pwc-archive/papers-with-abstracts huggingface dataset:
A practical guide to machine learning interatomic potentials -- Status and future
Authors: Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng, Chen Shen, Zhenghao Wu, Clare Yijia Xie, Julia H. Yang, Nongnuch Artrith, Ben Blaiszik, Gerbrand Ceder, Kamal Choudhary, Gabor Csanyi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, KJ Schmidt, So Takamoto, Aidan Thompson, Julia Westermayr, Brandon M. Wood
Date: 2025-03-12 00:00:00

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years.
A novel metric for species vulnerability and coexistence in spatially-extended ecosystems
Authors: Davide Bernardi, Giorgio Nicoletti, Prajwal Padmanabha, Samir Suweis, Sandro Azaele, Andrea Rinaldo, Amos Maritan
Date: 2025-03-13 00:00:00

We develop a theoretical framework to understand the persistence and coexistence of competitive species in a spatially explicit metacommunity model with a heterogeneous dispersal kernel. Our analysis, based on methods from the physics of disordered systems and non-Gaussian dynamical mean field theory, reveals that species coexistence is governed by a single key parameter, which we term competitive balance. From competitive balance, we derive a novel metric to quantitatively assess the vulnerability of a species, showing that abundance alone is not sufficient to determine it. Rather, a species' vulnerability crucially depends on the state of the metacommunity as a whole. We test our theory by analyzing two distinct tropical forest datasets, finding excellent agreement with our theoretical predictions. A key step in our analysis is the introduction of a new quantity - the competitive score - which disentangles the abundance distribution and enables us to circumvent the challenge of estimating both the colonization kernel and the joint abundance distribution. Our findings provide novel and fundamental insights into the ecosystem-level trade-offs underlying macroecological patterns and introduce a robust approach for estimating extinction risks.
Thermodynamic bounds on energy use in Deep Neural Networks
Authors: Alexei V. Tkachenko
Date: 2025-03-13 00:00:00

While Landauer's principle sets a fundamental energy limit for irreversible digital computation, we show that Deep Neural Networks (DNNs) implemented on analog physical substrates can operate under markedly different thermodynamic constraints. We distinguish between two classes of analog systems: dynamic and quasi-static. In dynamic systems, energy dissipation arises from neuron resets, with a lower bound governed by Landauer's principle. To analyse a quasi-static analog platform, we construct an explicit mapping of a generic feedforward DNN onto a physical system described by a model Hamiltonian. In this framework, inference can proceed reversibly, with no minimum free energy cost imposed by thermodynamics. We further analyze the training process in quasi-static analog networks and derive a fundamental lower bound on its energy cost, rooted in the interplay between thermal and statistical noise. Our results suggest that while analog implementations can outperform digital ones during inference, the thermodynamic cost of training scales similarly in both paradigms.
Rapid analysis of point-contact Andreev reflection spectra via machine learning with adaptive data augmentation
Authors: Dongik Lee, Valentin Stanev, Xiaohang Zhang, Mijeong Kang, Ichiro Takeuchi, Seunghun Lee
Date: 2025-03-13 00:00:00
Tasks: Data Augmentation

Delineating the superconducting order parameters is a pivotal task in investigating superconductivity for probing pairing mechanisms, as well as their symmetry and topology. Point-contact Andreev reflection (PCAR) measurement is a simple yet powerful tool for identifying the order parameters. The PCAR spectra exhibit significant variations depending on the type of the order parameter in a superconductor, including its magnitude ($\mathit{\Delta}$), as well as temperature, interfacial quality, Fermi velocity mismatch, and other factors. The information on the order parameter can be obtained by finding the combination of these parameters, generating a theoretical spectrum that fits a measured experimental spectrum. However, due to the complexity of the spectra and the high dimensionality of parameters, extracting the fitting parameters is often time-consuming and labor-intensive. In this study, we employ a convolutional neural network (CNN) algorithm to create models for rapid and automated analysis of PCAR spectra of various superconductors with different pairing symmetries (conventional $s$-wave, chiral $p_x+ip_y$-wave, and $d_{x^2-y^2}$-wave). The training datasets are generated based on the Blonder-Tinkham-Klapwijk (BTK) theory and further modified and augmented by selectively incorporating noise and peaks according to the bias voltages. This approach not only replicates the experimental spectra but also brings the model's attention to important features within the spectra. The optimized models provide fitting parameters for experimentally measured spectra in less than 100 ms per spectrum. Our approaches and findings pave the way for rapid and automated spectral analysis which will help accelerate research on superconductors with complex order parameters.
Deep Learning based discovery of Integrable Systems
Authors: Shailesh Lal, Suvajit Majumder, Evgeny Sobko
Date: 2025-03-13 00:00:00
Tasks: Deep Learning, Form

