DAMASK – The Düsseldorf Advanced Material Simulation Kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale - ScienceDirect
https://www.sciencedirect.com/science/article/pii/S0927025618302714
https://www.sciencedirect.com/science/article/pii/S0927025618302714
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Scientists Discover New Type of Crystal | Sci.News
https://www.sci.news/othersciences/physicalchemistry/zangenite-13870.html
https://www.sci.news/othersciences/physicalchemistry/zangenite-13870.html
Sci.News
Scientists Discover New Type of Crystal
In exploring how crystals form, researchers at New York University came across an unusual, rod-shaped crystal that hadn’t been identified before.
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A new molecular model of bilayer graphene with higher semiconducting properties
https://phys.org/news/2025-05-molecular-bilayer-graphene-higher-semiconducting.html
https://phys.org/news/2025-05-molecular-bilayer-graphene-higher-semiconducting.html
phys.org
A new molecular model of bilayer graphene with higher semiconducting properties
Juan Casado Cordón, Professor of Physical Chemistry at the University of Malaga, considers graphene—an infinite layer of carbon atoms—as one of the greatest discoveries of the last 20 years due to ...
Forwarded from Umedjon Khalilov
Anharmonic effects control interaction of carbyne confined in carbon nanotubes shaping their vibrational properties | Nature Communications
https://www.nature.com/articles/s41467-025-59863-3
https://www.nature.com/articles/s41467-025-59863-3
Nature
Anharmonic effects control interaction of carbyne confined in carbon nanotubes shaping their vibrational properties
Nature Communications - Carbynes are bulk linear chains of carbon atoms that can be stabilized inside carbon nanotubes. Here the authors show that while electronic states of tube and chain are...
Data-Driven Discovery of Water-Stable Metal–Organic Frameworks with High Water Uptake Capacity | ACS Applied Materials & Interfaces
https://pubs.acs.org/doi/10.1021/acsami.5c09320
https://pubs.acs.org/doi/10.1021/acsami.5c09320
ACS Publications
Data-Driven Discovery of Water-Stable Metal–Organic Frameworks with High Water Uptake Capacity
Metal–organic frameworks (MOFs) are promising candidate materials for applications that would benefit from precise chemical patterning, such as desalination, but many MOFs suffer from poor stability in water. In addition to water stability, high water uptake…
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Modeling electric response of materials, a million atoms at a time
https://phys.org/news/2025-06-electric-response-materials-million-atoms.html
https://phys.org/news/2025-06-electric-response-materials-million-atoms.html
phys.org
Modeling electric response of materials, a million atoms at a time
Researchers in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a machine learning framework that can predict with quantum-level accuracy how materials respond ...
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New two-dimensional carbon material is 8x stronger than graphene - Earth.com
https://www.earth.com/news/scientists-create-a-two-dimensional-carbon-material-eight-times-stronger-than-graphene/
https://www.earth.com/news/scientists-create-a-two-dimensional-carbon-material-eight-times-stronger-than-graphene/
Earth.com
New two-dimensional carbon material is 8x stronger than graphene - Earth.com
Researchers have created a new ultra-strong carbon called MAC, eight times stronger than graphene, opening the door to durable technologies.
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First quantum-mechanical model of quasicrystals reveals why they exist
https://phys.org/news/2025-06-quantum-mechanical-quasicrystals-reveals.html
https://phys.org/news/2025-06-quantum-mechanical-quasicrystals-reveals.html
phys.org
First quantum-mechanical model of quasicrystals reveals why they exist
A rare and bewildering intermediate between crystal and glass can be the most stable arrangement for some combinations of atoms, according to a study from the University of Michigan.
Forwarded from Umedjon Khalilov
Machine-learning design of ductile FeNiCoAlTa alloys with high strength | Nature
https://www.nature.com/articles/s41586-025-09160-2
https://www.nature.com/articles/s41586-025-09160-2
Nature
Machine-learning design of ductile FeNiCoAlTa alloys with high strength
Nature - A new group of multi-principal-element alloys, designed through machine learning and extreme microstructural heterogeneities, achieve high strength (1.8-GPa yield strength) and ductility...
Large language models to accelerate organic chemistry synthesis | Nature Machine Intelligence https://share.google/A8QHA8KQhfpTn858b
Nature
Large language models to accelerate organic chemistry synthesis
Nature Machine Intelligence - Large language models (LLMs) can be useful tools for science, but they often lack expert understanding of complex domains that they were not trained on. Zhang and...
Accelerated data-driven materials science with the Materials Project | Nature Materials https://share.google/vkmplzgZS5XzigHnC
Nature
Accelerated data-driven materials science with the Materials Project
Nature Materials - Materials design and informatics have become increasingly prominent over the past several decades. Using the Materials Project as an example, this Perspective discusses how...
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Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors | Nature Communications https://share.google/LEYU30I4MDKiyxWjL
Nature
Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors
Nature Communications - Here, the authors report a machine-learning-based high-throughput prediction framework to identify materials with strong extreme ultraviolet (EUV) photoresponse and...
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Gated CO2 permeation across dynamic graphene pores | Nature Communications https://share.google/9pfNvrYSuuH3MbBFv
Nature
Gated CO2 permeation across dynamic graphene pores
Nature Communications - The true potential of graphene pores has remained unclear due to limited mechanistic studies on oxidation-created pores. Using molecular simulations, authors show dynamic...
Spin as an input parameter: Machine learning predicts magnetic properties of materials https://share.google/Dh5QwwqbyKG3X4Nt0
phys.org
Spin as an input parameter: Machine learning predicts magnetic properties of materials
Magnetic materials are in high demand. They're essential to the energy storage innovations on which electrification depends and to the robotics systems powering automation. They're also inside more familiar ...
Molecular simulations uncover how graphite emerges where diamond should form, challenging old assumptions https://share.google/bVK4Bzn7z7QRGJCMW
phys.org
Molecular simulations uncover how graphite emerges where diamond should form, challenging old assumptions
The graphite found in your favorite pencil could have instead been the diamond your mother always wears. What made the difference? Researchers are finding out.