pwc-archive/papers-with-abstracts huggingface dataset:
Conditional Diffusion-Based Retrieval of Atmospheric CO2 from Earth Observing Spectroscopy
Authors: William R. Keely, Otto Lamminpää, Steffen Mauceri, Sean M. R. Crowell, Christopher W. O'Dell, Gregory R. McGarragh
Date: 2025-04-23 00:00:00
Tasks: Retrieval, Uncertainty Quantification

Satellite-based estimates of greenhouse gas (GHG) properties from observations of reflected solar spectra are integral for understanding and monitoring complex terrestrial systems and their impact on the carbon cycle due to their near global coverage. Known as retrieval, making GHG concentration estimations from these observations is a non-linear Bayesian inverse problem, which is operationally solved using a computationally expensive algorithm called Optimal Estimation (OE), providing a Gaussian approximation to a non-Gaussian posterior. This leads to issues in solver algorithm convergence, and to unrealistically confident uncertainty estimates for the retrieved quantities. Upcoming satellite missions will provide orders of magnitude more data than the current constellation of GHG observers. Development of fast and accurate retrieval algorithms with robust uncertainty quantification is critical. Doing so stands to provide substantial climate impact of moving towards the goal of near continuous real-time global monitoring of carbon sources and sinks which is essential for policy making. To achieve this goal, we propose a diffusion-based approach to flexibly retrieve a Gaussian or non-Gaussian posterior, for NASA's Orbiting Carbon Observatory-2 spectrometer, while providing a substantial computational speed-up over the current operational state-of-the-art.
Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies
Authors: Dishanand Jayeprokash, Julia Gonski
Date: 2025-04-29 00:00:00
Tasks: Anomaly Detection, Data Compression

Small satellite technologies have enhanced the potential and feasibility of geodesic missions, through simplification of design and decreased costs allowing for more frequent launches. On-satellite data acquisition systems can benefit from the implementation of machine learning (ML), for better performance and greater efficiency on tasks such as image processing or feature extraction. This work presents convolutional autoencoders for implementation on the payload of small satellites, designed to achieve dual functionality of data compression for more efficient off-satellite transmission, and at-source anomaly detection to inform satellite data-taking. This capability is demonstrated for a use case of disaster monitoring using aerial image datasets of the African continent, offering avenues for both novel ML-based approaches in small satellite applications along with the expansion of space technology and artificial intelligence in Africa.
Polarized pulse pair observations during a long duration interstellar communication experiment
Authors: William J. Crilly Jr
Date: 2025-04-28 00:00:00

In prior work, conducted since 2017, two celestial pointing directions have been observed to be associated with the measurement of anomalous high counts of narrow bandwidth, short duration, polarized radio frequency pulse pairs. The prior experimental work utilized up to three geographically-spaced synchronized radio telescopes, a single-dish radio telescope, and a radio interferometer. The experimental work reported here examines full right ascension coverage at one declination, utilizing the interferometer, during 124.1 days. Results suggest the possible presence of an additional anomalous celestial pointing direction. Seven standard deviations of noise-modeled shifts of mean polarized pulse pair count were observed in three celestial directions. Indications of interferometer space delay aliasing were observed. A phase noise test, celestial source identification methods and associated measurements were used to seek potential explanations of the unusual observed phenomena.
Fried Parameter Estimation from Single Wavefront Sensor Image with Artificial Neural Networks
Authors: Jeffrey Smith, Taisei Fujii, Jesse Cranney, Charles Gretton
Date: 2025-04-23 00:00:00
Tasks: GPU, parameter estimation

Atmospheric turbulence degrades the quality of astronomical observations in ground-based telescopes, leading to distorted and blurry images. Adaptive Optics (AO) systems are designed to counteract these effects, using atmospheric measurements captured by a wavefront sensor to make real-time corrections to the incoming wavefront. The Fried parameter, r0, characterises the strength of atmospheric turbulence and is an essential control parameter for optimising the performance of AO systems and more recently sky profiling for Free Space Optical (FSO) communication channels. In this paper, we develop a novel data-driven approach, adapting machine learning methods from computer vision for Fried parameter estimation from a single Shack-Hartmann or pyramid wavefront sensor image. Using these data-driven methods, we present a detailed simulation-based evaluation of our approach using the open-source COMPASS AO simulation tool to evaluate both the Shack-Hartmann and pyramid wavefront sensors. Our evaluation is over a range of guide star magnitudes, and realistic noise, atmospheric and instrument conditions. Remarkably, we are able to develop a single network-based estimator that is accurate in both open and closed-loop AO configurations. Our method accurately estimates the Fried parameter from a single WFS image directly from AO telemetry to a few millimetres. Our approach is suitable for real time control, exhibiting 0.83ms r0 inference times on retail NVIDIA RTX 3090 GPU hardware, and thereby demonstrating a compelling economic solution for use in real-time instrument control.
Policy-Based Radiative Transfer: Solving the $2$-Level Atom Non-LTE Problem using Soft Actor-Critic Reinforcement Learning
Authors: Brandon Panos, Ivan Milic
Date: 2025-04-22 00:00:00
Tasks: Reinforcement Learning (RL)

