Chang Liu

Publications

• Full-potential KKR calculations for interaction energies in Al-Rich AlX- X= H∼Sn) Alloys: I. fundamental features and thermal electronic contribution due to fermi-dirac distribution
• Full-Potential KKR calculations for point defect energies in Fe-based dilute alloys, based on the Generalized-Gradient Approximation
• Exploring diamond-like lattice thermal conductivity crystals via feature-based transfer learning
• Exploring diamond-like lattice thermal conductivity crystals via feature-based transfer learning
• Machine learning to predict quasicrystals from chemical compositions
• Accuracy of Real Space Cluster Expansion for Total Energies of Pd-rich PdX (X=Rh, Ru) Alloys, based on Full-Potential KKR Calculations for Perfect and Impurity Systems
• iQSPR in XenonPy: A Bayesian Molecular Design Algorithm
• Full-Potential KKR Calculations for Lattice Distortion Around Impurities in Al-based Dilute Alloys, Based on the Generalized-Gradient Approximation
• Crystal structure prediction with machine learning-based element substitution
• Functional Output Regression for Machine Learning in Materials Science
• マテリアルズインフォマティクス
• マテリアルズ・インフォマティクス概説
• Theoretical Approach for Long-Ranged Local Lattice Distortion in Al-Rich AlX (X = H∼Sn) Disordered Alloys by Kanzaki Model Combined with KKR Green’s Function Method
• Exploring diamondlike lattice thermal conductivity crystals via feature-based transfer learning
• Recreation of the periodic table with an unsupervised machine learning algorithm
• Ab-Initio Calculations for Solvus Temperatures of Pd-Rich PdRu Alloys: Real-Space Cluster Expansion and Cluster Variation Method
• Full-Potential KKR Calculations for Interaction Energies in Al-Rich AlX (X = H∼Sn) Alloys: I. Fundamental Features and Thermal Electronic Contribution due to Fermi-Dirac Distribution
• Predicting Materials Properties with Little Data Using Shotgun Transfer Learning
• Real Space Cluster Expansion for Total Energies of Pd-Rich PdX (X = Rh, Ru) Alloys, Based on Full-Potential KKR Calculations: An Approach from a Dilute Limit
• Interaction Energies Among Rh Impurities in Pd and Solvus Temperatures of Pd-Rich PdRh Alloys
• Full-Potential KKR calculations for Lattice Distortion around Impurities in Al-based dilute alloys, based on the Generalized-Gradient Approximation
• Full-Potential KKR Calculations for Lattice Distortion Effect of Point Defect in bcc-Fe Dilute Alloys, Based on the Generalized-Gradient Approximation
• Full-Potential KKR Calculations for Point Defect Energies in Fe-Based Dilute Alloys, Based on the Generalized-Gradient Approximation
• Full-Potential KKR Calculations for Lattice Distortion of Impurities in Fe-Based Dilute Alloys, Based on the Generalized-Gradient Approximation
• Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach
• Quasicrystals predicted and discovered by machine learning
• Representation of materials by kernel mean embedding
• A Bayesian method for concurrently designing molecules and synthetic reaction networks
• Machine Learning to Predict Quasicrystals from Chemical Compositions
• Comprehensive experimental datasets of quasicrystals and their approximants
• Shotgun crystal structure prediction using machine-learned formation energies