pwc-archive/papers-with-abstracts huggingface dataset:
PET-MAD, a universal interatomic potential for advanced materials modeling
Authors: Arslan Mazitov, Filippo Bigi, Matthias Kellner, Paolo Pegolo, Davide Tisi, Guillaume Fraux, Sergey Pozdnyakov, Philip Loche, Michele Ceriotti
Date: 2025-03-18 00:00:00
Tasks: Diversity

Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the effort. Leveraging large quantum mechanical databases and expressive architectures, recent "universal" models deliver qualitative accuracy across the periodic table but are often biased toward low-energy configurations. We introduce PET-MAD, a generally applicable MLIP trained on a dataset combining stable inorganic and organic solids, systematically modified to enhance atomic diversity. Using a moderate but highly-consistent level of electronic-structure theory, we assess PET-MAD's accuracy on established benchmarks and advanced simulations of six materials. PET-MAD rivals state-of-the-art MLIPs for inorganic solids, while also being reliable for molecules, organic materials, and surfaces. It is stable and fast, enabling, out-of-the-box, the near-quantitative study of thermal and quantum mechanical fluctuations, functional properties, and phase transitions. It can be efficiently fine-tuned to deliver full quantum mechanical accuracy with a minimal number of targeted calculations.
Causal Discovery from Data Assisted by Large Language Models
Authors: Kamyar Barakati, Alexander Molak, Chris Nelson, Xiaohang Zhang, Ichiro Takeuchi, Sergei V. Kalinin
Date: 2025-03-18 00:00:00
Tasks: Causal Discovery

Knowledge driven discovery of novel materials necessitates the development of the causal models for the property emergence. While in classical physical paradigm the causal relationships are deduced based on the physical principles or via experiment, rapid accumulation of observational data necessitates learning causal relationships between dissimilar aspects of materials structure and functionalities based on observations. For this, it is essential to integrate experimental data with prior domain knowledge. Here we demonstrate this approach by combining high-resolution scanning transmission electron microscopy (STEM) data with insights derived from large language models (LLMs). By fine-tuning ChatGPT on domain-specific literature, such as arXiv papers on ferroelectrics, and combining obtained information with data-driven causal discovery, we construct adjacency matrices for Directed Acyclic Graphs (DAGs) that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO3 (SmBFO). This approach enables us to hypothesize how synthesis conditions influence material properties, particularly the coercive field (E0), and guides experimental validation. The ultimate objective of this work is to develop a unified framework that integrates LLM-driven literature analysis with data-driven discovery, facilitating the precise engineering of ferroelectric materials by establishing clear connections between synthesis conditions and their resulting material properties.
Leveraging Vision Capabilities of Multimodal LLMs for Automated Data Extraction from Plots
Authors: Maciej P. Polak, Dane Morgan
Date: 2025-03-16 00:00:00

Automated data extraction from research texts has been steadily improving, with the emergence of large language models (LLMs) accelerating progress even further. Extracting data from plots in research papers, however, has been such a complex task that it has predominantly been confined to manual data extraction. We show that current multimodal large language models, with proper instructions and engineered workflows, are capable of accurately extracting data from plots. This capability is inherent to the pretrained models and can be achieved with a chain-of-thought sequence of zero-shot engineered prompts we call PlotExtract, without the need to fine-tune. We demonstrate PlotExtract here and assess its performance on synthetic and published plots. We consider only plots with two axes in this analysis. For plots identified as extractable, PlotExtract finds points with over 90% precision (and around 90% recall) and errors in x and y position of around 5% or lower. These results prove that multimodal LLMs are a viable path for high-throughput data extraction for plots and in many circumstances can replace the current manual methods of data extraction.
Landscape Complexity for the Empirical Risk of Generalized Linear Models: Discrimination between Structured Data
Authors: Theodoros G. Tsironis, Aris L. Moustakas
Date: 2025-03-18 00:00:00

