The tumor microenvironment (TME) is composed of many different cellular and acellular
components that together drive tumor growth, invasion, metastasis, and response to …

Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …

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 …

Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …

Recent advances in machine learning (ML) have led to substantial performance
improvement in material database benchmarks, but an excellent benchmark score may not …

Phase engineering of nanomaterials (PEN) is an emerging field that aims to tailor the
physicochemical properties of nanomaterials by precisely manipulating their crystal phases …

Multiplexed imaging enables measurement of multiple proteins in situ, offering an
unprecedented opportunity to chart various cell types and states in tissues. However, cell …

Abstract Machine learning (ML) has become critical for post-acquisition data analysis in
(scanning) transmission electron microscopy,(S) TEM, imaging and spectroscopy. An …

Over the past 70 years, the semiconductor industry has undergone transformative changes,
largely driven by the miniaturization of devices and the integration of innovative structures …