pwc-archive/papers-with-abstracts huggingface dataset:
Behavioral Traits as Dynamical Systems: Utilizing Entropy to Analyze Longitudinal Psychometric Data in Coupled Ordinary Differential Equations
Authors: Anderson M. Rodriguez
Date: 2025-06-25 00:00:00

Traits such as neuroticism persist across species despite exhibiting characteristics typically regarded as maladaptive. This project presents an alternative model for understanding the stability of such traits by integrating findings from the Swedish Adoption/Twin Study on Aging (Pedersen, 2015) (SATSA) with a system of recursive, biologically inspired ordinary differential equations (ODEs). To utilize the ODEs analytically, Shannon entropy is extracted from longitudinal Likert-scale psychometric data, enabling the translation of high-dimensional behavioral responses into continuous-time dynamical systems. The model incorporates principles from evolutionary biology, including mutation-selection balance, genetic pleiotropy and metabolic constraints, and embeds environmental feedback as a recursive driver of phenotypic expression. The argument is presented that traits such as neuroticism exist not stochastically, but as emergent multistable attractors within a biologically-constrained system. This paper shows that entropy extracted from longitudinal psychometric data can be meaningfully modeled using recursive ordinary differential equations, revealing stable dynamical attractors and biologically and environmentally grounded constraints in traits often deemed maladaptive. This framework offers a scalable, mathematically grounded foundation for analyzing phenotypic expression, with the ultimate goal of biological extension for eventual multi-omic modeling of behavioral traits.
Modelling Statistics of Polypeptides in Emissions from Smokers Near Ocean Ridges
Authors: Ben Intoy, J. W. Halley
Date: 2025-06-24 00:00:00

We have previously shown in model studies that rapid quenches of systems of monomers interacting to form polymer chains can fix nonequilibrium chemistries which could lead to the origin of life. We suggested that such quenching processes might have occurred at very high rates on early earth, giving an efficient mechanism for natural sorting through enormous numbers of nonequilibrium chemistries from which those most likely to lead to life could be naturally selected. Taking account of kinetic barriers, we found good agreement between laboratory quenching experiments on solutions of amino acids and the resulting model. We also made a preliminary comparison between reported data on polypeptides sampled from emissions from smokers near ocean ridges and our model. However that previous model assumed that the concentrations of all monomeric amino acids in the medium were the same whereas that is not the case in samples taken from ocean smokers. Here we take account of the heterogeneous concentrations of the amino acid monomers in the medium in the analysis of the smoker data and compare the results with the data on polypeptide concentrations found in the samples taken by a submersible in the Marianna Trough. Results are consistent with the hypothesis that smokers were the source of large and extremely diverse number of polypeptides in thermal disequilibrium which could incubate processes leading to life.
Modeling the influences of non-local connectomic projections on geometrically constrained cortical dynamics
Authors: Rishikesan Maran, Eli J. Müller, Ben D. Fulcher
Date: 2025-06-24 00:00:00
Tasks: Specificity

The function and dynamics of the cortex are fundamentally shaped by the specific wiring configurations of its constituent axonal fibers, also known as the connectome. However, many dynamical properties of macroscale cortical activity are well captured by instead describing the activity as propagating waves across the cortical surface, constrained only by the surface's two-dimensional geometry. It thus remains an open question why the local geometry of the cortex can successfully capture macroscale cortical dynamics, despite neglecting the specificity of Fast-conducting, Non-local Projections (FNPs) which are known to mediate the rapid and non-local propagation of activity between remote neural populations. Here we address this question by developing a novel mathematical model of macroscale cortical activity in which cortical populations interact both by a continuous sheet and by an additional set of FNPs wired independently of the sheet's geometry. By simulating the model across a range of connectome topologies, external inputs, and timescales, we demonstrate that the addition of FNPs strongly shape the model dynamics of rapid, stimulus-evoked responses on fine millisecond timescales ($\lessapprox 30~\text{ms}$), but contribute relatively little to slower, spontaneous fluctuations over longer timescales ($> 30~\text{ms}$), which increasingly resemble geometrically constrained dynamics without FNPs. Our results suggest that the discrepant views regarding the relative contributions of local (geometric) and non-local (connectomic) cortico-cortical interactions are context-dependent: While FNPs specified by the connectome are needed to capture rapid communication between specific distant populations (as per the rapid processing of sensory inputs), they play a relatively minor role in shaping slower spontaneous fluctuations (as per resting-state functional magnetic resonance imaging).
Identifying the sources of noise synergy and redundancy in the feed-forward loop motif
Authors: Mintu Nandi, Sudip Chattopadhyay, Suman K Banik
Date: 2025-06-23 00:00:00

