Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Emerging trends in machine learning: a polymer perspective
In the last five years, there has been tremendous growth in machine learning and artificial
intelligence as applied to polymer science. Here, we highlight the unique challenges …
intelligence as applied to polymer science. Here, we highlight the unique challenges …
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 …
Autonomous experimentation systems for materials development: A community perspective
Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …
development. However, advanced materials can take decades to discover and decades …
A priori control of zeolite phase competition and intergrowth with high-throughput simulations
Zeolites are versatile catalysts and molecular sieves with large topological diversity, but
managing phase competition in zeolite synthesis is an empirical, labor-intensive task. In this …
managing phase competition in zeolite synthesis is an empirical, labor-intensive task. In this …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
Machine learning in energy storage materials
With its extremely strong capability of data analysis, machine learning has shown versatile
potential in the revolution of the materials research paradigm. Here, taking dielectric …
potential in the revolution of the materials research paradigm. Here, taking dielectric …
Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review
Supercapacitors are appealing energy storage devices for their promising features like high
power density, outstanding cycling stability, and a quick charge–discharge cycle. The …
power density, outstanding cycling stability, and a quick charge–discharge cycle. The …
FAIR for AI: An interdisciplinary and international community building perspective
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles
were proposed in 2016 as prerequisites for proper data management and stewardship, with …
were proposed in 2016 as prerequisites for proper data management and stewardship, with …
Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations
Summary Machine Learning Interatomic Potential (MLIP) overcomes the challenges of high
computational costs in density-functional theory and the relatively low accuracy in classical …
computational costs in density-functional theory and the relatively low accuracy in classical …
Principles of the battery data genome
Batteries are central to modern society. They are no longer just a convenience but a critical
enabler of the transition to a resilient, low-carbon economy. Battery development capabilities …
enabler of the transition to a resilient, low-carbon economy. Battery development capabilities …