Computational thermodynamics and its applications

ZK Liu - Acta Materialia, 2020 - Elsevier
Thermodynamics is a science concerning the state of a system, whether it is stable,
metastable or unstable, when interacting with the surroundings. In this overview …

Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives

ZK Liu - Calphad, 2023 - Elsevier
Thermodynamics is a science concerning the state of a system, whether it is stable,
metastable, or unstable, when interacting with its surroundings. The combined law of …

Theory of cross phenomena and their coefficients beyond Onsager theorem

ZK Liu - Materials Research Letters, 2022 - Taylor & Francis
Cross phenomena, representing responses of a system to external stimuli, are ubiquitous
from quantum to macro scales. The Onsager theorem is often used to describe them, stating …

Melting temperature prediction using a graph neural network model: From ancient minerals to new materials

QJ Hong, SV Ushakov… - Proceedings of the …, 2022 - National Acad Sciences
The melting point is a fundamental property that is time-consuming to measure or compute,
thus hindering high-throughput analyses of melting relations and phase diagrams over large …

A data-driven approach to improve customer churn prediction based on telecom customer segmentation

T Zhang, S Moro, RF Ramos - Future Internet, 2022 - mdpi.com
Numerous valuable clients can be lost to competitors in the telecommunication industry,
leading to profit loss. Thus, understanding the reasons for client churn is vital for …

A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks

S Liu, B Bocklund, J Diffenderfer, S Chaganti… - npj Computational …, 2024 - nature.com
Predicting phase stability in high entropy alloys (HEAs), such as phase fractions as functions
of composition and temperature, is essential for understanding alloy properties and …

Advances of machine learning in materials science: Ideas and techniques

SS Chong, YS Ng, HQ Wang, JC Zheng - Frontiers of Physics, 2024 - Springer
In this big data era, the use of large dataset in conjunction with machine learning (ML) has
been increasingly popular in both industry and academia. In recent times, the field of …

Reflections on one million compounds in the open quantum materials database (OQMD)

J Shen, SD Griesemer, A Gopakumar… - Journal of Physics …, 2022 - iopscience.iop.org
Density functional theory (DFT) has been widely applied in modern materials discovery and
many materials databases, including the open quantum materials database (OQMD) …

Genomic materials design: calculation of phase dynamics

GB Olson, ZK Liu - Calphad, 2023 - Elsevier
The CALPHAD system of fundamental phase-level databases, now known as the Materials
Genome, has enabled a mature technology of computational materials design and …

Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges

S Jha, M Yen, YS Salinas, E Palmer… - Journal of Materials …, 2023 - pubs.rsc.org
Machine learning (ML) has been the focus in recent studies aiming to improve battery and
supercapacitor technology. Its application in materials research has demonstrated promising …