The propagation of noise through parallel regulatory pathways is a prominent feature of feed-forward loops in genetic networks. Although the contributions of the direct and indirect regulatory pathways of feed-forward loops to output variability have been well characterized, the impact of their joint action arising from their shared input and output remains poorly understood. Here, we identify an additional component of noise that emerges specifically from this convergent nature of the pathways. Using inter-gene correlations, we reveal the regulatory basis of the additional noise and interpret it as synergy or redundancy in noise propagation, depending on whether the combined pathways amplify or suppress fluctuations. This framework not only accounts for previously observed differences in noise behavior across coherent and incoherent feed-forward loops but also provides a generalizable strategy to connect network structure with stochastic gene regulation.
Development and in vitro Characterization of a Novel Bioactive Hydrogel for Bioprinting Uterine Constructs
Authors: Abbas Fazel Anvari Yazdi, Kobra Tahermanesh, Maryam Ejlali, Louison Blivet-Bailly, Vatsala Singh, Bishnu Acharya, Daniel J. MacPhee, Ildiko Badea, Xiong-biao Chen
Date: 2025-06-18 00:00:00

Background and Aim: Decellularized uterine extracellular matrix (dUECM) offers a promising bioactive scaffold for uterine tissue engineering, but its application in 3D constructs has been limited by fabrication challenges. This study aimed to develop a printable, bioactive hydrogel from dUECM suitable for 3D bioprinting and to evaluate its ability to support human uterine myometrial cell growth in vitro. Materials and Methods: Porcine uterine tissues were decellularized using 1 percent Triton X-100 and 0.1 to 1.5 percent SDS for 48 to 72 hours. The resulting dUECM was assessed via histology, DNA and GAG quantification, scanning electron microscopy, FTIR, Raman spectroscopy, and thermogravimetric analysis. Selected dUECM samples were digested with pepsin and blended with 2 or 3 percent alginate to create bioinks. Constructs were printed using extrusion-based bioprinting and evaluated for swelling, degradation, mechanical properties, and printability. Biocompatibility was tested by seeding hTERT-HM cells onto cast hydrogels and performing MTT and Live/Dead assays over seven days. Results: The optimal protocol (1 percent Triton X-100 and 1 percent SDS for 48 hours) reduced DNA to 51.3 ng per mg, while retaining 54.9 micrograms per mg of GAGs. FTIR and Raman spectroscopy confirmed collagen preservation, while thermal stability was moderately reduced. The 3 percent alginate with 1.5 percent dUECM formulation showed superior printability, swelling stability, degradation resistance, and mechanical strength. Cell proliferation reached 258 percent by day 7, significantly outperforming alginate alone. Conclusion: The optimized dUECM hydrogel supports printability, mechanical performance, and cell viability, offering a robust platform for uterine tissue engineering.
Operator Forces For Coarse-Grained Molecular Dynamics
Authors: Leon Klein, Atharva Kelkar, Aleksander Durumeric, Yaoyi Chen, Frank Noé
Date: 2025-06-24 00:00:00

