jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jan Velecký | Hana Faldynová | Pedro Hermosilla | Nela Sendlerová | Mark Doerr | Sára Egersdorfová | Uwe T. Bornscheuer | Jiřı́ Damborský | Zbyněk Prokop | Stanislav Mazurenko
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-27)
Field: Materials Science — Materials Chemistry

SoluProtMut: Siamese Deep Learning for Predicting Solubility Effects of Protein Mutations with Experimental Validation Abstract Protein solubility is an attractive engineering target because it is a critical property influencing the scalability of protein production and the success of therapeutic proteins in biomedical applications. However, predicting solubility changes upon mutation in silico is challenging due to data heterogeneity and protein bias. Here, we explore how different sources of solubility data can be used for machine learning and present SoluProtMut, a Siamese deep geometric neural network trained to predict the impact of mutations on protein solubility. Our final model was trained exclusively on deep mutational scanning data. We compare our model with five established solubility prediction methods. The model achieves state-of-the-art performance on an independent dataset of various proteins, especially in predicting the effects of multipoint mutations (informedness of 26.5 %). Our findings also reaffirm that the scarcity of solubility data continues to hamper progress in this field. To address this limitation, we experimentally quantified solubility changes for hundreds of single-point and multipoint mutants of haloalkane dehalogenase. Complemented with recent deep-mutational-scanning data on myoglobin, we employed both these data for external validation. Although the generalization to unseen proteins remains limited, our findings demonstrate the potential of integrating high-throughput assays with deep learning to improve the accuracy and scope of solubility prediction. Highlights - We present a novel anti-symmetric Siamese architecture for mutational prediction on structures built on graph-based convolutional neural networks ensuring SE(3) invariance. - We demonstrate that a model trained only on single-point mutants of a single protein derived from high-throughput experiments generalizes to multipoint mutants of unseen proteins, achieving the state-of-the-art binary prediction informedness of 26.5 %. - We address the key limitation in the domain by extending the available data with 277 single-point and multipoint mutants of haloalkane dehalogenase labelled in-house and a selection of recently published 1037 single-point mutants of myoglobin. - We systematically study how different data subsets affect the performance of the trained models, revealing that including yeast-derived high-throughput data in training hampers generalization to low-throughput assays but recovers the performance on the yeast-derived myoglobin dataset. Deep learning ; Solubility ; Convolutional neural network ; Mutation ; Generalization ; Artificial neural network ; Scalability ; Mutant Machine Learning in Materials Science ; Protein Structure and Dynamics ; Protein purification and stability
Authors: M.L. Casais-Molina | C. Cab | Rubén A. Medina-Esquivel | Jorge A. Tapia
Source: Condensed Matter (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

Unveiling Bulk Modulus and Stretching Bond Force Constants of Cubic and Wurtzite Boron Nitride Structures: A DFT Study The mechanical properties of cubic (c-BN) and wurtzite (w-BN) boron nitride structures were investigated and compared using density functional theory (DFT) with several exchange–correlation functionals. This research focuses on determining the bulk modulus (B) and, for the first time, the stretching bond force constants (kr), two fundamental parameters that describe the intrinsic stiffness and elastic resistance of these BN structures. Despite their structural similarity with the same tetrahedral coordination between atoms, c-BN and w-BN exhibit subtle differences in bond strength and compressibility that have not been fully clarified at the atomistic level. By systematically analyzing the influence of hybrid and semi-local functionals, consistent relationship between structural configuration and the predicted B and kr values of both c-BN and w-BN structures were established and compared. These findings not only validate DFT as a reliable approach for assessing the mechanical properties of BN polymorphs, but also offer key parameters for machine learning and advanced multiscale modeling. Therefore, this theoretical study contributes to understanding the origin of mechanical properties in BN structures and supports their design in applications where a particular hardness and stability are required. Boron nitride ; Wurtzite crystal structure ; Density functional theory ; Bulk modulus ; Stiffness ; Compressibility ; Modulus ; Structural stability ; Nitride Boron and Carbon Nanomaterials Research ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Ashi Ramavat | Priya Antil | Saakshi Kaushik | Babita Kataria | Ramendra Pati Pandey
Source: Preprints.org (preprint, 2025-10-14)
Field: Materials Science — Biomaterials