We present a novel reinforcement learning (RL) approach for solving the classical 2-level atom non-LTE radiative transfer problem by framing it as a control task in which an RL agent learns a depth-dependent source function $S(\tau)$ that self-consistently satisfies the equation of statistical equilibrium (SE). The agent's policy is optimized entirely via reward-based interactions with a radiative transfer engine, without explicit knowledge of the ground truth. This method bypasses the need for constructing approximate lambda operators ($\Lambda^*$) common in accelerated iterative schemes. Additionally, it requires no extensive precomputed labeled datasets to extract a supervisory signal, and avoids backpropagating gradients through the complex RT solver itself. Finally, we show through experiment that a simple feedforward neural network trained greedily cannot solve for SE, possibly due to the moving target nature of the problem. Our $\Lambda^*-\text{Free}$ method offers potential advantages for complex scenarios (e.g., atmospheres with enhanced velocity fields, multi-dimensional geometries, or complex microphysics) where $\Lambda^*$ construction or solver differentiability is challenging. Additionally, the agent can be incentivized to find more efficient policies by manipulating the discount factor, leading to a reprioritization of immediate rewards. If demonstrated to generalize past its training data, this RL framework could serve as an alternative or accelerated formalism to achieve SE. To the best of our knowledge, this study represents the first application of reinforcement learning in solar physics that directly solves for a fundamental physical constraint.
Representation Learning on a Random Lattice
Authors: Aryeh Brill
Date: 2025-04-28 00:00:00
Tasks: Representation Learning

Decomposing a deep neural network's learned representations into interpretable features could greatly enhance its safety and reliability. To better understand features, we adopt a geometric perspective, viewing them as a learned coordinate system for mapping an embedded data distribution. We motivate a model of a generic data distribution as a random lattice and analyze its properties using percolation theory. Learned features are categorized into context, component, and surface features. The model is qualitatively consistent with recent findings in mechanistic interpretability and suggests directions for future research.
Neuronal correlations shape the scaling behavior of memory capacity and nonlinear computational capability of recurrent neural networks
Authors: Shotaro Takasu, Toshio Aoyagi
Date: 2025-04-28 00:00:00

Reservoir computing is a powerful framework for real-time information processing, characterized by its high computational ability and quick learning, with applications ranging from machine learning to biological systems. In this paper, we demonstrate that the memory capacity of a reservoir recurrent neural network scales sublinearly with the number of readout neurons. To elucidate this phenomenon, we develop a theoretical framework for analytically deriving memory capacity, attributing the decaying growth of memory capacity to neuronal correlations. In addition, numerical simulations reveal that once memory capacity becomes sublinear, increasing the number of readout neurons successively enables nonlinear processing at progressively higher polynomial orders. Furthermore, our theoretical framework suggests that neuronal correlations govern not only memory capacity but also the sequential growth of nonlinear computational capabilities. Our findings establish a foundation for designing scalable and cost-effective reservoir computing, providing novel insights into the interplay among neuronal correlations, linear memory, and nonlinear processing.
Modeling and Optimization of Transistor Voltage Amplifiers Based on Stochastic Thermodynamics
Authors: Xiaoxuan Peng, Xiaohu Ge
Date: 2025-04-27 00:00:00

As transistor sizes reach the mesoscopic scale, the limitations of traditional methods in ensuring thermodynamic consistency have made power dissipation optimization in transistor amplifiers a critical challenge. Based on stochastic thermodynamics, a transistor voltage amplifier model is first proposed as a new insight to investigate nonlinear relationships between the power dissipation and voltage gain for complementary symmetric voltage amplifier circuits (CSVACs). Utilizing the proposed model, the phenomenon, i.e., the power dissipation exponentially increases with the increase of voltage gain in CSVACs, is first clarified by an analytical expression to quantify the impact of voltage gain and input signal amplitude on the power dissipation. Considering the characteristic of power dissipation, a new multistage architecture is proposed to reduce the power dissipation in CSVACs. To optimize the power dissipation by adjusting the number of stages and the voltage gain at each stage, an optimal multistage scheme is proposed for multistage CSVACs. Simulation and experimental results show up to 99.36% and 94.59% power dissipation reduction compared with traditional CSVACs by the proposed optimal multistage scheme, respectively.
On Simulating Thin-Film Processes at the Atomic Scale Using Machine Learned Force Fields
Authors: S. Kondati Natarajan, J. Schneider, N. Pandey, J. Wellendorff, S. Smidstrup
Date: 2025-05-02 00:00:00