Coarse-grained (CG) molecular dynamics simulations extend the length and time scale of atomistic simulations by replacing groups of correlated atoms with CG beads. Machine-learned coarse-graining (MLCG) has recently emerged as a promising approach to construct highly accurate force fields for CG molecular dynamics. However, the calibration of MLCG force fields typically hinges on force matching, which demands extensive reference atomistic trajectories with corresponding force labels. In practice, atomistic forces are often not recorded, making traditional force matching infeasible on pre-existing datasets. Recently, noise-based kernels have been introduced to adapt force matching to the low-data regime, including situations in which reference atomistic forces are not present. While this approach produces force fields which recapitulate slow collective motion, it introduces significant local distortions due to the corrupting effects of the noise-based kernel. In this work, we introduce more general kernels based on normalizing flows that substantially reduce these local distortions while preserving global conformational accuracy. We demonstrate our method on small proteins, showing that flow-based kernels can generate high-quality CG forces solely from configurational samples.
Federated Learning from Molecules to Processes: A Perspective
Authors: Jan G. Rittig, Clemens Kortmann
Date: 2025-06-23 00:00:00
Tasks: Federated Learning

We present a perspective on federated learning in chemical engineering that envisions collaborative efforts in machine learning (ML) developments within the chemical industry. Large amounts of chemical and process data are proprietary to chemical companies and are therefore locked in data silos, hindering the training of ML models on large data sets in chemical engineering. Recently, the concept of federated learning has gained increasing attention in ML research, enabling organizations to jointly train machine learning models without disclosure of their individual data. We discuss potential applications of federated learning in several fields of chemical engineering, from the molecular to the process scale. In addition, we apply federated learning in two exemplary case studies that simulate practical scenarios of multiple chemical companies holding proprietary data sets: (i) prediction of binary mixture activity coefficients with graph neural networks and (ii) system identification of a distillation column with autoencoders. Our results indicate that ML models jointly trained with federated learning yield significantly higher accuracy than models trained by each chemical company individually and can perform similarly to models trained on combined datasets from all companies. Federated learning has therefore great potential to advance ML models in chemical engineering while respecting corporate data privacy, making it promising for future industrial applications.
Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
Authors: Michael Plainer, Hao Wu, Leon Klein, Stephan Günnemann, Frank Noé
Date: 2025-06-20 00:00:00

Diffusion models have recently gained significant attention due to their effectiveness in various scientific domains, including biochemistry. When trained on equilibrium molecular distributions, diffusion models provide both: a generative procedure to sample equilibrium conformations and associated forces derived from the model's scores. However, using the forces for coarse-grained molecular dynamics simulations uncovers inconsistencies in the samples generated via classical diffusion inference and simulation, despite both originating from the same model. Particularly at the small diffusion timesteps required for simulations, diffusion models fail to satisfy the Fokker-Planck equation, which governs how the score should evolve over time. We interpret this deviation as an indication of the observed inconsistencies and propose an energy-based diffusion model with a Fokker-Planck-derived regularization term enforcing consistency. We demonstrate the effectiveness of our approach on toy systems, alanine dipeptide, and introduce a state-of-the-art transferable Boltzmann emulator for dipeptides that supports simulation and demonstrates enhanced consistency and efficient sampling.
Descriptor-based Foundation Models for Molecular Property Prediction
Authors: Jackson Burns, Akshat Zalte, William Green
Date: 2025-06-18 00:00:00
Tasks: Molecular Property Prediction, Prediction, Property Prediction