We introduce a novel machine learning based framework for discovering integrable models. Our approach first employs a synchronized ensemble of neural networks to find high-precision numerical solution to the Yang-Baxter equation within a specified class. Then, using an auxiliary system of algebraic equations, [Q_2, Q_3] = 0, and the numerical value of the Hamiltonian obtained via deep learning as a seed, we reconstruct the entire Hamiltonian family, forming an algebraic variety. We illustrate our presentation with three- and four-dimensional spin chains of difference form with local interactions. Remarkably, all discovered Hamiltonian families form rational varieties.
Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach
Authors: Anwar Ibrahim, Denis Derkach, Alexey Petrenko, Fedor Ratnikov, Maxim Kaledin
Date: 2025-03-12 00:00:00
Tasks: Reinforcement Learning (RL)

Optimizing accelerator control is a critical challenge in experimental particle physics, requiring significant manual effort and resource expenditure. Traditional tuning methods are often time-consuming and reliant on expert input, highlighting the need for more efficient approaches. This study aims to create a simulation-based framework integrated with Reinforcement Learning (RL) to address these challenges. Using \texttt{Elegant} as the simulation backend, we developed a Python wrapper that simplifies the interaction between RL algorithms and accelerator simulations, enabling seamless input management, simulation execution, and output analysis. The proposed RL framework acts as a co-pilot for physicists, offering intelligent suggestions to enhance beamline performance, reduce tuning time, and improve operational efficiency. As a proof of concept, we demonstrate the application of our RL approach to an accelerator control problem and highlight the improvements in efficiency and performance achieved through our methodology. We discuss how the integration of simulation tools with a Python-based RL framework provides a powerful resource for the accelerator physics community, showcasing the potential of machine learning in optimizing complex physical systems.
Predicting Tropical Cyclone Track Forecast Errors using a Probabilistic Neural Network
Authors: M. A. Fernandez, Elizabeth A. Barnes, Randal J. Barnes, Mark DeMaria, Marie McGraw, Galina Chirokova, Lixin Lu
Date: 2025-03-12 00:00:00

A new method for estimating tropical cyclone track uncertainty is presented and tested. This method uses a neural network to predict a bivariate normal distribution, which serves as an estimate for track uncertainty. We train the network and make predictions on forecasts from the National Hurricane Center (NHC), which currently uses static error distributions based on forecasts from the past five years for most applications. The neural network-based method produces uncertainty estimates that are dynamic and probabilistic. Further, the neural network-based method allows for probabilistic statements about tropical cyclone trajectories, including landfall probability, which we highlight. We show that our predictions are well calibrated using multiple metrics, that our method produces better uncertainty estimates than current NHC approaches, and that our method achieves similar performance to the Global Ensemble Forecast System. Once trained, the computational cost of predictions using this method is negligible, making it a strong candidate to improve the NHC's operational estimations of tropical cyclone track uncertainty.
Predicting Chemical Reaction Outcomes Based on Electron Movements Using Machine Learning
Authors: Shuan Chen, Kye Sung Park, Taewan Kim, Sunkyu Han, Yousung Jung
Date: 2025-03-13 00:00:00

Accurately predicting chemical reaction outcomes and potential byproducts is a fundamental task of modern chemistry, enabling the efficient design of synthetic pathways and driving progress in chemical science. Reaction mechanism, which tracks electron movements during chemical reactions, is critical for understanding reaction kinetics and identifying unexpected products. Here, we present Reactron, the first electron-based machine learning model for general reaction prediction. Reactron integrates electron movement into its predictions, generating detailed arrow-pushing diagrams that elucidate each mechanistic step leading to product formation. We demonstrate the high predictive performance of Reactron over existing product-only models by a large-scale reaction outcome prediction benchmark, and the adaptability of the model to learn new reactivity upon providing a few examples. Furthermore, it explores combinatorial reaction spaces, uncovering novel reactivities beyond its training data. With robust performance in both in- and out-of-distribution predictions, Reactron embodies human-like reasoning in chemistry and opens new frontiers in reaction discovery and synthesis design.
Learning second-order TVD flux limiters using differentiable solvers
Authors: Chenyang Huang, Amal S. Sebastian, Venkatasubramanian Viswanathan
Date: 2025-03-11 00:00:00

