7 citations · 41 across the 30 of their papers we have counts for
10 papers · 1 filter
Computational discovery of new 2D materials using deep learning generative models
Yuqi Song, Edirisuriya M. Dilanga Siriwardane, Yong Zhao +1
Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic…
MLatticeABC: Generic Lattice Constant Prediction of Crystal Materials using Machine Learning
Yuxin Li, Wenhui Yang, Rongzhi Dong +1
Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials. Predicting crystal lattice constants has…
Distance Matrix based Crystal Structure Prediction using Evolutionary Algorithms
Jianjun Hu, Wenhui Yang, Edirisuriya M. Dilanga Siriwardane
Crystal structure prediction (CSP) for inorganic materials is one of the central and most challenging problems in materials science and computational chemistry. This problem can be…
Inverse Design of Composite Metal Oxide Optical Materials based on Deep Transfer Learning
Rongzhi Dong, Yabo Dan, Xiang Li +1
Optical materials with special optical properties are widely used in a broad span of technologies, from computer displays to solar energy utilization leading to large dataset accum…
Lattice Thermal Conductivity Prediction using Symbolic Regression and Machine Learning
Christian Loftis, Kunpeng Yuan, Yong Zhao +2
Prediction models of lattice thermal conductivity have wide applications in the discovery of thermoelectrics, thermal barrier coatings, and thermal management of semiconductors. kL…
Contact Map based Crystal Structure Prediction using Global Optimization
Jianjun Hu, Wenhui Yang, Rongzhi Dong +3
Crystal structure prediction is now playing an increasingly important role in discovery of new materials. Global optimization methods such as genetic algorithms (GA) and particle s…