Machine Learning-Driven Analytical Models for Threshold Displacement Energy Prediction in Materials
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Duque, Rosty B. Martinez, Duha, Arman, Borunda, Mario F. |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Effect of Uncorrelated On-site Scalar Potential and Mass Disorder on Transport of Two-Dimensional Dirac Fermions
par: Duha, Arman, et autres
Publié: (2024)
par: Duha, Arman, et autres
Publié: (2024)
Machine Learning-Driven Insights into Excitonic Effects in 2D Materials
par: Javed, Ahsan, et autres
Publié: (2025)
par: Javed, Ahsan, et autres
Publié: (2025)
Scalable Machine Learning Models for Predicting Quantum Transport in Disordered 2D Hexagonal Materials
par: Mastoor, Seyed Mahdi, et autres
Publié: (2025)
par: Mastoor, Seyed Mahdi, et autres
Publié: (2025)
Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction
par: An, Yuqi, et autres
Publié: (2026)
par: An, Yuqi, et autres
Publié: (2026)
Machine Learning Hamiltonians are Accurate Energy-Force Predictors
par: Kim, Seongsu, et autres
Publié: (2026)
par: Kim, Seongsu, et autres
Publié: (2026)
Machine Learning and Data-Driven Methods in Computational Surface and Interface Science
par: Hörmann, Lukas, et autres
Publié: (2025)
par: Hörmann, Lukas, et autres
Publié: (2025)
Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials
par: Fragapane, Natascia L., et autres
Publié: (2025)
par: Fragapane, Natascia L., et autres
Publié: (2025)
CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties
par: Wines, Daniel, et autres
Publié: (2024)
par: Wines, Daniel, et autres
Publié: (2024)
Data-Efficient Machine learning for Predicting Dopant Formation Energies in TiO$_2$ Monolayer
par: Asikainen, Kati, et autres
Publié: (2026)
par: Asikainen, Kati, et autres
Publié: (2026)
LeapFrog: Getting the Jump on Multi-Scale Materials Simulations Using Machine Learning
par: Pinto, Damien, et autres
Publié: (2024)
par: Pinto, Damien, et autres
Publié: (2024)
Prediction of Frequency-Dependent Optical Spectrum for Solid Materials: A Multi-Output & Multi-Fidelity Machine Learning Approach
par: Ibrahim, Akram, et autres
Publié: (2024)
par: Ibrahim, Akram, et autres
Publié: (2024)
Long-Range Machine Learning of Electron Density for Twisted Bilayer Moiré Materials
par: Lou, Zekun, et autres
Publié: (2026)
par: Lou, Zekun, et autres
Publié: (2026)
Integrated Experiment and Simulation Co-Design: A Key Infrastructure for Predictive Mesoscale Materials Modeling
par: Joshi, Shailendra P., et autres
Publié: (2025)
par: Joshi, Shailendra P., et autres
Publié: (2025)
Constitutive Kolmogorov-Arnold Networks (CKANs): Combining Accuracy and Interpretability in Data-Driven Material Modeling
par: Abdolazizi, Kian P., et autres
Publié: (2025)
par: Abdolazizi, Kian P., et autres
Publié: (2025)
Universal Machine Learning Kohn-Sham Hamiltonian for Materials
par: Zhong, Yang, et autres
Publié: (2024)
par: Zhong, Yang, et autres
Publié: (2024)
Towards Universal Material Property Prediction with Deep Learning and Single-Descriptor electronic Density
par: Chen, Feng, et autres
Publié: (2025)
par: Chen, Feng, et autres
Publié: (2025)
Predicting Miscibility in Binary Compounds: A Machine Learning and Genetic Algorithm Study
par: Feng, Chiwen, et autres
Publié: (2024)
par: Feng, Chiwen, et autres
Publié: (2024)
Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates
par: Wang, Zeyu, et autres
Publié: (2026)
par: Wang, Zeyu, et autres
Publié: (2026)
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property Prediction
par: Ekuma, Chinedu
Publié: (2024)
par: Ekuma, Chinedu
Publié: (2024)
A Foundational Potential Energy Surface Dataset for Materials
par: Kaplan, Aaron D., et autres
Publié: (2025)
par: Kaplan, Aaron D., et autres
Publié: (2025)
Load-dependent Hardness Prediction for Materials using Machine Learning
par: Mukherjee, Madhubanti, et autres
Publié: (2026)
par: Mukherjee, Madhubanti, et autres
Publié: (2026)
A Comparative Study of Machine Learning Models Predicting Energetics of Interacting Defects