Nanomedicine for Cancer and Autoimmune Immunotherapy Nanomedicine has now become a radical agent of change in the game of immunotherapy and introduced precision, control, and customization like never before when it comes to cancer, as well as autoimmune conditions. Using platforms based on nano scale, researchers have been able to manipulate immune responses by working on a scale both spatial and temporal in such a way that it helps overcome the drawbacks associated with working with immune response such as immune evasion, systemic toxicity and poor pharmacokinetics. Sophisticated nanoparticles (such as stimuli-sensitive ones, exosome-mimetic vesicle nanoparticles, and nanoparticles with CRISPR) allow directed immunomodulators, antigens and gene-editing systems to reach one or more particular immune compartments. The innovations allow reprogramming of immune cells, immune tolerance rejuvenation and expansion of antitumor immunity without significant off-target effects. Finding applications in integrating the artificial intelligence as well as multi-omics techniques, the process leads to personalization of the nano-immunotherapies based on patient-specific immuno-signatures. The chapter discusses the mechanistic rationale, therapeutic advancement, and the translational opportunities of nanotechnology-based immunotherapies that define them as part of a foundation of future generations of clinical approaches to precision immune modulation in oncology and autoimmune diseases. Immune system ; Immunotherapy ; Cancer ; Cancer immunotherapy ; Nanomedicine ; Personalization ; Reprogramming ; Precision medicine Nanoparticle-Based Drug Delivery ; Immunotherapy and Immune Responses ; Monoclonal and Polyclonal Antibodies Research
Authors: Oneil Goisot | H. Vanlandeghem | Richard Haettel | F. Robaut | O. Robach | L. Porcar | M. Verdier
Source: ArXiv.org (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

Exploration of the hysteresis of martensite-austenite transition in bulk \b{eta}-Cu-Zn-Al single crystals Improvement of functional and structural fatigue endurance for applications of ferroelastic materials requires an optimization of their composition. A strategy for finding alloy compositions that minimize the transformation hysteresis is necessary. We propose an experimental high throughput methodology to explore the model \b{eta}-Cu-Zn-Al system. It is based on an original route to process bulk gradient composition single crystals to investigate fine variation of composition range coupled with local measurements of the austenite-martensite microstructure by light microscopy during the transformation. The latter method is compared with differential scanning calorimetry measurements. The methodology is applied in an Al-richer range of composition of standard CuZnAl SMA where a minimum of transformation hysteresis is observed. Differential scanning calorimetry ; Hysteresis ; Microstructure ; Range (aeronautics) ; Transformation (genetics) ; Process (computing) ; Shape-memory alloy ; Atmospheric temperature range Shape Memory Alloy Transformations ; Ferroelectric and Piezoelectric Materials ; Metallurgical and Alloy Processes
Authors: Edwin A. Bedolla-Montiel | Ramón A. Castañeda-Cerdán | Ramón Castañeda-Priego
Source: ArXiv.org (preprint, 2025-02-06)
Field: Materials Science — Materials Chemistry

Phase diagram of the hard-sphere potential model in three and four dimensions using a pseudo-hard-sphere potential The hard-sphere potential has become a cornerstone in the study of both molecular and complex fluids. Despite its mathematical simplicity, its implementation in fixed time-step molecular simulations remains a formidable challenge due to the discontinuity at contact. To circumvent the issues associated with the ill-defined force at contact, a continuous potential--referred to here as the pseudo-hard-sphere (pHS) potential--has recently been proposed [J. Chem, Phys. 149, 164907 (2018)]. This potential is constructed to match the second virial coefficient of the hard-sphere potential and is expected to mimic its thermodynamic properties. However, this hypothesis has only been partially validated within the fluid region of the phase diagram for hard-sphere dispersions in two and three dimensions. In this contribution, we examine the ability of the continuous pHS potential to reproduce the equation of state of a hard-sphere fluid, not only in the fluid phase but also across the fluid-solid coexistence region. Our focus is primarily on hard-sphere systems in three and four dimensions. We compare the results obtained from Brownian dynamics simulations of the pHS potential with those derived from refined event-driven simulations of the corresponding hard-sphere potential. Furthermore, we provide a comparative analysis with theoretical equations of state based on both mean-field and integral equation approximations. Phase diagram ; Hard spheres ; Diagram ; Statistical physics ; Phase (matter) ; Physics ; Mathematics ; Theoretical physics ; Thermodynamics ; Statistics ; Quantum mechanics Material Dynamics and Properties ; Theoretical and Computational Physics ; High-pressure geophysics and materials
Authors: Khalil Pestehei | Mahdieh Ghiasi | Sepideh Moradkhani | Bita Fazel | Sara Honarmand Ebrahimi | Amirhossein Ghiasi
Source: Research Square (preprint, 2025-09-03)
Field: Materials Science — Biomaterials

