Sara Kadkhodaei
Cytowane przez
Cytowane przez
Flaw insensitive fracture in nanocrystalline graphene
T Zhang, X Li, S Kadkhodaei, H Gao
Nano letters 12 (9), 4605-4610, 2012
Software tools for high-throughput CALPHAD from first-principles data
A van de Walle, R Sun, QJ Hong, S Kadkhodaei
Calphad 58, 70-81, 2017
The free energy of mechanically unstable phases
A Van De Walle, Q Hong, S Kadkhodaei, R Sun
Nature communications 6 (1), 7559, 2015
Free energy calculation of mechanically unstable but dynamically stabilized bcc titanium
S Kadkhodaei, QJ Hong, A Van De Walle
Physical Review B 95 (6), 064101, 2017
First-principles calculations of thermal properties of the mechanically unstable phases of the PtTi and NiTi shape memory alloys
S Kadkhodaei, A van de Walle
Acta Materialia 147, 296-303, 2018
Predicting synthesizability of crystalline materials via deep learning
A Davariashtiyani, Z Kadkhodaie, S Kadkhodaei
Communications Materials 2 (1), 115, 2021
Cluster expansion of alloy theory: a review of historical development and modern innovations
S Kadkhodaei, JA Muñoz
JOM 73 (11), 3326-3346, 2021
Phonon-assisted diffusion in bcc phase of titanium and zirconium from first principles
S Kadkhodaei, A Davariashtiyani
Physical Review Materials 4 (4), 043802, 2020
Software tools for thermodynamic calculation of mechanically unstable phases from first-principles data
S Kadkhodaei, A van de Walle
Computer Physics Communications 246, 106712, 2020
Computational design of corrosion-resistant and wear-resistant titanium alloys for orthopedic implants
N Siony, L Vuong, O Lundaajamts, S Kadkhodaei
Materials Today Communications 33, 104465, 2022
Epicycle method for elasticity limit calculations
A Van De Walle, S Kadkhodaei, R Sun, QJ Hong
Physical Review B 95 (14), 144113, 2017
A simple local expression for the prefactor in transition state theory
S Kadkhodaei, A van de Walle
The Journal of Chemical Physics 150 (14), 2019
Understanding the role of anharmonic phonons in diffusion of bcc metals
S Fattahpour, A Davariashtiyani, S Kadkhodaei
Physical Review Materials 6 (2), 023803, 2022
Formation energy prediction of crystalline compounds using deep convolutional network learning on voxel image representation
A Davariashtiyani, S Kadkhodaei
Communications Materials 4 (1), 105, 2023
Heat radiation mitigation in rare-earth pyrosilicate composites: A first principles investigation of refractive index mismatch
S Kadkhodaei, S Fattahpour, A Davariashtiyani
Ceramics International 50 (9), 15021-15036, 2024
Voxel Image of Crystals for High-Throughput Materials Screening: Formation Energy Prediction by a Deep Convolutional Network
A Davariashtiyani, S Kadkhodaei
Impact of Data Bias on Machine Learning for Crystal Compound Synthesizability Predictions
A Davariashtiyani, B Wang, S Hajinazar, E Zurek, S Kadkhodaei
arXiv preprint arXiv:2406.17956, 2024
Improving ab initio diffusion calculations in materials through Gaussian process regression
S Fattahpour, S Kadkhodaei
Physical Review Materials 8 (1), 013804, 2024
Enhancing ab initio diffusion calculations in materials through Gaussian process regression
S Fattahpour, S Kadkhodaei
arXiv preprint arXiv:2307.01407, 2023
Zirconium Machine Learned Potential Trained on a Euclidean Neural Network
V Meraz, S Gomez, V Arteaga Muniz, A de La Rocha Galán, T Smidt, ...
APS March Meeting Abstracts 2022, Z32. 005, 2022
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