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 …
Unleashing the power of artificial intelligence in materials design
The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing
the field of materials engineering thanks to their power to predict material properties, design …
the field of materials engineering thanks to their power to predict material properties, design …
Deep learning model to predict complex stress and strain fields in hierarchical composites
Materials-by-design is a paradigm to develop previously unknown high-performance
materials. However, finding materials with superior properties is often computationally or …
materials. However, finding materials with superior properties is often computationally or …
End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures
Due to the high demand for materials with superior mechanical properties and diverse
functions, designing composite materials is an integral part in materials development …
functions, designing composite materials is an integral part in materials development …
[HTML][HTML] Generative design, manufacturing, and molecular modeling of 3D architected materials based on natural language input
We describe a method to generate 3D architected materials based on mathematically
parameterized human readable word input, offering a direct materialization of language. Our …
parameterized human readable word input, offering a direct materialization of language. Our …
A semi-supervised approach to architected materials design using graph neural networks
Recent breakthroughs in artificial intelligence (AI) afford opportunities for new paradigms for
material design and optimization. For modeling-driven design approaches, the optimization …
material design and optimization. For modeling-driven design approaches, the optimization …
End-to-end deep learning model to predict and design secondary structure content of structural proteins
Structural proteins are the basis of many biomaterials and key construction and functional
components of all life. Further, it is well-known that the diversity of proteins' function relies on …
components of all life. Further, it is well-known that the diversity of proteins' function relies on …
End-to-end protein normal mode frequency predictions using language and graph models and application to sonification
The prediction of mechanical and dynamical properties of proteins is an important frontier,
especially given the greater availability of proteins structures. Here we report a series of …
especially given the greater availability of proteins structures. Here we report a series of …
Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling
We report the use of a deep learning model to design de novo proteins, based on the
interplay of elementary building blocks via hierarchical patterns. The deep neural network …
interplay of elementary building blocks via hierarchical patterns. The deep neural network …
Rapid prediction of protein natural frequencies using graph neural networks
Natural vibrational frequencies of proteins help to correlate functional shifts with sequence
or geometric variations that lead to negligible changes in protein structures, such as point …
or geometric variations that lead to negligible changes in protein structures, such as point …