PicoPaladin retweeted
π΅ππ πππππ! The latest issue of #MRSBulletin covers #PointDefects in #CrystallineMaterial at 100 Years. The introduction by guest editors Drs. Michael Scarpulla, John Lyons, Filip Tuomisto and Elif Ertekin is free to read! #MaterialsMonday
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Jun 8
π΅ππ πππππ! The latest issue of #MRSBulletin covers #PointDefects in #CrystallineMaterial at 100 Years. The introduction by guest editors Drs. Michael Scarpulla, John Lyons, Filip Tuomisto and Elif Ertekin is free to read! #MaterialsMonday
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100 years of #PointDefects! π
In 1926, Yakov Frenkel changed materials science forever. Join #MRSPresents as we celebrate a century of this breakthrough and its impact on everything from semiconductors to quantum tech.
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Production of F-centres in alkali halide salts using a Tesla coil.
#pointdefects
#colouration
#highenergyradiation
#trappedelectrons
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5 Oct 2025
ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
1. The article introduces ADAPT, a novel machine learning force field (MLFF) designed to address the limitations of existing graph neural network (GNN)-based models in capturing long-range interactions and avoiding oversmoothing when modeling point defects in materials.
2. ADAPT replaces graph representations with a direct coordinates-in-space formulation, treating atoms as "tokens" and using a Transformer encoder to model their interactions. This approach allows for explicit consideration of all pairwise atomic interactions, which is crucial for accurately predicting the properties of point defects.
3. The study demonstrates that ADAPT achieves a significant reduction in both force and energy prediction errors compared to state-of-the-art GNN-based models. Specifically, it shows a βΌ33% reduction in errors on a dataset of silicon point defects, while requiring only a fraction of the computational resources.
4. ADAPT's architecture includes an importance-weighted mean squared error (MSE) loss function, which emphasizes the critical regions near defects during training. This specialized loss function helps improve the model's performance in practical applications where defect regions dominate the mechanical response of the material.
5. The training cost of ADAPT is remarkably lower than that of message-passing architectures. The small ADAPT model requires only 2.24 minutes per epoch on a single NVIDIA A100 GPU, converging after 80 epochs, whereas retraining MACE requires 8.5 minutes per epoch for 300 epochs on 16 NVIDIA A100 GPUs.
6. The authors also developed a separate formation energy-predictor model using a MLP residual architecture, which outperformed both MACE and MatterSim in predicting defect formation energies. This model achieves a better than 30% reduction in mean absolute error (MAE) over MatterSim 5M after 400 epochs of training.
7. ADAPT's design allows for independent deployment of the force and energy prediction models when only one quantity is needed, reducing runtime and memory consumption. This modularity also enables incremental model refinements without retraining the entire model.
8. The article highlights that while ADAPT's separation of force and energy predictions offers practical advantages, it results in a non-conservative MLFF where forces are not guaranteed to correspond to gradients of the energy surface. This trade-off is important to consider for applications requiring conservative force fields.
9. The authors suggest future directions for ADAPT, including enforcing physical invariances within the architecture and loss function, extending training to a wider class of defects and materials, and developing models that integrate physical constraints directly into the architecture.
πPaper: arxiv.org/abs/2509.24115
#MachineLearning #MaterialsScience #PointDefects #TransformerModel #ComputationalEfficiency #ForceFields
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26 Nov 2024
Vibrational and Magnetic States of Point Defects in Bilayer MoSe2 | Journal of the American Chemical Society #Vibrational #Magnetic #States #PointDefects #Bilayer #MoSe2 pubs.acs.org/doi/10.1021/jacβ¦
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Inner layer of MoS2/SnS #heterojunctions is suitable #catalyst for hydrogen evolution reaction, thanks to #nanoconfinement & #pointdefects. Check our work, published in @ACS_AEM. Exceptional effort by Ankita. @iitdelhi @chemistry_IITD #DFT #NAMD #catalyst pubs.acs.org/doi/10.1021/acsβ¦
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#ScanningTunnelingMicroscopy characterization and DFT calculations enable the identification and manipulation of atomic and electronic structures of #PointDefects in a single van der Waals monolayer of SnSe. @hkust
#OpenAccess: go.acs.org/aQz
ALT TOC image from the article
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5 Jun 2024
Controlled creation of point defects in three-dimensional colloidal crystals, Max P. M. Schelling and Janne-Mieke Meijer #PointDefects #ColloidalCrystal @JenneMikie @TUeTN @ICMStue go.aps.org/4e14Irs
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24 Feb 2024
PRB Editors' Suggestion: Excited-state geometry relaxation of #PointDefects in #monolayer #hexagonal #BoronNitride
A. Kirchhoff, T. Deilmann, and M. Rohlfing
Phys. Rev. B 109, 085127
β‘οΈ go.aps.org/3UMVurE
#EdSugg #condmat #physics @APSPhysics
ALT EDITORS' SUGGESTION Excited-state geometry relaxation of point defects in monolayer hexagonal boron nitride Optical excitation and deexcitation processes are governed by the dependence of the involved energy levels on the atom positions. This geometry dependence is calculated here for several defects in h -BN, e.g., C B V N , within constrained density functional theory and many-body perturbation theory. The investigated Stokes shifts show distinct differences compared to experiments. For the Tamm-Dancoff approximation, an approximation frequently employed in the calculation of exciton energies in crystalline solids, the authors find small but significant influence for defect systems. Alexander Kirchhoff, Thorsten Deilmann, and Michael Rohlfing Phys. Rev. B 109, 085127 (2024)
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