APL Machine Learning features vibrant and timely research for two communities: researchers who use machine learning (ML) and data-driven approaches for physical sciences and related disciplines, and researchers from these disciplines who work on novel concepts, including materials, devices, systems, and algorithms relevant for the development of better ML and AI technologies.
Carbon nanotube (CNT) synthesis is a ripe area for autonomous experimentation. It is a high-dimensional problem, with both a large number of experimental inputs and critically important outputs, ...
What does materials science look like in the “Age of Artificial Intelligence?” Each material’s domain—synthesis, characterization, and modeling—has a different answer to this question, motivated by ...
We present the development of a machine-learning (ML) model for predicting the congruency of compound melts by utilizing a combination of density functional theory-calculated formation energies and a ...
We present an approach for modifying transformer architectures by integrating graph-aware relational reasoning into the attention mechanism, merging concepts from graph neural networks and language ...
This work highlights the advantages that ferroelectric tunnel junction (FTJ) memristors can bring to the non-volatile memory technology and in custom designed neuromorphic hardware. Advantages of ...
Next-generation artificial intelligence (AI) hardware based on memristive devices offers a promising approach to reducing the increasingly large energy consumption of AI applications. However, ...
Resistive crossbar arrays have been shown to enable the implementation of energy-efficient in-memory computing accelerators suitable for the diffusion of artificial neural networks (ANNs) at the ...
Ultra-intense laser–matter interactions are often difficult to predict from first principles because of the complexity of plasma processes and the many degrees of freedom relating to the laser and ...
Carbon nanotube (CNT) synthesis is a ripe area for autonomous experimentation. It is a high-dimensional problem, with both a large number of experimental inputs and critically important outputs, ...