par: Yu, Hao
Publié: (2024)
par: Yu, Hao
Publié: (2024)
Stress, Strain, or Displacement? A Novel Machine Learning Based Framework to Predict Mixed Mode I/II Fracture Toughness
par: Mirzaei, Amir Mohammad
Publié: (2025)
par: Mirzaei, Amir Mohammad
Publié: (2025)
Linear Scaling Calculation of Atomic Forces and Energies with Machine Learning Local Density Matrix
par: Xin, Zaizhou, et autres
Publié: (2025)
par: Xin, Zaizhou, et autres
Publié: (2025)
Large Language Models for Material Property Predictions: elastic constant tensor prediction and materials design
par: Liu, Siyu, et autres
Publié: (2024)
par: Liu, Siyu, et autres
Publié: (2024)
UniMatSim: A High-Throughput Materials Simulation Automation Framework Based on Universal Machine Learning Potentials
par: Xiang, Yanjin, et autres
Publié: (2026)
par: Xiang, Yanjin, et autres
Publié: (2026)
Symmetry- and Gradient-enhanced Gaussian Process Regression for the Active Learning of Potential Energy Surfaces in Porous Materials
par: Krondorfer, Johannes K., et autres
Publié: (2025)
par: Krondorfer, Johannes K., et autres
Publié: (2025)
Grain Boundary Defect Production during Successive Displacement Cascades on a Tungsten Surface
par: Zhang, Yang, et autres
Publié: (2024)
par: Zhang, Yang, et autres
Publié: (2024)
From Data to Alloys Predicting and Screening High Entropy Alloys for High Hardness Using Machine Learning
par: Bouri, Rahul, et autres
Publié: (2025)
par: Bouri, Rahul, et autres
Publié: (2025)
Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials
par: Ullberg, R. Seaton, et autres
Publié: (2026)
par: Ullberg, R. Seaton, et autres
Publié: (2026)
Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals
par: Heilman, Alex, et autres
Publié: (2024)
par: Heilman, Alex, et autres
Publié: (2024)
Accelerating Phonon Thermal Conductivity Prediction by an Order of Magnitude Through Machine Learning-Assisted Extraction of Anharmonic Force Constants
par: Srivastava, Yagyank, et autres
Publié: (2024)
par: Srivastava, Yagyank, et autres
Publié: (2024)
Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Study
par: Mehdizadeh, Ardavan, et autres
Publié: (2025)
par: Mehdizadeh, Ardavan, et autres
Publié: (2025)
On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants
par: Avilov, Fedor S., et autres
Publié: (2024)
par: Avilov, Fedor S., et autres
Publié: (2024)
CTGNN: Crystal Transformer Graph Neural Network for Crystal Material Property Prediction
par: Du, Zijian, et autres
Publié: (2024)
par: Du, Zijian, et autres
Publié: (2024)
Machine Learning Inversion from Scattering for Mechanically Driven Polymers
par: Ding, Lijie, et autres
Publié: (2024)
par: Ding, Lijie, et autres
Publié: (2024)
Identifying Split Vacancy Defects with Machine-Learned Foundation Models and Electrostatics
par: Kavanagh, Seán R.
Publié: (2024)
par: Kavanagh, Seán R.
Publié: (2024)
Data-Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis
par: Jiang, Zhi, et autres
Publié: (2025)
par: Jiang, Zhi, et autres
Publié: (2025)
Crystal Transformer Based Universal Atomic Embedding for Accurate and Transferable Prediction of Materials Properties
par: Jin, Luozhijie, et autres
Publié: (2024)
par: Jin, Luozhijie, et autres
Publié: (2024)
Materials Discovery With Quantum-Enhanced Machine Learning Algorithms
par: Graña, Ignacio F., et autres
Publié: (2025)
par: Graña, Ignacio F., et autres
Publié: (2025)
Documents similaires
-
Effect of Uncorrelated On-site Scalar Potential and Mass Disorder on Transport of Two-Dimensional Dirac Fermions
par: Duha, Arman, et autres
Publié: (2024) -
Machine Learning-Driven Insights into Excitonic Effects in 2D Materials
par: Javed, Ahsan, et autres
Publié: (2025) -
Scalable Machine Learning Models for Predicting Quantum Transport in Disordered 2D Hexagonal Materials
par: Mastoor, Seyed Mahdi, et autres
Publié: (2025) -
Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction
par: An, Yuqi, et autres
Publié: (2026) -
Machine Learning Hamiltonians are Accurate Energy-Force Predictors
par: Kim, Seongsu, et autres
Publié: (2026)