Fast and accurate prediction of molecular properties with machine learning is pivotal to scientific advancements across myriad domains. Foundation models in particular have proven especially effective, enabling accurate training on small, real-world datasets. This study introduces CheMeleon, a novel molecular foundation model pre-trained on deterministic molecular descriptors from the Mordred package, leveraging a Directed Message-Passing Neural Network to predict these descriptors in a noise-free setting. Unlike conventional approaches relying on noisy experimental data or biased quantum mechanical simulations, CheMeleon uses low-noise molecular descriptors to learn rich molecular representations. Evaluated on 58 benchmark datasets from Polaris and MoleculeACE, CheMeleon achieves a win rate of 79% on Polaris tasks, outperforming baselines like Random Forest (46%), fastprop (39%), and Chemprop (36%), and a 97% win rate on MoleculeACE assays, surpassing Random Forest (63%) and other foundation models. However, it struggles to distinguish activity cliffs like many of the tested models. The t-SNE projection of CheMeleon's learned representations demonstrates effective separation of chemical series, highlighting its ability to capture structural nuances. These results underscore the potential of descriptor-based pre-training for scalable and effective molecular property prediction, opening avenues for further exploration of descriptor sets and unlabeled datasets.
Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Authors: Shagun Maheshwari, Janghoon Ock, Adeesh Kolluru, Amir Barati Farimani, John R. Kitchin
Date: 2025-06-17 00:00:00
Tasks: Graph Neural Network

Machine-learning force fields (MLFFs) have emerged as a promising solution for speeding up ab initio molecular dynamics (MD) simulations, where accurate force predictions are critical but often computationally expensive. In this work, we employ GemNet-T, a graph neural network model, as an MLFF and investigate two training strategies: (1) direct training on MD17 (10K samples) without pre-training, and (2) pre-training on the large-scale OC20 dataset followed by fine-tuning on MD17 (10K). While both approaches achieve low force mean absolute errors (MAEs), reaching 5 meV/A per atom, we find that lower force errors do not necessarily guarantee stable MD simulations. Notably, the pre-trained GemNet-T model yields significantly improved simulation stability, sustaining trajectories up to three times longer than the model trained from scratch. These findings underscore the value of pre-training on large, diverse datasets to capture complex molecular interactions and highlight that force MAE alone is not always a sufficient metric of MD simulation stability.
Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation
Authors: Mahmoud Amiri, Thomas Bocklitz
Date: 2025-06-13 00:00:00
Tasks: Chunking, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-Augmented Generation (RAG) systems are increasingly vital for navigating the ever-expanding body of scientific literature, particularly in high-stakes domains such as chemistry. Despite the promise of RAG, foundational design choices -- such as how documents are segmented and represented -- remain underexplored in domain-specific contexts. This study presents the first large-scale, systematic evaluation of chunking strategies and embedding models tailored to chemistry-focused RAG systems. We investigate 25 chunking configurations across five method families and evaluate 48 embedding models on three chemistry-specific benchmarks, including the newly introduced QuestChemRetrieval dataset. Our results reveal that recursive token-based chunking (specifically R100-0) consistently outperforms other approaches, offering strong performance with minimal resource overhead. We also find that retrieval-optimized embeddings -- such as Nomic and Intfloat E5 variants -- substantially outperform domain-specialized models like SciBERT. By releasing our datasets, evaluation framework, and empirical benchmarks, we provide actionable guidelines for building effective and efficient chemistry-aware RAG systems.
Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models
Authors: Xinghao Dong, Huchen Yang, Jin-Long Wu
Date: 2025-06-25 00:00:00

We propose a latent score-based generative AI framework for learning stochastic, non-local closure models and constitutive laws in nonlinear dynamical systems of computational mechanics. This work addresses a key challenge of modeling complex multiscale dynamical systems without a clear scale separation, for which numerically resolving all scales is prohibitively expensive, e.g., for engineering turbulent flows. While classical closure modeling methods leverage domain knowledge to approximate subgrid-scale phenomena, their deterministic and local assumptions can be too restrictive in regimes lacking a clear scale separation. Recent developments of diffusion-based stochastic models have shown promise in the context of closure modeling, but their prohibitive computational inference cost limits practical applications for many real-world applications. This work addresses this limitation by jointly training convolutional autoencoders with conditional diffusion models in the latent spaces, significantly reducing the dimensionality of the sampling process while preserving essential physical characteristics. Numerical results demonstrate that the joint training approach helps discover a proper latent space that not only guarantees small reconstruction errors but also ensures good performance of the diffusion model in the latent space. When integrated into numerical simulations, the proposed stochastic modeling framework via latent conditional diffusion models achieves significant computational acceleration while maintaining comparable predictive accuracy to standard diffusion models in physical spaces.
Deep Electromagnetic Structure Design Under Limited Evaluation Budgets
Authors: Shijian Zheng, Fangxiao Jin, Shuhai Zhang, Quan Xue, Mingkui Tan
Date: 2025-06-24 00:00:00

