Xue Jia 교수

Abstract Thermoelectric materials can be potentially applied to waste heat recovery and solid-state cooling because they allow a direct energy conversion between heat and electricity and vice versa. The accelerated materials design based on machine learning has enabled the systematic discovery of promising materials. Herein we proposed a successful strategy to discover and design a series of promising half-Heusler thermoelectric materials through the iterative combination of unsupervised machine

Electrocatalytic water splitting, comprising the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), provides a sustainable route for hydrogen production. While low-cost metal oxides (MOs) are appealing as alternatives to noble metal electrocatalysts, their application in acidic media remains challenging. However, the dynamic nature of some MO surface structures under electrochemical conditions offers an opportunity for rational catalyst design to achieve bifunctionality in ac

Data mining from computational materials database has become a popular strategy to identify unexplored catalysts. Herein, the opportunities and challenges of this strategy are analyzed by investigating a discrepancy between data mining and experiments in identifying low-cost metal oxide (MO) electrocatalysts. Based on a search engine capable of identifying stable MOs at the pH and potentials of interest, a series of MO electrocatalysts is identified as potential candidates for various reactions.

Machine learning can map and predict the oxygen reduction reaction performance of multicomponent metal oxides in alkaline media.

The data-driven machine learning technique is widely used to assist in accelerating the design of thermoelectric materials. In this study, we proposed a positive and unlabeled learning (PU learning) method, a semi-supervised learning, to train a classifier to distinguish the positive samples from the unlabeled samples, in which the positive class was labeled by matching the formulas in our dataset with the published article titles. The probabilities that the unlabeled materials belong to the pos

• Data science accelerates electrocatalyst discovery for sustainable energy applications. • DFT-derived parameters enable volcano plots and predictive models for reactions. • Transition from DFT-based descriptors to high-dimensional data analysis. • Machine learning reveals complex patterns beyond traditional descriptor approaches. The integration of data science into electrocatalysis has revolutionized the discovery of high-performance catalysts for sustainable energy applications. To emphasize

Metal oxides (MOs) are a class of electrocatalysts which could be the low-cost alternatives to precious metals. However, many MOs suffer from poor stability under electrochemical operating conditions. The Materials Project stands out as one of the largest computational materials databases to date, where the bulk Pourbaix diagrams are essential in assessing the aqueous stability of potential electrocatalysts. Herein, we performed data mining from the Materials Project database to identify potenti

Ferroptosis, an iron-dependent programmed cell death driven by lipid peroxidation, plays a critical role in autoimmune diseases such as rheumatoid arthritis, systemic lupus erythematosus, and psoriasis. This review systematically explores the interaction between ferroptosis and the immune system, highlighting its dynamic regulation of immune cell function (e.g., Treg cell stability, neutrophil activity) and inflammatory microenvironments via signaling pathways including JAK/STAT and NF-κB. Ferro

The hydrogenation of carbon dioxide to produce clean fuels is one of the important means to achieve carbon neutrality, among which CO2 methanation has attracted much attention due to its thermodynamic advantages and environmental friendliness. However, the large-scale application of the technology still faces bottlenecks such as insufficient low-temperature activity of the catalyst and poor resistance to carbon deposition. The multiscale catalyst design method based on machine learning (ML) prov