Interpretable machine learning model on thermal conductivity using publicly available datasets and our internal lab dataset
Machine learning (ML), a subdiscipline of artificial intelligence studies, has gained
importance in predicting or suggesting efficient thermoelectric materials. Previous ML …
importance in predicting or suggesting efficient thermoelectric materials. Previous ML …
Machine Learning for Next Generation Thermoelectrics
Thermoelectricity offers a ground-breaking solution for capturing waste heat and
transforming it into valuable electricity. Despite its promise, the quest for high-performance …
transforming it into valuable electricity. Despite its promise, the quest for high-performance …
Challenges Reconciling Theory and Experiments in the Prediction of Lattice Thermal Conductivity: The Case of Cu-Based Sulvanites
I Caro-Campos, MM González-Barrios… - Chemistry of …, 2024 - ACS Publications
The exploration of large chemical spaces in search of new thermoelectric materials requires
the integration of experiments, theory, simulations, and data science. The development of …
the integration of experiments, theory, simulations, and data science. The development of …
Thermoelectric Material Performance (zT) Predictions with Machine Learning
Research efforts using the tools in machine-and deep learning models have begun to show
success in predicting target properties such as thermoelectric (TE) properties, including the …
success in predicting target properties such as thermoelectric (TE) properties, including the …
Identifying the effect of tributyl phosphate on the growth of PbTe quantum dots: Linking experimental and theoretical approaches
The effects of tributyl phosphate (TBP), as a process control agent (PCA), on morphology,
size and dispersion of PbTe quantum dots (QDs), at 5 h of milling time, are investigated. By …
size and dispersion of PbTe quantum dots (QDs), at 5 h of milling time, are investigated. By …
Predictive models for inorganic materials thermoelectric properties with machine learning
D Don-tsa, MA Mohou, K Amouzouvi… - Machine Learning …, 2024 - iopscience.iop.org
The high computational demand of the Density Functional Theory (DFT) based method for
screening new materials properties remains a strong limitation to the development of clean …
screening new materials properties remains a strong limitation to the development of clean …
Fracture Prediction of Hydrogel Using Machine Learning and Inhomogeneous Multiscale Network
Hydrogels are soft polymeric materials with promising applications in biomedical fields.
Understanding their fracture behavior is crucial for optimizing device design and …
Understanding their fracture behavior is crucial for optimizing device design and …
Predicting Thermoelectric Power Factor of Bismuth Telluride During Laser Powder Bed Fusion Additive Manufacturing
An additive manufacturing (AM) process, like laser powder bed fusion, allows for the
fabrication of objects by spreading and melting powder in layers until a freeform part shape …
fabrication of objects by spreading and melting powder in layers until a freeform part shape …
Sustainable Nanomaterials in Machine Learning: Occurrence and Applications
M Mudabbiruddin, KU Khan - Sustainable Nanomaterials: Synthesis and …, 2024 - Springer
Abstract Machine learning (ML) has proven to be a useful technique in resolving the
problem. Due to their distinctive features and prospective applications, nanomaterials have …
problem. Due to their distinctive features and prospective applications, nanomaterials have …