Atomistic modeling of thin-film processes provides an avenue not only for discovering key chemical mechanisms of the processes but also to extract quantitative metrics on the events and reactions taking place at the gas-surface interface. Molecular dynamics (MD) is a powerful computational method to study the evolution of a process at the atomic scale, but studies of industrially relevant processes usually require suitable force fields, which are in general not available for all processes of interest. However, machine learned force fields (MLFF) are conquering the field of computational materials and surface science. In this paper, we demonstrate how to efficiently build MLFFs suitable for process simulations and provide two examples for technologically relevant processes: precursor pulse in the atomic layer deposition of HfO2 and atomic layer etching of MoS2.
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
Authors: Sanjeev Raja, Martin Šípka, Michael Psenka, Tobias Kreiman, Michal Pavelka, Aditi S. Krishnapriyan
Date: 2025-04-25 00:00:00
Tasks: Denoising

Transition path sampling (TPS), which involves finding probable paths connecting two points on an energy landscape, remains a challenge due to the complexity of real-world atomistic systems. Current machine learning approaches use expensive, task-specific, and data-free training procedures, limiting their ability to benefit from high-quality datasets and large-scale pre-trained models. In this work, we address TPS by interpreting candidate paths as trajectories sampled from stochastic dynamics induced by the learned score function of pre-trained generative models, specifically denoising diffusion and flow matching. Under these dynamics, finding high-likelihood transition paths becomes equivalent to minimizing the Onsager-Machlup (OM) action functional. This enables us to repurpose pre-trained generative models for TPS in a zero-shot manner, in contrast with bespoke, task-specific approaches in previous work. We demonstrate our approach on varied molecular systems, obtaining diverse, physically realistic transition pathways and generalizing beyond the pre-trained model's original training dataset. Our method can be easily incorporated into new generative models, making it practically relevant as models continue to scale and improve with increased data availability. Code is available at github.com/ASK-Berkeley/OM-TPS.
Transition States Energies from Machine Learning: An Application to Reverse Water-Gas Shift on Single-Atom Alloys
Authors: Raffaele Cheula, Mie Andersen
Date: 2025-05-01 00:00:00
Tasks: GPR

Obtaining accurate transition state (TS) energies is a bottleneck in computational screening of complex materials and reaction networks due to the high cost of TS search methods and first-principles methods such as density functional theory (DFT). Here we propose a machine learning (ML) model for predicting TS energies based on Gaussian process regression with the Wasserstein Weisfeiler-Lehman graph kernel (WWL-GPR). Applying the model to predict adsorption and TS energies for the reverse water-gas shift (RWGS) reaction on single-atom alloy (SAA) catalysts, we show that it can significantly improve the accuracy compared to traditional approaches based on scaling relations or ML models without a graph representation. Further benefitting from the low cost of model training, we train an ensemble of WWL-GPR models to obtain uncertainties through subsampling of the training data and show how these uncertainties propagate to turnover frequency (TOF) predictions through the construction of an ensemble of microkinetic models. Comparing the errors in model-based vs DFT-based TOF predictions, we show that the WWL-GPR model reduces errors by almost an order of magnitude compared to scaling relations. This demonstrates the critical impact of accurate energy predictions on catalytic activity estimation. Finally, we apply our model to screen new materials, identifying promising catalysts for RWGS. This work highlights the power of combining advanced ML techniques with DFT and microkinetic modeling for screening catalysts for complex reactions like RWGS, providing a robust framework for future catalyst design.
Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy
Authors: Aditya Vatsavai, Ganesh Narasimha, Yongtao Liu, Jan-Chi Yang, Hiroshu Funakubo, Maxim Ziatdinov, Rama Vasudevan
Date: 2025-04-28 00:00:00
Tasks: Active Learning

Rapidly determining structure-property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure-property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure-property relationships in materials science.
Graph Neural Network Prediction of Nonlinear Optical Properties
Authors: Yomn Alkabakibi, Congwei Xie, Artem R. Oganov
Date: 2025-04-28 00:00:00
Tasks: Graph Neural Network, Prediction