We use the Kac-Rice formula and results from random matrix theory to obtain the average number of critical points of a family of high-dimensional empirical loss functions, where the data are correlated $d$-dimensional Gaussian vectors, whose number has a fixed ratio with their dimension. The correlations are introduced to model the existence of structure in the data, as is common in current Machine-Learning systems. Under a technical hypothesis, our results are exact in the large-$d$ limit, and characterize the annealed landscape complexity, namely the logarithm of the expected number of critical points at a given value of the loss. We first address in detail the landscape of the loss function of a single perceptron and then generalize it to the case where two competing data sets with different covariance matrices are present, with the perceptron seeking to discriminate between them. The latter model can be applied to understand the interplay between adversity and non-trivial data structure. For completeness, we also treat the case of a loss function used in training Generalized Linear Models in the presence of correlated input data.
High-entropy Advantage in Neural Networks' Generalizability
Authors: Entao Yang, Xiaotian Zhang, Yue Shang, Ge Zhang
Date: 2025-03-17 00:00:00
Tasks: Language Modeling, Language Modelling

One of the central challenges in modern machine learning is understanding how neural networks generalize knowledge learned from training data to unseen test data. While numerous empirical techniques have been proposed to improve generalization, a theoretical understanding of the mechanism of generalization remains elusive. Here we introduce the concept of Boltzmann entropy into neural networks by re-conceptualizing such networks as hypothetical molecular systems where weights and biases are atomic coordinates, and the loss function is the potential energy. By employing molecular simulation algorithms, we compute entropy landscapes as functions of both training loss and test accuracy (or test loss), on networks with up to 1 million parameters, across four distinct machine learning tasks: arithmetic question, real-world tabular data, image recognition, and language modeling. Our results reveal the existence of high-entropy advantage, wherein high-entropy network states generally outperform those reached via conventional training techniques like stochastic gradient descent. This entropy advantage provides a thermodynamic explanation for neural network generalizability: the generalizable states occupy a larger part of the parameter space than its non-generalizable analog at low train loss. Furthermore, we find this advantage more pronounced in narrower neural networks, indicating a need for different training optimizers tailored to different sizes of networks.
End-to-End Optimal Detector Design with Mutual Information Surrogates
Authors: Kinga Anna Wozniak, Stephen Mulligan, Jan Kieseler, Markus Klute, Francois Fleuret, Tobias Golling
Date: 2025-03-18 00:00:00

We introduce a novel approach for end-to-end black-box optimization of high energy physics (HEP) detectors using local deep learning (DL) surrogates. These surrogates approximate a scalar objective function that encapsulates the complex interplay of particle-matter interactions and physics analysis goals. In addition to a standard reconstruction-based metric commonly used in the field, we investigate the information-theoretic metric of mutual information. Unlike traditional methods, mutual information is inherently task-agnostic, offering a broader optimization paradigm that is less constrained by predefined targets. We demonstrate the effectiveness of our method in a realistic physics analysis scenario: optimizing the thicknesses of calorimeter detector layers based on simulated particle interactions. The surrogate model learns to approximate objective gradients, enabling efficient optimization with respect to energy resolution. Our findings reveal three key insights: (1) end-to-end black-box optimization using local surrogates is a practical and compelling approach for detector design, providing direct optimization of detector parameters in alignment with physics analysis goals; (2) mutual information-based optimization yields design choices that closely match those from state-of-the-art physics-informed methods, indicating that these approaches operate near optimality and reinforcing their reliability in HEP detector design; and (3) information-theoretic methods provide a powerful, generalizable framework for optimizing scientific instruments. By reframing the optimization process through an information-theoretic lens rather than domain-specific heuristics, mutual information enables the exploration of new avenues for discovery beyond conventional approaches.
Modeling Epidemics with Memory Effects: an Open Quantum System Approach
Authors: Fabio Bagarello, Francesco Gargano, Polina Khrennikova
Date: 2025-03-18 00:00:00