This paper presents a data-driven framework for learning optimal second-order total variation diminishing (TVD) flux limiters via differentiable simulations. In our fully differentiable finite volume solvers, the limiter functions are replaced by neural networks. By representing the limiter as a pointwise convex linear combination of the Minmod and Superbee limiters, we enforce both second-order accuracy and TVD constraints at all stages of training. Our approach leverages gradient-based optimization through automatic differentiation, allowing a direct backpropagation of errors from numerical solutions to the limiter parameters. We demonstrate the effectiveness of this method on various hyperbolic conservation laws, including the linear advection equation, the Burgers' equation, and the one-dimensional Euler equations. Remarkably, a limiter trained solely on linear advection exhibits strong generalizability, surpassing the accuracy of most classical flux limiters across a range of problems with shocks and discontinuities. The learned flux limiters can be readily integrated into existing computational fluid dynamics codes, and the proposed methodology also offers a flexible pathway to systematically develop and optimize flux limiters for complex flow problems.
Data augmentation using diffusion models to enhance inverse Ising inference
Authors: Yechan Lim, Sangwon Lee, Junghyo Jo
Date: 2025-03-13 00:00:00
Tasks: Data Augmentation, Missing Values

Identifying model parameters from observed configurations poses a fundamental challenge in data science, especially with limited data. Recently, diffusion models have emerged as a novel paradigm in generative machine learning, capable of producing new samples that closely mimic observed data. These models learn the gradient of model probabilities, bypassing the need for cumbersome calculations of partition functions across all possible configurations. We explore whether diffusion models can enhance parameter inference by augmenting small datasets. Our findings demonstrate this potential through a synthetic task involving inverse Ising inference and a real-world application of reconstructing missing values in neural activity data. This study serves as a proof-of-concept for using diffusion models for data augmentation in physics-related problems, thereby opening new avenues in data science.
Why the Brain Cannot Be a Digital Computer: History-Dependence and the Computational Limits of Consciousness
Authors: Andrew Knight
Date: 2025-03-13 00:00:00

This paper presents a novel information-theoretic proof demonstrating that the human brain as currently understood cannot function as a classical digital computer. Through systematic quantification of distinguishable conscious states and their historical dependencies, we establish that the minimum information required to specify a conscious state exceeds the physical information capacity of the human brain by a significant factor. Our analysis calculates the bit-length requirements for representing consciously distinguishable sensory "stimulus frames" and demonstrates that consciousness exhibits mandatory temporal-historical dependencies that multiply these requirements beyond the brain's storage capabilities. This mathematical approach offers new insights into the fundamental limitations of computational models of consciousness and suggests that non-classical information processing mechanisms may be necessary to account for conscious experience.
How Should We Evaluate Uncertainty in Accelerated MRI Reconstruction?
Authors: Luca Trautmann, Peter Wijeratne, Itamar Ronen, Ivor Simpson
Date: 2025-03-13 00:00:00
Tasks: Image Registration, MRI Reconstruction, SSIM

Reconstructing accelerated MRI is an ill-posed problem. Machine learning has recently shown great promise at this task, but current approaches to quantifying uncertainty focus on measuring the variability in pixelwise intensity variation. Although these provide interpretable maps, they lack structural understanding and they do not have a clear relationship to how the data will be analysed subsequently. In this paper, we propose a new approach to evaluating reconstruction variability based on apparent anatomical changes in the reconstruction, which is more tightly related to common downstream tasks. We use image registration and segmentation to evaluate several common MRI reconstruction approaches, where uncertainty is measured via ensembling, for accelerated imaging. We demonstrate the intrinsic variability in reconstructed images and show that models with high scores on often used quality metrics such as SSIM and PSNR, can nonetheless display high levels of variance and bias in anatomical measures.
A PyTorch-Enabled Tool for Synthetic Event Camera Data Generation and Algorithm Development
Authors: Joseph L. Greene, Adrish Kar, Ignacio Galindo, Elijah Quiles, Elliott Chen, Matthew Anderson
Date: 2025-03-12 00:00:00

