279 results
Showing 1–20 · 0.36 seconds · RSS
APL Machine Learning
Scopus
ISSN 2770-9019 · AIP Publishing LLC · 279 articles in the index
-
Journal Article Full text
Osprey: Production-ready agentic AI for safety-critical control systems
Operating large-scale scientific facilities requires coordinating diverse subsystems, translating operator intent into precise hardware actions, and maintaining strict safety oversight. Language model-driven agents offer a natural interface for these tasks, but most existing approaches are not yet…
-
Journal Article Full text
Efficient knowledge distillation via salient feature masking
Traditional Knowledge Distillation (KD) transfers all outputs from a teacher model to a student model, often introducing knowledge redundancy. This redundancy dilutes critical information, leading to degraded student model performance. To address this, we propose Salient Feature Masking for…
-
Journal Article Full text
Bringing ML to the real world: Rewards are all we need
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision-making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very…
-
Journal Article Full text
A systematic analysis on the relationship between reservoir-computing capacities and physical properties of spin waves
We experimentally demonstrate spin-wave-based reservoir computing (RC) using a device with one-input and nine-output antennas on a yttrium iron garnet film, and we study the relationship between nonlinear phenomena and computational capacities to clarify the mechanism of computing. First, a single…
-
Journal Article Full text
INR-TEM: Robust cavity detection in multifocus TEM images via implicit neural representations
When characterizing materials using transmission electron microscopy (TEM) images, detecting and quantifying small features in microstructures, such as cavities, pose significant challenges. Off-the-shelf object detection models, including YOLOv8, show considerable performance degradation…
-
Journal Article Full text
FE-PINNs: Finite-element-based physics-informed neural networks for surrogate modeling
We present a method whereby the finite element (FE) method is used to train physics-informed neural networks that are suitable for surrogate modeling. The method is based on a new class of mesh-based neural network operators called stenciled operators, which leverage the inverse isoparametric map…
-
Journal Article Full text
Robust learning from literature data: Model generalizability and uncertainty for predicting conjugated polymer solution conformation
Predicting solution conformation and aggregation of conjugated polymers remains a bottleneck for translating solution processing into controlled film microstructures and for closing the loop in self-driving laboratories. We construct a cleaned, machine-readable dataset of 256 entries that links…
-
Journal Article Full text
Structural benchmarks overlook density-of-states errors in machine-learned interatomic potentials
Universal machine-learned interatomic potentials are widely used as fast surrogates for density functional theory in structural modeling. Standard benchmarks emphasize energies, forces, and relaxed geometries. Whether the resulting structures also preserve the density of states remains largely…
-
Journal Article Full text
Graph neural network force fields for adiabatic dynamics of lattice Hamiltonians
Scalable and symmetry-consistent force-field models are essential for extending quantum-accurate simulations to large spatiotemporal scales. While descriptor-based neural networks can incorporate lattice symmetries through carefully engineered features, we show that graph neural networks (GNNs)…
-
Journal Article Full text
Modeling sessile drop interfaces using physics-informed neural networks
We investigate the application of physics-informed neural networks (PINNs) to simulate sessile drops interacting with surfaces exhibiting heterogeneous wettability and contact angle hysteresis. Unlike traditional mesh-based numerical methods, the PINN models the drop shape by minimizing a set of…
-
Journal Article Full text
Explainable distributional structure of MXene compositions revealed by Shapley analysis
Explainable artificial intelligence methods are increasingly essential for extracting physical insight from high-throughput computational materials data. We apply the Shapley behavioral transformation framework to two MXene datasets to characterize how individual compositions contribute to dataset…
-
Journal Article Full text
Persistent homology as efficient phase descriptor for 2D skyrmion lattices
Two-dimensional (2D) particle systems, such as magnetic skyrmions, exhibit topological phase transitions between unique 2D phases. However, a simple and computationally efficient methodology to capture lattice configurational properties and construct an appropriate, easily calculable indicator for…
-
Journal Article Full text
A case study on the performance metrics of integrated photonic computing
Photonic processors use optical signals for computation, leveraging the high bandwidth and low loss of optical links. While many approaches have been proposed, including in-memory photonic circuits, most efforts have focused on the physical design of photonic components rather than full…
-
Journal Article Full text
Collocation geometry creates structural blind spots in physics-informed neural networks
Physics-informed neural networks (PINNs) minimize empirical residuals of governing equations, but a small sampled residual need not certify the structural properties of the underlying partial differential equation (PDE). We examine this issue from a discrete least-squares viewpoint using…
-
Journal Article Full text
Demonstration of machine-learning-based control for femtosecond-level pulse-shaping applications
Advancements in laser-based secondary radiation sources are being made by controlling the pulse shape of ultrafast, high-power laser systems away from its best-compressed level (shortest duration). Pulse shaping at the femtosecond level relies on control over a combination of both the laser…
-
Journal Article Full text
Magnetic field reconstruction from proton radiography via physics-guided out-of-distribution filtering
Reconstructing multidimensional vector fields from path-integrated projection data is a fundamental challenge in high-energy-density physics, particularly when experimental sources exhibit spectral broadening and shot-to-shot jitter. We present a physics-guided deep-learning framework that…
-
Journal Article Full text
Physics-informed neural networks for the point defect model: Solving and inverting passive-film growth kinetics
Predicting how passive oxide films grow and break down is central to corrosion science and the long-term integrity of structural alloys, and the point defect model (PDM) is the standard kinetic description: a stiff, coupled system of Nernst–Planck, Poisson, and Butler–Volmer equations on a moving…
-
Journal Article Full text
Multimodal and cross-modal learning techniques
Scientific machine learning is becoming a powerful approach for accelerating discovery across materials science, chemistry, biology, and autonomous experimentation. However, scientific phenomena are rarely defined by a single observation. In materials science, a material or experiment may be…
-
Journal Article Full text
Automated multiphase identification and refinement in powder diffraction using mismatch-tolerant machine learning
Powder diffraction is a primary structural characterization tool in materials science, yet automated phase identification remains a major bottleneck for autonomous discovery. Existing workflows rely heavily on search–match heuristics and manual Rietveld refinement, and broadly usable end-to-end…
-
Journal Article Full text
A scanner-informed synthetic overlay benchmark dataset for EUV lithography calibration
We present EUV-OVL-SYN, an open and fully reproducible synthetic benchmark dataset for EUV lithography. Overlay error, the misalignment between consecutive patterning layers, is a primary yield-limiting factor at advanced nodes, yet the absence of publicly available, physically realistic datasets…