorganic …
intricate phase identification and quantification problems in complex multiphase inorganic …
Emerging trends in machine learning: a polymer perspective
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 …
intelligence as applied to polymer science. Here, we highlight the unique challenges …
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming
steps in the development cycle of novel thin-film materials. We propose a machine learning …
steps in the development cycle of novel thin-film materials. We propose a machine learning …
Machine learning in nuclear materials research
Nuclear materials are often demanded to function for extended time in extreme
environments, including high radiation fluxes with associated transmutations, high …
environments, including high radiation fluxes with associated transmutations, high …
Machine learning roadmap for perovskite photovoltaics
M Srivastava, JM Howard, T Gong… - The Journal of …, 2021 - ACS Publications
Perovskite solar cells (PSC) are a favorable candidate for next-generation solar systems
with efficiencies comparable to Si photovoltaics, but their long-term stability must be proven …
with efficiencies comparable to Si photovoltaics, but their long-term stability must be proven …
Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach
Determination of crystal system and space group in the initial stages of crystal structure
analysis forms a bottleneck in material science workflow that often requires manual tuning …
analysis forms a bottleneck in material science workflow that often requires manual tuning …