Bioengineered hybrid electrospun scaffold of Polycaprolacton/Gelatin-nano- hydroxyapatite, coated with platelet-rich fibrin, for enhanced osteogenesis and bone healing using adipose derived stem cells Gelatin ; Fibrin ; Scaffold ; Platelet-rich fibrin ; Biomedical engineering ; Stem cell ; Adipose tissue ; Bone healing ; Chemistry ; Tissue engineering ; Mesenchymal stem cell ; Materials science ; Cell biology ; Medicine ; Anatomy ; Biochemistry ; Immunology ; Biology Electrospun Nanofibers in Biomedical Applications ; Periodontal Regeneration and Treatments ; Bone Tissue Engineering Materials
Authors: Dor Shohat | Paul Baconnier | Itamar Procaccia | Martin van Hecke | Yoav Lahini
Source: ArXiv.org (preprint, 2025-06-10)
Field: Materials Science — Materials Chemistry

Aging of amorphous materials under cyclic strain Amorphous materials driven away from equilibrium display a diverse repertoire of complex, history-dependent behaviors. One striking feature is a failure to return to equilibrium after an abrupt change in otherwise static external conditions. Instead, amorphous materials often exhibit physical aging: an ever-slowing, nonexponential relaxation that can span a huge range of timescales. Here we examine the aging behavior of three different amorphous materials subjected to slow periodic driving. The results reveal a generic aging phenomenon characterized by a logarithmic decay of dissipation per cycle. This observation is evaluated against several mesoscopic models of amorphous matter that successfully capture aging under static conditions: (i) a collection of noninteracting relaxation processes (ii) a noisy hysteron model with random pairwise interactions, and (iii) a structural model consisting of a random network of bi-stable elastic bonds. We find that only the latter model reproduces all experimental findings and relate its success to its persistent, slow exploration of a complex energy landscape with clear signatures of replica symmetry breaking. Thus, cyclic driving emerges as a simple yet powerful protocol to characterize amorphous materials, probe their complex energy landscapes, and distinguish between different models. Amorphous solid ; Mesoscopic physics ; Replica ; Relaxation (psychology) ; Dissipation ; Logarithm ; Range (aeronautics) ; Glass transition Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Theoretical and Computational Physics
Authors: Eduardo Luís Schneider | Rodrigo Nazarof Saran | C Cordeiro | Diego Tolotti de Almeida | Lisete Cristine Scienza | Cláudia Trindade Oliveira | Jakson Manfredini Vassoler
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Mechanical Property Evolution of Ze10a Alloy Subjected to Thermomechanical Treatments between 250 and 300 °C Alloy ; Property (philosophy) ; Materials science ; Composite material ; Metallurgy ; Philosophy Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Mina Oliayi | Rahman Emamzadeh | Mohamad Reza Ganjalikhany
Source:  (preprint, 2025-02-17)
Field: Materials Science — Polymers and Plastics

