Copertina

Impact of Data Bias on Machine Learning for Crystal Compound Synthesizability Predictions

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Davariashtiyani, Ali, Wang, Busheng, Hajinazar, Samad, Zurek, Eva, Kadkhodaei, Sara
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Materials Science
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

XtalOpt Version 14: Variable-Composition Crystal Structure Search for Functional Materials Through Pareto Optimization
di: Hajinazar, Samad, et al.
Pubblicazione: (2025)

XtalOpt Version 13: Multi-Objective Evolutionary Search for Novel Functional Materials
di: Hajinazar, Samad, et al.
Pubblicazione: (2024)

Powder X-Ray Diffraction Assisted Evolutionary Algorithm for Crystal Structure Prediction
di: Racioppi, Stefano, et al.
Pubblicazione: (2024)

Leveraging Large Language Models to Address Data Scarcity in Machine Learning: Applications in Graphene Synthesis
di: Biswajeet, Devi Dutta, et al.
Pubblicazione: (2025)

Enhancing ab initio diffusion calculations in materials through Gaussian process regression
di: Fattahpour, Seyyedfaridoddin, et al.
Pubblicazione: (2023)

Is Large Language Model All You Need to Predict the Synthesizability and Precursors of Crystal Structures?
di: Song, Zhilong, et al.
Pubblicazione: (2024)

Using Topology to Predict Electrides in the Solid State
di: Racioppi, Stefano, et al.
Pubblicazione: (2025)

Bridging Theory and Experiment in Materials Discovery: Machine-Learning-Assisted Prediction of Synthesizable Structures
di: Xin, Yu, et al.
Pubblicazione: (2025)

Defect energy formalism for CALPHAD thermodynamics of dilute point defects: Theory
di: Movaffagh, Amir M. Orvati, et al.
Pubblicazione: (2024)

Quantum Effects or Theoretical Artifacts? A Computational Reanalysis of Hydrogen at High-Pressure
di: Racioppi, Stefano, et al.
Pubblicazione: (2025)

Bridging the Synthesizability Gap in Perovskites by Combining Computations, Literature Data, and PU Learning
di: Desai, Rushik, et al.
Pubblicazione: (2025)

Theoretical Predictions of MB5N5: Atom-Stuffed Boronitride Clathrate Cages Derived from the High-Pressure Superhydride
di: Geng, Nisha, et al.
Pubblicazione: (2025)

hajinazar/pycxl: Pycxl1.8.1
di: Samad Hajinazar
Pubblicazione: (2025)

pycxl: Python Script to Calculate Convex Hull
di: Samad Hajinazar
Pubblicazione: (2025)

Full-bandwidth anisotropic Migdal-Eliashberg theory and its application to superhydrides
di: Lucrezi, Roman, et al.
Pubblicazione: (2023)

Computational Discovery of Metastable NaMnO$_2$ Polymorphs as High-Performance Cathodes with Ultralow Na$^+$ Migration Barriers
di: Wang, Fukuan, et al.
Pubblicazione: (2025)

Synthesizability Prediction of Crystalline Structures with a Hierarchical Transformer and Uncertainty Quantification
di: Ebrahimzadeh, Danial, et al.
Pubblicazione: (2025)

Large Out-of-Plane Piezoelectric Effect in Janus Ferromagnetic Semiconductor Monolayer of CrOFBr
di: Ma, Qiuyue, et al.
Pubblicazione: (2024)

Multifunctional Altermagnet with Large Out-of-Plane Piezoelectric Response in Janus V$_{2}$AsBrO Monolayer
di: Ma, Qiuyue, et al.
Pubblicazione: (2025)

Geometry-Based Neural-Network Prediction of Electron Localization Function Topology in Dense Hydrogen
di: Wang, Xiaoyu, et al.
Pubblicazione: (2026)

A chemical bonding based descriptor for predicting the impact of quantum nuclear and anharmonic effects on hydrogen-based superconductors
di: Belli, Francesco, et al.
Pubblicazione: (2025)

A Synthesizability-Guided Pipeline for Materials Discovery
di: Prein, Thorben, et al.
Pubblicazione: (2025)

Superionicity in Ammonium Polyhydrides at Extreme Pressures
di: de Villa, Kyla, et al.
Pubblicazione: (2025)

SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
di: Amariamir, Sasan, et al.
Pubblicazione: (2024)

Predicting Miscibility in Binary Compounds: A Machine Learning and Genetic Algorithm Study
di: Feng, Chiwen, et al.
Pubblicazione: (2024)

Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database
di: Roberts, Josiah, et al.
Pubblicazione: (2024)

Near-room-temperature antiferromagnetism in Janus Fe$X$F ($X$ = O, S) monolayers
di: Zhang, Xixiang, et al.
Pubblicazione: (2025)

Machine Learning Prediction of Charged Defect Formation Energies from Crystal Structures
di: Kiyohara, Shin, et al.
Pubblicazione: (2025)

Non-local Chemistry Driven by Cation-Anion Size Disparity in Helium Inserted Compounds under High Pressure
di: Liu, Zhen, et al.
Pubblicazione: (2025)

Machine Learning-Driven Crystal System Prediction for Perovskites Using Augmented X-ray Diffraction Data
di: Mathew, Ansu, et al.
Pubblicazione: (2026)

Emergent Multifunctionality in Two-Dimensional Janus VSBrI Monolayer: A Study of Multiferroicity, Magnetoelectricity, and Piezoelectricity
di: Ma, Qiuyue, et al.
Pubblicazione: (2024)

Superconductivity in Dilute Hydrides of Ammonia under Pressure
di: Wang, Xiaoyu, et al.
Pubblicazione: (2024)

Refining Tc Prediction in Hydrides via Symbolic-Regression-Enhanced Electron-Localization-Function-Based Descriptors
di: Belli, Francesco, et al.
Pubblicazione: (2025)

Synthesizability, hardness, and stacking order in multicomponent transition metal carbides from machine-learned potentials
di: Liu, Xin, et al.
Pubblicazione: (2026)

High-Entropy Skutterudites as Thermoelectrics: Potential Synthesizability, Enhanced Stability and Band Convergence via the Cocktail Effect
di: Plata, Jose J., et al.
Pubblicazione: (2025)

Quantum Stabilization and Flat Hydrogen-based Bands of Nitrogen-doped Lutetium Hydride
di: Denchfield, Adam, et al.
Pubblicazione: (2024)

Decoratypes: An Extensible Crystal Taxonomy for Machine Learning-Guided Materials Discovery
di: Miller, Kyle D., et al.
Pubblicazione: (2025)

Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning
di: Wong, Nicolas, et al.
Pubblicazione: (2026)

OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure
di: Wang, Yinan, et al.
Pubblicazione: (2025)

Crystal Growth Characterization of WSe$_2$ Thin Film Using Machine Learning
di: Moses, Isaiah A., et al.
Pubblicazione: (2023)