Electromagnetic structure (EMS) design plays a critical role in developing advanced antennas and materials, but remains challenging due to high-dimensional design spaces and expensive evaluations. While existing methods commonly employ high-quality predictors or generators to alleviate evaluations, they are often data-intensive and struggle with real-world scale and budget constraints. To address this, we propose a novel method called Progressive Quadtree-based Search (PQS). Rather than exhaustively exploring the high-dimensional space, PQS converts the conventional image-like layout into a quadtree-based hierarchical representation, enabling a progressive search from global patterns to local details. Furthermore, to lessen reliance on highly accurate predictors, we introduce a consistency-driven sample selection mechanism. This mechanism quantifies the reliability of predictions, balancing exploitation and exploration when selecting candidate designs. We evaluate PQS on two real-world engineering tasks, i.e., Dual-layer Frequency Selective Surface and High-gain Antenna. Experimental results show that our method can achieve satisfactory designs under limited computational budgets, outperforming baseline methods. In particular, compared to generative approaches, it cuts evaluation costs by 75-85%, effectively saving 20.27-38.80 days of product designing cycle.
LFR-PINO: A Layered Fourier Reduced Physics-Informed Neural Operator for Parametric PDEs
Authors: Jing Wang, Biao Chen, Hairun Xie, Rui Wang, Yifan Xia, Jifa Zhang, Hui Xu
Date: 2025-06-21 00:00:00
Tasks: Computational Efficiency

Physics-informed neural operators have emerged as a powerful paradigm for solving parametric partial differential equations (PDEs), particularly in the aerospace field, enabling the learning of solution operators that generalize across parameter spaces. However, existing methods either suffer from limited expressiveness due to fixed basis/coefficient designs, or face computational challenges due to the high dimensionality of the parameter-to-weight mapping space. We present LFR-PINO, a novel physics-informed neural operator that introduces two key innovations: (1) a layered hypernetwork architecture that enables specialized parameter generation for each network layer, and (2) a frequency-domain reduction strategy that significantly reduces parameter count while preserving essential spectral features. This design enables efficient learning of a universal PDE solver through pre-training, capable of directly handling new equations while allowing optional fine-tuning for enhanced precision. The effectiveness of this approach is demonstrated through comprehensive experiments on four representative PDE problems, where LFR-PINO achieves 22.8%-68.7% error reduction compared to state-of-the-art baselines. Notably, frequency-domain reduction strategy reduces memory usage by 28.6%-69.3% compared to Hyper-PINNs while maintaining solution accuracy, striking an optimal balance between computational efficiency and solution fidelity.
Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks
Authors: Jonas R. Naujoks, Aleksander Krasowski, Moritz Weckbecker, Galip Ümit Yolcu, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek, René P. Klausen
Date: 2025-06-19 00:00:00

Physics-informed neural networks (PINNs) offer a powerful approach to solving partial differential equations (PDEs), which are ubiquitous in the quantitative sciences. Applied to both forward and inverse problems across various scientific domains, PINNs have recently emerged as a valuable tool in the field of scientific machine learning. A key aspect of their training is that the data -- spatio-temporal points sampled from the PDE's input domain -- are readily available. Influence functions, a tool from the field of explainable AI (XAI), approximate the effect of individual training points on the model, enhancing interpretability. In the present work, we explore the application of influence function-based sampling approaches for the training data. Our results indicate that such targeted resampling based on data attribution methods has the potential to enhance prediction accuracy in physics-informed neural networks, demonstrating a practical application of an XAI method in PINN training.