In this work, we introduce a quantum-inspired epidemic model to study the dynamics of an infectious disease in a population divided into compartments. By treating the healthy population as a large reservoir, we construct a framework based on open quantum systems and a Hilbert space formalism to model the spread of the infection. This approach allows for a mathematical framework that captures both Markovian and semi-Markovian dynamics in the evolution equations. Through numerical experiments, we examine the impact of varying memory parameters on the epidemic evolution, focusing in particular on the conditions under which the model remains physically admissible.
Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study
Authors: Adrita Ghosh, Parthasakha Das, Tanujit Chakraborty, Pritha Das, Dibakar Ghosh
Date: 2025-03-18 00:00:00

Cholera, an acute diarrheal disease, is a serious concern in developing and underdeveloped areas. A qualitative understanding of cholera epidemics aims to foresee transmission patterns based on reported data and mechanistic models. The mechanistic model is a crucial tool for capturing the dynamics of disease transmission and population spread. However, using real-time cholera cases is essential for forecasting the transmission trend. This prospective study seeks to furnish insights into transmission trends through qualitative dynamics followed by machine learning-based forecasting. The Monte Carlo Markov Chain approach is employed to calibrate the proposed mechanistic model. We identify critical parameters that illustrate the disease's dynamics using partial rank correlation coefficient-based sensitivity analysis. The basic reproduction number as a crucial threshold measures asymptotic dynamics. Furthermore, forward bifurcation directs the stability of the infection state, and Hopf bifurcation suggests that trends in transmission may become unpredictable as societal disinfection rates rise. Further, we develop epidemic-informed machine learning models by incorporating mechanistic cholera dynamics into autoregressive integrated moving averages and autoregressive neural networks. We forecast short-term future cholera cases in Malawi by implementing the proposed epidemic-informed machine learning models to support this. We assert that integrating temporal dynamics into the machine learning models can enhance the capabilities of cholera forecasting models. The execution of this mechanism can significantly influence future trends in cholera transmission. This evolving approach can also be beneficial for policymakers to interpret and respond to potential disease systems. Moreover, our methodology is replicable and adaptable, encouraging future research on disease dynamics.
Towards Location-Specific Precipitation Projections Using Deep Neural Networks
Authors: Bipin Kumar, Bhvisy Kumar Yadav, Soumypdeep Mukhopadhyay, Rakshit Rohan, Bhupendra Bahadur Singh, Rajib Chattopadhyay, Nagraju Chilukoti, Atul Kumar Sahai
Date: 2025-03-18 00:00:00
Tasks: Weather Forecasting

Accurate precipitation estimates at individual locations are crucial for weather forecasting and spatial analysis. This study presents a paradigm shift by leveraging Deep Neural Networks (DNNs) to surpass traditional methods like Kriging for station-specific precipitation approximation. We propose two innovative NN architectures: one utilizing precipitation, elevation, and location, and another incorporating additional meteorological parameters like humidity, temperature, and wind speed. Trained on a vast dataset (1980-2019), these models outperform Kriging across various evaluation metrics (correlation coefficient, root mean square error, bias, and skill score) on a five-year validation set. This compelling evidence demonstrates the transformative power of deep learning for spatial prediction, offering a robust and precise alternative for station-specific precipitation estimation.
RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall Downscaling
Authors: Marcello Iotti, Paolo Davini, Jost von Hardenberg, Giuseppe Zappa
Date: 2025-03-17 00:00:00
Tasks: Generative Adversarial Network, Image Super-Resolution, Super-Resolution

