Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Data‐driven materials science: status, challenges, and perspectives
Data‐driven science is heralded as a new paradigm in materials science. In this field, data is
the new resource, and knowledge is extracted from materials datasets that are too big or …
the new resource, and knowledge is extracted from materials datasets that are too big or …
Disordered enthalpy–entropy descriptor for high-entropy ceramics discovery
The need for improved functionalities in extreme environments is fuelling interest in high-
entropy ceramics,–. Except for the computational discovery of high-entropy carbides …
entropy ceramics,–. Except for the computational discovery of high-entropy carbides …
New kagome prototype materials: discovery of , and
In this work, we present our discovery and characterization of a new kagome prototype
structure, KV 3 Sb 5. We also present the discovery of the isostructural compounds RbV 3 Sb …
structure, KV 3 Sb 5. We also present the discovery of the isostructural compounds RbV 3 Sb …
Emerging materials intelligence ecosystems propelled by machine learning
The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …
successes and promises, several AI ecosystems are blossoming, many of them within the …
High-entropy high-hardness metal carbides discovered by entropy descriptors
High-entropy materials have attracted considerable interest due to the combination of useful
properties and promising applications. Predicting their formation remains the major …
properties and promising applications. Predicting their formation remains the major …
Machine learning for molecular and materials science
Here we summarize recent progress in machine learning for the chemical sciences. We
outline machine-learning techniques that are suitable for addressing research questions in …
outline machine-learning techniques that are suitable for addressing research questions in …
Phase stability and mechanical properties of novel high entropy transition metal carbides
Twelve different equiatomic five-metal carbides of group IVB, VB, and VIB refractory
transition metals are synthesized via high-energy ball milling and spark plasma sintering …
transition metals are synthesized via high-energy ball milling and spark plasma sintering …
Interpretable and explainable machine learning for materials science and chemistry
Conspectus Machine learning has become a common and powerful tool in materials
research. As more data become available, with the use of high-performance computing and …
research. As more data become available, with the use of high-performance computing and …
Machine learning: accelerating materials development for energy storage and conversion
With the development of modern society, the requirement for energy has become
increasingly important on a global scale. Therefore, the exploration of novel materials for …
increasingly important on a global scale. Therefore, the exploration of novel materials for …