@gkhmadsen.bsky.social
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đź“Ą 31
đź“ť 11
Theoretical materials chemistry at TU Wien
reposted by
Christoph Dellago
19 days ago
Look at this fantasic nested sampling cake. There is even some replica exchange! They made it for Nico Unglert, who successfully defended his doctoral thesis today at
@tuwien.at
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Nico's work on predictive phase diagrams combining active learning for MLIP backed replica-exchange nested sampling is out in NPJ Comp Mater.
www.nature.com/articles/s41...
Funded by
@fwf-at.bsky.social
through
@taco-oxides.bsky.social
and COE-MECS
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Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling - npj Computational Materials
npj Computational Materials - Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling
https://www.nature.com/articles/s41524-026-01989-z
19 days ago
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Linus' work on identifying uncertain motifs for active learning of rough copper–water interface MLIP out in ACS Materials Au
@pubs.acs.org
pubs.acs.org/doi/10.1021/...
Funded by
@fwf-at.bsky.social
through COE-MECS
loading . . .
How Realistic Are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper–Water Interfaces
Copper is a highly promising catalyst for the electrochemical CO2 reduction reaction (CO2RR) since it is the only pure metal that can form highly added-value products such as ethylene and ethanol. Since the CO2RR takes place in aqueous solution, the detailed atomic structure of the water–copper interface is essential for unraveling the key reaction mechanisms. In this study, we investigate copper–water interfaces exhibiting nanometer-scale roughnesses. We introduce two molecular dynamics protocols to create rough copper surfaces, which are subsequently brought into contact with water. From these interfaces, we sample additional training configurations from machine-learning-interatomic-potential-driven molecular dynamics simulations containing hundreds of thousands of atoms. An active learning workflow is developed to identify regions with high spatially resolved uncertainty and convert them into DFT-feasible cells through a modified amorphous matrix embedding approach. Finally, we analyze the local environments at the interface using unsupervised machine-learning techniques. Unique environments emerge on the rough copper surfaces absent from model systems, including stacking-fault-induced configurations and undercoordinated corner atoms. Notably, corner atoms consistently feature chemisorbed water molecules in our simulations, indicating their potential importance in catalytic processes.
https://pubs.acs.org/doi/10.1021/acsmaterialsau.5c00174
25 days ago
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This grand theory is also applicable to being a professor
add a skeleton here at some point
3 months ago
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Alex combining symmetry, MLIPs and evolutionary searches to complete the rotational defects in MoS2 is out in
@pccp.rsc.org
.
doi.org/10.1039/D5CP...
. Funded by
@fwf-at.bsky.social
through TU-Dx and
@taco-oxides.bsky.social
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Completing the hierarchy of rotational defects in monolayer MoS2 through symmetry-aware evolutionary search
Monolayer molybdenum disulfide (MoS2) shows a plethora of defect configurations, which constitutes the basis for tailoring material properties through defect engineering. Detailed characterization of ...
https://doi.org/10.1039/D5CP03121D
3 months ago
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🎂for benzene. And a bit disappointing that bluesky doesn't have a benzene emoji
add a skeleton here at some point
6 months ago
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I have a "negative personal opinion" on Trump (and his entire posse). I guess no MRS for the foreseeable future. Canada, can you up your game?
add a skeleton here at some point
about 1 year ago
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reposted by
Robert McNees
over 1 year ago
Happy
#QuantumDay
to all who celebrate! Max Planck submitted work on blackbody radiation to the German Physical Society
#OTD
in 1900. His novel “quantum hypothesis” suggested that matter emits and absorbs light with frequency f only in discrete chunks of energy E=hf. 🧪 ⚛️ (1/n) Image: AIP
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Sebastian's work on the high-temperature phase of HfO2 and the interpretation of MD trajectories is out in Phys Rev B.
doi.org/10.1103/Phys...
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Neural network enabled molecular dynamics study of ${\mathrm{HfO}}_{2}$ phase transitions
The advances of machine-learned force fields have opened up molecular dynamics (MD) simulations for compounds for which ab initio MD is too resource intensive and phenomena for which classical force f...
https://doi.org/10.1103/PhysRevB.110.174105
over 1 year ago
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I once discovered new interesting papers every week on twitter. That's long gone. Let's find the same energy here. X can keep the pornbots.
add a skeleton here at some point
over 1 year ago
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you reached the end!!
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