To this day, accurately simulating local-scale precipitation and reliably reproducing its distribution remains a challenging task. The limited horizontal resolution of Global Climate Models is among the primary factors undermining their skill in this context. The physical mechanisms driving the onset and development of precipitation, especially in extreme events, operate at spatio-temporal scales smaller than those numerically resolved, thus struggling to be captured accurately. In order to circumvent this limitation, several downscaling approaches have been developed over the last decades to address the discrepancy between the spatial resolution of models output and the resolution required by local-scale applications. In this paper, we introduce RainScaleGAN, a conditional deep convolutional Generative Adversarial Network (GAN) for precipitation downscaling. GANs have been effectively used in image super-resolution, an approach highly relevant for downscaling tasks. RainScaleGAN's capabilities are tested in a perfect-model setup, where the spatial resolution of a precipitation dataset is artificially degraded from 0.25$^{\circ}\times$0.25$^{\circ}$ to 2$^{\circ}\times$2$^\circ$, and RainScaleGAN is used to restore it. The developed model outperforms one of the leading precipitation downscaling method found in the literature. RainScaleGAN not only generates a synthetic dataset featuring plausible high-resolution spatial patterns and intensities, but also produces a precipitation distribution with statistics closely mirroring those of the ground-truth dataset. Given that RainScaleGAN's approach is agnostic with respect to the underlying physics, the method has the potential to be applied to other physical variables such as surface winds or temperature.
Integrating mobile and fixed monitoring data for high-resolution PM2.5 mapping using machine learning
Authors: Rui Xu, Dawen Yao, Yuzhuang Pian, Ruhui Cao, Yixin Fu, Xinru Yang, Ting Gan, Yonghong Liu
Date: 2025-03-16 00:00:00

Constructing high resolution air pollution maps at lower cost is crucial for sustainable city management and public health risk assessment. However, traditional fixed-site monitoring lacks spatial coverage, while mobile low-cost sensors exhibit significant data instability. This study integrates PM2.5 data from 320 taxi-mounted mobile low-cost sensors and 52 fixed monitoring stations to address these limitations. By employing the machine learning methods, an appropriate mapping relationship was established between fixed and mobile monitoring concentration. The resulting pollution maps achieved 500-meter spatial and 5-minute temporal resolutions, showing close alignment with fixed monitoring data (+4.35% bias) but significant deviation from raw mobile data (-31.77%). The fused map exhibits the fine-scale spatial variability also observed in the mobile pollution map, while showing the stable temporal variability closer to that of the fixed pollution map (fixed: 1.12 plus or minus 0.73%, mobile: 3.15 plus or minus 2.44%, mapped: 1.01 plus or minus 0.65%). These findings demonstrate the potential of large-scale mobile low-cost sensor networks for high-resolution air quality mapping, supporting targeted urban environmental governance and health risk mitigation.
Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data
Authors: Siddharth Rout, Eldad Haber, Stéphane Gaudreault
Date: 2025-03-15 00:00:00

The modeling of dynamical systems is essential in many fields, but applying machine learning techniques is often challenging due to incomplete or noisy data. This study introduces a variant of stochastic interpolation (SI) for probabilistic forecasting, estimating future states as distributions rather than single-point predictions. We explore its mathematical foundations and demonstrate its effectiveness on various dynamical systems, including the challenging WeatherBench dataset.
SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models
Authors: Kunyang Sun, Dorian Bagni, Joseph M. Cavanagh, Yingze Wang, Jacob M. Sawyer, Andrew Gritsevskiy, Oufan Zhang, Teresa Head-Gordon
Date: 2025-03-16 00:00:00
Tasks: Drug Discovery