Nonlinear optical (NLO) materials for generating lasers via second harmonic generation (SHG) are highly sought in today's technology. However, discovering novel materials with considerable SHG is challenging due to the time-consuming and costly nature of both experimental methods and first-principles calculations. In this study, we present a deep learning approach using the Atomistic Line Graph Neural Network (ALIGNN) to predict NLO properties. Sourcing data from the Novel Opto-Electronic Materials Discovery (NOEMD) database and using the Kurtz-Perry (KP) coefficient as the key target, we developed a robust model capable of accurately estimating nonlinear optical responses. Our results demonstrate that the model achieves 82.5% accuracy at a tolerated absolute error up to 1 pm/V and relative error not exceeding 0.5. This work highlights the potential of deep learning in accelerating the discovery and design of advanced optical materials with desired properties.
Interpretable machine learning-guided design of Fe-based soft magnetic alloys
Authors: Aditi Nachnani, Kai K. Li-Caldwell, Saptarshi Biswas, Prince Sharma, Gaoyuan Ouyang, Prashant Singh
Date: 2025-04-28 00:00:00
Tasks: Feature Importance, Interpretable Machine Learning, Uncertainty Quantification

We present a machine-learning guided approach to predict saturation magnetization (MS) and coercivity (HC) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveals that increasing Si and B content reduces MS from 1.81T (DFT~2.04 T) to ~1.54 T (DFT~1.56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09T), Fe-5Si-5B (2.01T) and Fe-10Si-10B (1.54T) alloy compositions further support our findings. These trends are consistent with density functional theory (DFT) predictions, which link increased electronic disorder and band broadening to lower MS values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveals that MS is governed by a nonlinear interplay between Fe content, early transition metal ratios, and annealing temperature, while HC is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudo-quaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe84.8Si0.5B9.4Cu0.8 P3.5C1), FINEMET (Fe73.5Si13.5B9 Cu1Nb3), NANOPERM (Fe88Zr7B4Cu1), and HITPERM (Fe44Co44Zr7B4Cu1. Our fundings demonstrate the potential of ML framework for accelerated search of high-performance, Co- and Ni-free, soft magnetic materials.
Supervised Pretraining for Material Property Prediction
Authors: Chowdhury Mohammad Abid Rahman, Aldo H. Romero, Prashnna K. Gyawali
Date: 2025-04-27 00:00:00
Tasks: Prediction, Property Prediction, Representation Learning, Self-Supervised Learning

Accurate prediction of material properties facilitates the discovery of novel materials with tailored functionalities. Deep learning models have recently shown superior accuracy and flexibility in capturing structure-property relationships. However, these models often rely on supervised learning, which requires large, well-annotated datasets an expensive and time-consuming process. Self-supervised learning (SSL) offers a promising alternative by pretraining on large, unlabeled datasets to develop foundation models that can be fine-tuned for material property prediction. In this work, we propose supervised pretraining, where available class information serves as surrogate labels to guide learning, even when downstream tasks involve unrelated material properties. We evaluate this strategy on two state-of-the-art SSL models and introduce a novel framework for supervised pretraining. To further enhance representation learning, we propose a graph-based augmentation technique that injects noise to improve robustness without structurally deforming material graphs. The resulting foundation models are fine-tuned for six challenging material property predictions, achieving significant performance gains over baselines, ranging from 2% to 6.67% improvement in mean absolute error (MAE) and establishing a new benchmark in material property prediction. This study represents the first exploration of supervised pertaining with surrogate labels in material property prediction, advancing methodology and application in the field.
Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomography
Authors: Tristan Manchester, Adam Anders, Julio Spadotto, Hannah Eccleston, William Beavan, Hugues Arcis, Brian J. Connolly
Date: 2025-04-27 00:00:00
Tasks: Segmentation

In situ synchrotron X-ray computed tomography enables dynamic material studies, but automated segmentation remains challenging due to complex imaging artefacts and limited training data. We present a methodology for deep learning-based segmentation by transforming high-quality ex situ laboratory data to train models for binary segmentation of in situ synchrotron data, demonstrated through copper oxide dissolution studies. Using a modified SegFormer architecture, our approach achieves high segmentation performance on unseen data while reducing processing time from hours to seconds per 3D dataset. The method maintains consistent performance over significant morphological changes during experiments, despite training only on static specimens. This methodology can be readily applied to diverse materials systems, accelerating the analysis of time-resolved tomographic data across scientific disciplines.