Artificial intelligence and machine learning in design of mechanical materials
Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …
is becoming an important tool in the fields of materials and mechanical engineering …
Recent advances and applications of machine learning in solid-state materials science
One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …
is machine learning. This collection of statistical methods has already proved to be capable …
The role of machine learning in the understanding and design of materials
Develo** algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …
enable us to systematically find novel materials, which can have huge technological 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 …
Graph networks as a universal machine learning framework for molecules and crystals
Graph networks are a new machine learning (ML) paradigm that supports both relational
reasoning and combinatorial generalization. Here, we develop universal MatErials Graph …
reasoning and combinatorial generalization. Here, we develop universal MatErials Graph …
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 …
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 for materials scientists: an introductory guide toward best practices
This Methods/Protocols article is intended for materials scientists interested in performing
machine learning-centered research. We cover broad guidelines and best practices …
machine learning-centered research. We cover broad guidelines and best practices …
Innovative materials science via machine learning
C Gao, X Min, M Fang, T Tao, X Zheng… - Advanced Functional …, 2022 - Wiley Online Library
Nowadays, the research on materials science is rapidly entering a phase of data‐driven
age. Machine learning, one of the most powerful data‐driven methods, have been being …
age. Machine learning, one of the most powerful data‐driven methods, have been being …
Autonomous discovery in the chemical sciences part I: Progress
This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …
discovery in the chemical sciences. In this first part, we describe a classification for …