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.
Predicting the thermal conductivity of two-dimensional (2D) heterostructures is challenging and cannot be adequately resolved using conventional computational approaches. To address this challenge, ...
We report a dual-modality ghost diffraction (GD) system to simultaneously enable high-fidelity data transmission and high-resolution object reconstruction through complex disordered media using an ...
Electrical waves in the heart form rotating spiral or scroll waves during life-threatening arrhythmias, such as atrial or ventricular fibrillation. The wave dynamics are typically modeled using ...
High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials. However, the ...
A multi-layer perceptron neural network was used to predict the laser transition figure of merit, a measure of the laser threshold gain, of over 900 × 106 Quantum Cascade (QC) laser designs using ...
Photonic neural networks (PNNs) are gaining significant interest in the research community due to their potential for high parallelization, low latency, and energy efficiency. PNNs compute using ...
In recent years, neural networks have been used to solve phase retrieval problems in imaging with superior accuracy and speed than traditional techniques, especially in the presence of noise. ...
A convolutional neural network is trained on a large dataset of suitably randomized film profiles and corresponding elastic energy densities ρ ɛ , computed by the finite element method. ...
The discovery of the quantum tunneling (QT) effect—the transmission of particles through a high potential barrier—was one of the most impressive achievements of quantum mechanics made in the 1920s. ...
