Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery.
Sign Up to like & getrecommendations! Published in 2021 at "Advanced materials"
DOI: 10.1002/adma.202004831
Abstract: Organic materials find application in a range of areas, including optoelectronics, sensing, encapsulation, molecular separations, and photocatalysis. The discovery of materials is frustratingly slow however, particularly when contrasted to the vast chemical space of possibilities… read more here.
Keywords: organic materials; experimental workflows; workflows accelerated; materials discovery ... See more keywords
Evolution of Dip-Pen Nanolithography (DPN): From Molecular Patterning to Materials Discovery.
Sign Up to like & getrecommendations! Published in 2020 at "Chemical reviews"
DOI: 10.1021/acs.chemrev.9b00725
Abstract: Dip-pen nanolithography (DPN) is a nanofabrication technique that can be used to directly write molecular patterns on substrates with high resolution and registration. Over the past two decades, DPN has evolved in its ability to… read more here.
Keywords: pen nanolithography; dip pen; dpn; nanolithography dpn ... See more keywords
Dynamic Workflows for Routine Materials Discovery in Surface Science
Sign Up to like & getrecommendations! Published in 2018 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.8b00386
Abstract: The rising application of informatics and data science tools for studying inorganic crystals and small molecules has revolutionized approaches to materials discovery and driven the development of accurate machine learning structure/property relationships. We discuss how… read more here.
Keywords: dynamic workflows; workflows routine; science; surface science ... See more keywords
Into the Unknown: How Computation Can Help Explore Uncharted Material Space
Sign Up to like & getrecommendations! Published in 2022 at "Journal of the American Chemical Society"
DOI: 10.1021/jacs.2c06833
Abstract: Novel functional materials are urgently needed to help combat the major global challenges facing humanity, such as climate change and resource scarcity. Yet, the traditional experimental materials discovery process is slow and the material space… read more here.
Keywords: computation; materials discovery; space; material space ... See more keywords
Topological materials discovery using electron filling constraints
Sign Up to like & getrecommendations! Published in 2018 at "Nature Physics"
DOI: 10.1038/nphys4277
Abstract: Electron filling criterion can guide the search for new topological materials with nodal-point or nodal-line Fermi surfaces. read more here.
Keywords: topological materials; electron filling; using electron; discovery using ... See more keywords
Bringing nuclear materials discovery and qualification into the 21st century
Sign Up to like & getrecommendations! Published in 2020 at "Nature Communications"
DOI: 10.1038/s41467-020-16406-2
Abstract: Time horizons for nuclear materials development and qualification must be shortened to realize future nuclear energy concepts. Inspired by the Materials Genome Initiative, we present an integrated approach to materials discovery and qualification to insert… read more here.
Keywords: qualification 21st; qualification; nuclear materials; bringing nuclear ... See more keywords
Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings
Sign Up to like & getrecommendations! Published in 2022 at "Nature Communications"
DOI: 10.1038/s41467-022-28543-x
Abstract: Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS)… read more here.
Keywords: density; contrastive learning; spectral properties; prediction ... See more keywords
A high-throughput data analysis and materials discovery tool for strongly correlated materials
Sign Up to like & getrecommendations! Published in 2018 at "npj Computational Materials"
DOI: 10.1038/s41524-018-0120-9
Abstract: Modeling of f-electron systems is challenging due to the complex interplay of the effects of spin–orbit coupling, electron–electron interactions, and the hybridization of the localized f-electrons with itinerant conduction electrons. This complexity drives not only… read more here.
Keywords: analysis; database; structure; strongly correlated ... See more keywords
Machine-learned metrics for predicting the likelihood of success in materials discovery
Sign Up to like & getrecommendations! Published in 2020 at "npj Computational Materials"
DOI: 10.1038/s41524-020-00401-8
Abstract: Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials with machine learning (ML), which amounts to… read more here.
Keywords: machine; machine learned; likelihood; materials discovery ... See more keywords
Benchmarking the acceleration of materials discovery by sequential learning†
Sign Up to like & getrecommendations! Published in 2020 at "Chemical Science"
DOI: 10.1039/c9sc05999g
Abstract: Sequential learning (SL) strategies, i.e. iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have… read more here.
Keywords: research; performance; benchmarking acceleration; sequential learning ... See more keywords
Metastable materials discovery in the age of large-scale computation
Sign Up to like & getrecommendations! Published in 2021 at "Applied physics reviews"
DOI: 10.1063/5.0049453
Abstract: Computational materials discovery has been successful in predicting novel, technologically relevant materials. However, it has remained focused almost exclusively on finding ground-state structures. Now that the lower-hanging fruit has been found in many fields of… read more here.
Keywords: large scale; age large; scale computation; discovery age ... See more keywords