Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
The joint automated repository for various integrated simulations (JARVIS) infrastructure at
the National Institute of Standards and Technology is a large-scale collection of curated …
the National Institute of Standards and Technology is a large-scale collection of curated …
Evolution of artificial intelligence for application in contemporary materials science
Contemporary materials science has seen an increasing application of various artificial
intelligence techniques in an attempt to accelerate the materials discovery process using …
intelligence techniques in an attempt to accelerate the materials discovery process using …
Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets
Modern data mining methods have demonstrated effectiveness in comprehending and
predicting materials properties. An essential component in the process of materials …
predicting materials properties. An essential component in the process of materials …
InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning
We introduce a computational framework (InterMat) to predict band offsets of semiconductor
interfaces using density functional theory (DFT) and graph neural networks (GNN). As a first …
interfaces using density functional theory (DFT) and graph neural networks (GNN). As a first …
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
High-entropy materials (HEMs) have recently emerged as a significant category of materials,
offering highly tunable properties. However, the scarcity of HEM data in existing density …
offering highly tunable properties. However, the scarcity of HEM data in existing density …
Physics-based data-augmented deep learning for enhanced autogenous shrinkage prediction on experimental dataset
Prediction of the autogenous shrinkage referred to as the reduction of apparent volume of
concrete under seal and isothermal conditions is of great significance in the service life …
concrete under seal and isothermal conditions is of great significance in the service life …
Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks
Modern data mining techniques using machine learning (ML) and deep learning (DL)
algorithms have been shown to excel in the regression-based task of materials property …
algorithms have been shown to excel in the regression-based task of materials property …
Holistic chemical evaluation reveals pitfalls in reaction prediction models
The prediction of chemical reactions has gained significant interest within the machine
learning community in recent years, owing to its complexity and crucial applications in …
learning community in recent years, owing to its complexity and crucial applications in …
[HTML][HTML] Multimodal learning of heat capacity based on transformers and crystallography pretraining
Thermal properties of materials are essential to many applications of thermal electronic
devices. Density functional theory (DFT) has shown capability in obtaining an accurate …
devices. Density functional theory (DFT) has shown capability in obtaining an accurate …