In Silico Investigation of Molecular Mechanisms Underlying the Function of NLuc and its Variants Bioluminescence, the emission of light by living organisms, results from chemiluminescent reactions facilitated by enzymes like luciferases. Among these, NanoLuc (NLuc) stands out due to its exceptional brightness, stability, and compact structure, making it a valuable tool in bioassays and imaging applications. NLuc is a 19.1 kDa monomeric enzyme derived from the deep-sea shrimp Oplophorus gracilirostris. Its structure comprises eleven antiparallel β-strands forming a β-barrel, capped by four α-helices. To enhance its versatility, NLuc has been engineered into split forms. The two-component split system divides NLuc into a large fragment and a small peptide, which can be either low-affinity (SmBiT) or high-affinity (HiBiT). Building upon this, a three-component system incorporates an additional peptide, offering improved control and potential applications in chemical biology. Despite the advancements in split NLuc applications, several impediments exist that can be addressed to improve these systems. Recent studies have illuminated the allosteric mechanisms of NLuc. The enzyme exhibits homotropic negative allostery, where product binding to an allosteric site inhibits substrate binding at the catalytic site. Ongoing research into its structural dynamics and allosteric behaviors continues to expand its potential applications, while efforts to enhance the efficiency of its split forms aim to broaden its utility in complex biological assays. In this in silico assay, we clarify the sources of malfunctions in split NLuc and also explore aspects of split NLuc technologies. We examine some hypotheses of NLuc mechanisms that display the complex behavior of this luciferase. In silico ; Function (biology) ; Computational biology ; Biology ; Computer science ; Genetics ; Gene Synthesis and properties of polymers
Authors: Jinpo Yang | Kai Chen | Yawen Zhou | Jian Zheng | Linhao Sun | Yun Zhang | Zhu Zhou
Source: Plants (article, 2025-11-03)
Field: Chemistry — Analytical Chemistry

Research on Geographical Origin Traceability of Salvia miltiorrhiza by Combining Two-Trace Two-Dimensional (2T2D) Correlation Spectroscopy and Improved DeiT Model <i>Salvia miltiorrhiza</i> Bunge (Danshen) is widely used in modern medicine, but the market faces challenges from counterfeit and mislabeled geographical indication products. To address this, we propose a novel framework combining Two-trace Two-dimensional (2T2D) correlation spectroscopy, hyperspectral imaging (HSI), transfer learning, and an enhanced deep learning model (DeiT-CBAM) to identify both authenticity and origin precisely. Hyperspectral data (873-1720 nm) were collected from six genuine and three adulterated regions and converted into synchronous 2T2D correlation spectroscopy images. We systematically evaluated five preprocessing strategies, three wavelength selection methods, three classical models, and four deep learning models. Models based on 2T2D correlation spectroscopy images consistently outperformed traditional one-dimensional spectral models. Notably, the DeiT-CBAM model, integrated with the successive projections algorithm (SPA), achieved optimal performance using only 79 wavelengths, with 100% accuracy on the training and validation sets and 99.62% on the test set, without the need for additional preprocessing. Model interpretability was further validated through layer-wise class activation mapping (layer-wise CAM). This study demonstrates that the integration of synchronous 2T2D correlation spectroscopy images with the DeiT-CBAM model offers robust discriminative performance, providing a reliable technical solution for geographical origin traceability of food, medicinal herbs, and other species. Hyperspectral imaging ; Discriminative model ; Traceability ; Interpretability ; Preprocessor ; Pattern recognition (psychology) ; Correlation ; Overfitting ; Salvia miltiorrhiza Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Reza Masoudian Saadabad | R. Emadi | Lingamallu Raghu Kumar | Davide Bacco | Maja Colautti
Source: ArXiv.org (preprint, 2025-04-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Constrained Neural Network for Metasurface Optical Response Prediction A physics-constrained neural network is presented for predicting the optical response of metasurfaces. Our approach incorporates physical laws directly into the neural network architecture and loss function, addressing critical challenges in the modeling of metasurfaces. Unlike methods that require specialized weighting strategies or separate architectural branches to handle different data regimes and phase wrapping discontinuities, this unified approach effectively addresses phase discontinuities, energy conservation constraints, and complex gap-dependent behavior. We implement sine-cosine phase representation with Euclidean normalization as a non-trainable layer within the network, enabling the model to account for the periodic nature of phase while enforcing the mathematical constraint $\sin^2 ϕ+ \cos^2 ϕ= 1$. A Euclidean distance-based loss function in the sine-cosine space ensures a physically meaningful error metric while preventing discontinuity issues. The model achieves good, consistent performance (e.g., coefficient of determinations above 0.9) with small, imbalanced datasets of 580 and 1075 data points, compared to several thousand typically required by alternative approaches. This physics-informed approach preserves physical interpretability while reducing reliance on large datasets and could be extended to systems involving periodic or wrapped quantities. Interpretability ; Normalization (sociology) ; Weighting ; Artificial neural network ; Discontinuity (linguistics) ; Euclidean geometry ; Function (biology) ; Representation (politics) ; Constraint (computer-aided design) Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Plasmonic and Surface Plasmon Research
Authors: Tao Yang | Ri-gui Zhou | Pengju Ren | Hongpeng Wang
Source: Research Square (preprint, 2025-11-11)
Field: Chemistry — Organic Chemistry

