materials science
PulseAugur coverage of materials science — every cluster mentioning materials science across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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Materials Science Milestone Paves Way for Next-Gen Tech
A significant milestone has been achieved in materials science and engineering, paving the way for the next generation of technology. This advancement is expected to accelerate the mass production of materials crucial f…
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Diffusion Model Predicts Crystal Structures from X-Ray Diffraction Data
Researchers have developed XRDiff, a novel diffusion model capable of predicting crystal structures from powder X-ray diffraction (PXRD) data. This model can infer structures based on known stoichiometry or, more challe…
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AI framework improves defect classification in materials science imaging
Researchers have developed a context-aware deep learning framework to improve defect classification in atomic-resolution STEM imaging. This new approach integrates image contrast with metadata such as composition and be…
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AI approach predicts properties of stacked bilayer materials
Researchers have developed a new multimodal learning approach to predict properties of stacked bilayer materials, aiming to accelerate discovery in materials science. This method addresses the underexplored area of usin…
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AI method enhances inorganic material discovery using crystal symmetry
Researchers have developed a novel padding method to improve the AI-driven generation of inorganic materials. This technique leverages crystal symmetry information to create more robust and informed representations of c…
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New framework enables AI agents to conduct atomistic research
Researchers have developed AtomisticSkills, an open-source framework designed to enable AI coding agents to perform complex atomistic research across materials science, chemistry, and drug discovery. This framework orga…
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Transformers accurately predict atomistic transitions in materials science
Researchers have developed a novel application of transformer models to predict atomistic transitions in materials, a process critical for material science but computationally intensive with traditional methods. This ma…