Generative machine learning models for small molecule drug discovery have shown immense promise, but many molecules they generate are too difficult to synthesize, making them impractical for further investigation or development. In this work, we present a novel approach by fine-tuning Meta's Llama3 Large Language Models (LLMs) to create SynLlama, which generates full synthetic pathways made of commonly accessible building blocks and robust organic reaction templates. SynLlama explores a large synthesizable space using significantly less data compared to other state-of-the-art methods, and offers strong performance in bottom-up synthesis, synthesizable analog generation, and hit expansion, offering medicinal chemists a valuable tool for drug discovery developments. We find that SynLlama, even without training on external building blocks, can effectively generalize to unseen yet purchasable building blocks, meaning that its reconstruction capabilities extend to a broader synthesizable chemical space than the training data. We also demonstrate the use of SynLlama in a pharmaceutical context for synthesis planning of analog molecules and hit expansion leads for proposed inhibitors of target proteins.
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
Authors: Sakib Matin, Emily Shinkle, Yulia Pimonova, Galen T. Craven, Aleksandra Pachalieva, Ying Wai Li, Kipton Barros, Nicholas Lubbers
Date: 2025-03-18 00:00:00
Tasks: Atomic Forces, Knowledge Distillation, Molecular Property Prediction, Property Prediction

The quality of machine learning interatomic potentials (MLIPs) strongly depends on the quantity of training data as well as the quantum chemistry (QC) level of theory used. Datasets generated with high-fidelity QC methods are typically restricted to small molecules and may be missing energy gradients, which make it difficult to train accurate MLIPs. We present an ensemble knowledge distillation (EKD) method to improve MLIP accuracy when trained to energy-only datasets. First, multiple teacher models are trained to QC energies and then generate atomic forces for all configurations in the dataset. Next, the student MLIP is trained to both QC energies and to ensemble-averaged forces generated by the teacher models. We apply this workflow on the ANI-1ccx dataset where the configuration energies computed at the coupled cluster level of theory. The resulting student MLIPs achieve new state-of-the-art accuracy on the COMP6 benchmark and show improved stability for molecular dynamics simulations.
A Reinforcement Learning-Driven Transformer GAN for Molecular Generation
Authors: Chen Li, Huidong Tang, Ye Zhu, Yoshihiro Yamanishi
Date: 2025-03-17 00:00:00
Tasks: Drug Discovery, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Generating molecules with desired chemical properties presents a critical challenge in fields such as chemical synthesis and drug discovery. Recent advancements in artificial intelligence (AI) and deep learning have significantly contributed to data-driven molecular generation. However, challenges persist due to the inherent sensitivity of simplified molecular input line entry system (SMILES) representations and the difficulties in applying generative adversarial networks (GANs) to discrete data. This study introduces RL-MolGAN, a novel Transformer-based discrete GAN framework designed to address these challenges. Unlike traditional Transformer architectures, RL-MolGAN utilizes a first-decoder-then-encoder structure, facilitating the generation of drug-like molecules from both $de~novo$ and scaffold-based designs. In addition, RL-MolGAN integrates reinforcement learning (RL) and Monte Carlo tree search (MCTS) techniques to enhance the stability of GAN training and optimize the chemical properties of the generated molecules. To further improve the model's performance, RL-MolWGAN, an extension of RL-MolGAN, incorporates Wasserstein distance and mini-batch discrimination, which together enhance the stability of the GAN. Experimental results on two widely used molecular datasets, QM9 and ZINC, validate the effectiveness of our models in generating high-quality molecular structures with diverse and desirable chemical properties.
An On-Chip Ultra-wideband Antenna with Area-Bandwidth Optimization for Sub-Terahertz Transceivers and Radars
Authors: Boxun Yan, Runzhou Chen, Mau-Chung Frank Chang
Date: 2025-03-05 00:00:00

In this paper, we present an on-chip antenna at 290 GHz that achieves a maximum efficiency of 42\% on a low-resistivity silicon substrate for sub-terahertz integrated transceivers. The proposed antenna is based on a dual-slot structure to accommodate a limited ground plane and maintain desired radiation and impedance characteristics across the target frequency range. The antenna impedance bandwidth reaches 39\% with compact physical dimensions of 0.24$\lambda_0\times$0.42$\lambda_0$. Simulation and measurement results confirm its promising antenna performance for potential transceiver and radar applications.