FARR: An Efficient Frozen-Feature Learning Framework for Wood Species Identification with Applications to Texture Recognition Robustness (evolution) ; Identification (biology) ; Residual ; Feature (linguistics) ; Generalization ; Scarcity ; Feature extraction ; Pattern recognition (psychology) Wood and Agarwood Research ; Advanced Neural Network Applications ; Brain Tumor Detection and Classification
Authors: Matthieu Melennec | Shamik Ghosh | F. Magniette
Source: EPJ Web of Conferences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Optimised Graph Convolution for Calorimetry Event Classification In the recent years, high energy physics discoveries have been driven by the increasing of luminosity and/or detector granularity. This evolution gives access to bigger statistics and data samples, but can make it hard to process the detector outputs with current methods and algorithms. Graph convolution networks, have been shown to be powerful tools to address these challenges. We present our graph convolution framework for particle identification and energy regression in high granularity calorimeters. In particular, we introduce our algorithm for optimised graph construction in resource constrained environments. We also introduce our implementation of graph convolution and pooling layers. We observe satisfying accuracies, and discuss possible application to other high granularity particle detector challenges. Granularity ; Computer science ; Graph ; Pooling ; Detector ; Convolution (computer science) ; Theoretical computer science ; Algorithm ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Big Data and Digital Economy ; Graph Theory and Algorithms
Authors: Chu Zhang | Zhongjie Tang | Xiaojing Tan | Hengnian Qi | Xincheng Zhang | Shanlin Ma
Source: Foods (article, 2025-09-24)
Field: Chemistry — Analytical Chemistry

Detection of Rice Prolamin and Glutelin Content Using Hyperspectral Imaging Combined with Feature Selection Algorithms and Multivariate Regression Models Prolamin and glutelin are the major constituents of rice protein. The rapid and non-destructive detection of prolamin and glutelin content is conducive to the accurate assessment of rice quality. In this study, hyperspectral imaging combined with regression models and feature wavelength selection was employed to detect the rice prolamin and glutelin content. Feature wavelength selection was achieved using the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and convolutional neural network (CNN)-based Gradient-weighted Class Activation Mapping++ (GradCAM++). Partial least squares regression (PLSR), support vector regression (SVR), back-propagation neural network (BPNN), and CNN models were established using the full spectra and the feature wavelengths. The BPNN models showed the best prediction performance for prolamin and glutelin. The optimal BPNN models achieved a correlation coefficient (<i>r</i>) greater than 0.8 for both proteins. Performance differences were observed between models using feature wavelengths and those using the full spectra. The GradCAM++ method was used to select feature wavelengths with different threshold values, and the performance of different threshold values were compared. The results demonstrated that hyperspectral imaging with multivariate data analysis was feasible for predicting the rice prolamin and glutelin content. This study provided a methodological reference for detecting prolamin and glutelin in rice, as well as the other protein types. Prolamin ; Glutelin ; Hyperspectral imaging ; Pattern recognition (psychology) ; Feature selection ; Multivariate statistics ; Feature (linguistics) ; Partial least squares regression ; Selection (genetic algorithm) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research