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
Learning Crystallographic Disorder: Bridging Prediction and Experiment in Materials Discovery.
Sign Up to like & getrecommendations! Published in 2025 at "Advanced materials"
DOI: 10.1002/adma.202514226
Abstract: Recent computational materials discovery efforts have led to an enormous number of predictions of previously unknown, potentially stable inorganic, crystalline compounds. In particular, both high-throughput screenings and generative models have benefited tremendously from recent advances… read more here.
Keywords: prediction experiment; materials discovery; crystallographic disorder; disorder ... See more keywords
Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery
Sign Up to like & getrecommendations! Published in 2024 at "Chemistry of Materials"
DOI: 10.1021/acs.chemmater.4c00643
Abstract: The discovery of new crystalline inorganic compounds—novel compositions of matter within known structure types, or even compounds with completely new crystal structures—constitutes an important goal of solid-state and materials chemistry. Some fractions of new compounds… read more here.
Keywords: intelligence driving; chemistry; artificial intelligence; new compounds ... 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
A Quantum Compass for Materials Discovery: Navigating the Combinatorial Explosion
Sign Up to like & getrecommendations! Published in 2025 at "ACS Central Science"
DOI: 10.1021/acscentsci.5c01713
Abstract: A quantum algorithm navigating the immense design space of multivariate porous materials demonstrates a logical and practical roadmap for the future of chemical synthesis. read more here.
Keywords: navigating combinatorial; discovery navigating; materials discovery; quantum compass ... 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
Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation.
Sign Up to like & getrecommendations! Published in 2024 at "Journal of the American Chemical Society"
DOI: 10.1021/jacs.4c03849
Abstract: High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a… read more here.
Keywords: experimental validation; cloud high; computational materials; materials discovery ... 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