Graph neural networks for materials science and chemistry
Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …
and materials science, being used to predict materials properties, accelerate simulations …
Spatial profiling technologies illuminate the tumor microenvironment
The tumor microenvironment (TME) is composed of many different cellular and acellular
components that together drive tumor growth, invasion, metastasis, and response to …
components that together drive tumor growth, invasion, metastasis, and response to …
Human-and machine-centred designs of molecules and materials for sustainability and decarbonization
Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …
In pursuit of the exceptional: Research directions for machine learning in chemical and materials science
Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …
technologically valuable and fundamentally interesting, because they often involve new …
Exploiting redundancy in large materials datasets for efficient machine learning with less data
Extensive efforts to gather materials data have largely overlooked potential data
redundancy. In this study, we present evidence of a significant degree of redundancy across …
redundancy. In this study, we present evidence of a significant degree of redundancy across …
Quantitative Description of Metal Center Organization and Interactions in Single‐Atom Catalysts
Ultra‐high‐density single‐atom catalysts (UHD‐SACs) present unique opportunities for
harnessing cooperative effects between neighboring metal centers. However, the lack of …
harnessing cooperative effects between neighboring metal centers. However, the lack of …
Applications of Transmission Electron Microscopy in Phase Engineering of Nanomaterials
Phase engineering of nanomaterials (PEN) is an emerging field that aims to tailor the
physicochemical properties of nanomaterials by precisely manipulating their crystal phases …
physicochemical properties of nanomaterials by precisely manipulating their crystal phases …
A critical examination of robustness and generalizability of machine learning prediction of materials properties
Recent advances in machine learning (ML) have led to substantial performance
improvement in material database benchmarks, but an excellent benchmark score may not …
improvement in material database benchmarks, but an excellent benchmark score may not …
CellSighter: a neural network to classify cells in highly multiplexed images
Multiplexed imaging enables measurement of multiple proteins in situ, offering an
unprecedented opportunity to chart various cell types and states in tissues. However, cell …
unprecedented opportunity to chart various cell types and states in tissues. However, cell …
Probing functional structures, defects, and interfaces of 2D transition metal dichalcogenides by electron microscopy
Abstract 2D transition metal dichalcogenides (TMDs) exhibit remarkable properties that are
strongly influenced by their atomic structures, as well as by various types of defects and …
strongly influenced by their atomic structures, as well as by various types of defects and …