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 …
From DFT to machine learning: recent approaches to materials science–a review
Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …
and complexity of generated data. This massive amount of raw data needs to be stored and …
Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
The modular nature of metal–organic frameworks (MOFs) enables synthetic control over
their physical and chemical properties, but it can be difficult to know which MOFs would be …
their physical and chemical properties, but it can be difficult to know which MOFs would be …
Data‐Driven Materials Innovation and Applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
Computational approaches for organic semiconductors: from chemical and physical understanding to predicting new materials
While a complete understanding of organic semiconductor (OSC) design principles remains
elusive, computational methods─ ranging from techniques based in classical and quantum …
elusive, computational methods─ ranging from techniques based in classical and quantum …
Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
While high-throughput density functional theory (DFT) has become a prevalent tool for
materials discovery, it is limited by the relatively large computational cost. In this paper, we …
materials discovery, it is limited by the relatively large computational cost. In this paper, we …
Symbolic regression in materials science
The authors showcase the potential of symbolic regression as an analytic method for use in
materials research. First, the authors briefly describe the current state-of-the-art method …
materials research. First, the authors briefly describe the current state-of-the-art method …
Database of two-dimensional hybrid perovskite materials: open-access collection of crystal structures, band gaps, and atomic partial charges predicted by machine …
We describe a first open-access database of experimentally investigated hybrid organic–
inorganic materials with a two-dimensional (2D) perovskite-like crystal structure. The …
inorganic materials with a two-dimensional (2D) perovskite-like crystal structure. The …
Data-driven discovery of 2D materials by deep generative models
Efficient algorithms to generate candidate crystal structures with good stability properties can
play a key role in data-driven materials discovery. Here, we show that a crystal diffusion …
play a key role in data-driven materials discovery. Here, we show that a crystal diffusion …
MatGPT: A vane of materials informatics from past, present, to future
Combining materials science, artificial intelligence (AI), physical chemistry, and other
disciplines, materials informatics is continuously accelerating the vigorous development of …
disciplines, materials informatics is continuously accelerating the vigorous development of …