Event, or neuromorphic cameras, offer a novel encoding of natural scenes by asynchronously reporting significant changes in brightness, known as events, with improved dynamic range, temporal resolution and lower data bandwidth when compared to conventional cameras. However, their adoption in domain-specific research tasks is hindered in part by limited commercial availability, lack of existing datasets, and challenges related to predicting the impact of their nonlinear optical encoding, unique noise model and tensor-based data processing requirements. To address these challenges, we introduce Synthetic Events for Neural Processing and Integration (SENPI) in Python, a PyTorch-based library for simulating and processing event camera data. SENPI includes a differentiable digital twin that converts intensity-based data into event representations, allowing for evaluation of event camera performance while handling the non-smooth and nonlinear nature of the forward model The library also supports modules for event-based I/O, manipulation, filtering and visualization, creating efficient and scalable workflows for both synthetic and real event-based data. We demonstrate SENPI's ability to produce realistic event-based data by comparing synthetic outputs to real event camera data and use these results to draw conclusions on the properties and utility of event-based perception. Additionally, we showcase SENPI's use in exploring event camera behavior under varying noise conditions and optimizing event contrast threshold for improved encoding under target conditions. Ultimately, SENPI aims to lower the barrier to entry for researchers by providing an accessible tool for event data generation and algorithmic developmnent, making it a valuable resource for advancing research in neuromorphic vision systems.
Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification
Authors: Rajiv Krishnakumar, Julien Baglio, Frederik F. Flöther, Christian Ruiz, Stefan Habringer, Nicole H. Romano
Date: 2025-03-13 00:00:00
Tasks: image-classification, Image Classification, Multiple Instance Learning

Whole-slide image classification represents a key challenge in computational pathology and medicine. Attention-based multiple instance learning (MIL) has emerged as an effective approach for this problem. However, the effect of attention mechanism architecture on model performance is not well-documented for biomedical imagery. In this work, we compare different methods and implementations of MIL, including deep learning variants. We introduce a new method using higher-dimensional feature spaces for deep MIL. We also develop a novel algorithm for whole-slide image classification where extreme machine learning is combined with attention-based MIL to improve sensitivity and reduce training complexity. We apply our algorithms to the problem of detecting circulating rare cells (CRCs), such as erythroblasts, in peripheral blood. Our results indicate that nonlinearities play a key role in the classification, as removing them leads to a sharp decrease in stability in addition to a decrease in average area under the curve (AUC) of over 4%. We also demonstrate a considerable increase in robustness of the model with improvements of over 10% in average AUC when higher-dimensional feature spaces are leveraged. In addition, we show that extreme learning machines can offer clear improvements in terms of training efficiency by reducing the number of trained parameters by a factor of 5 whilst still maintaining the average AUC to within 1.5% of the deep MIL model. Finally, we discuss options of enriching the classical computing framework with quantum algorithms in the future. This work can thus help pave the way towards more accurate and efficient single-cell diagnostics, one of the building blocks of precision medicine.
PLRD: Partially Linear Regression Discontinuity Inference
Authors: Aditya Ghosh, Guido Imbens, Stefan Wager
Date: 2025-03-12 00:00:00
Tasks: regression, valid

Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that widely used approaches to building confidence intervals in regression discontinuity designs exhibit suboptimal behavior in practice: In a simulation study calibrated to high-profile applications of regression discontinuity designs, existing methods either have systematic under-coverage or have wider-than-necessary intervals. We propose a new approach, partially linear regression discontinuity inference (PLRD), and find it to address shortcomings of existing methods: Throughout our experiments, confidence intervals built using PLRD are both valid and short. We also provide large-sample guarantees for PLRD under smoothness assumptions.
Designing Graph Convolutional Neural Networks for Discrete Choice with Network Effects
Authors: Daniel F. Villarraga, Ricardo A. Daziano
Date: 2025-03-12 00:00:00
Tasks: Discrete Choice Models

We introduce a novel model architecture that incorporates network effects into discrete choice problems, achieving higher predictive performance than standard discrete choice models while offering greater interpretability than general-purpose flexible model classes. Econometric discrete choice models aid in studying individual decision-making, where agents select the option with the highest reward from a discrete set of alternatives. Intuitively, the utility an individual derives from a particular choice depends on their personal preferences and characteristics, the attributes of the alternative, and the value their peers assign to that alternative or their previous choices. However, most applications ignore peer influence, and models that do consider peer or network effects often lack the flexibility and predictive performance of recently developed approaches to discrete choice, such as deep learning. We propose a novel graph convolutional neural network architecture to model network effects in discrete choices, achieving higher predictive performance than standard discrete choice models while retaining the interpretability necessary for inference--a quality often lacking in general-purpose deep learning architectures. We evaluate our architecture using revealed commuting choice data, extended with travel times and trip costs for each travel mode for work-related trips in New York City, as well as 2016 U.S. election data aggregated by county, to test its performance on datasets with highly imbalanced classes. Given the interpretability of our models, we can estimate relevant economic metrics, such as the value of travel time savings in New York City. Finally, we compare the predictive performance and behavioral insights from our architecture to those derived from traditional discrete choice and general-purpose deep learning models.