jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhijun Wang | Jinchen Cao | Xiaojian Wang | Wenbo Gu
Source: Computational Materials Science (article, 2026-02-24)
Field: Chemistry — Inorganic Chemistry

CO₂/CH₄ competitive adsorption in MOFs: high-throughput screening and molecular simulation Adsorption ; Diffusion ; Work (physics) ; Pressure swing adsorption ; Molecular dynamics ; Selectivity ; Metal-organic framework ; Rational design Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Catalytic Processes in Materials Science
Authors: Hussein Zahran | Aleksandr Zinovev | D. Terentyev | Ali Aouf | Magd Abdel Wahab
Source: European Journal of Mechanics - A/Solids (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

A deep learning approach to predict cyclic softening behaviour of irradiated and non-irradiated RAFM steels under low cycle fatigue Softening ; Artificial neural network ; Overfitting ; Learning curve ; Deep learning ; Curve fitting ; Backpropagation Fusion materials and technologies ; High Temperature Alloys and Creep ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Jin Lei
Source: Open MIND (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

Exterior complex scaling enables physics-informed neural networks for quantum scattering Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving differential equations, yet their application to quantum scattering problems has been hindered by the oscillatory, non-decaying nature of scattering wave functions. In this work, I demonstrate that exterior complex scaling (ECS) transforms scattering boundary conditions into exponentially decaying waves suitable for neural network solutions, enabling PINNs to solve nuclear scattering problems for the first time. I develop a driven-equation formulation where the source term is confined to the real axis, avoiding the need to analytically continue nuclear potentials into the complex plane. The method is validated on nucleon-nucleus scattering (n+$^{40}$Ca at $E_{\text{lab}}=20$~MeV) with 21 partial waves, achieving phase shift accuracy of $Δδ&lt; 0.1^\circ$ for most channels when compared to conventional solvers. I further demonstrate the approach on heavy-ion scattering ($^6$Li+$^{208}$Pb at 40~MeV) with 41 partial waves and strong Coulomb effects. This work establishes the foundation for extending PINNs to inverse problems where end-to-end differentiability enables direct fitting of optical potential parameters, coupled-channel reactions, and few-body scattering where traditional grid methods face exponential scaling. Scattering ; Scattering theory ; Inverse scattering problem ; Inverse scattering transform ; Artificial neural network ; Scaling ; Boundary (topology) ; Quantum ; Boundary value problem Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Pritam Bajirao Patil | Kanwal Takod | Debapriya Sarkar | Kasturi Poddar | Angana Sarkar
Source: Journal of Applied Electrochemistry (article, 2026-03-01)
Field: Materials Science — Biomaterials

In-situ development, artificial neural network-genetic algorithm-based optimization, and automation of the novel electrochemical cell lysis process for polyhydroxyalkanoates extraction Polyhydroxyalkanoates ; Lysis ; Extraction (chemistry) ; Bioprocess ; Electrochemistry ; Fourier transform infrared spectroscopy ; Environmentally friendly ; Process (computing) biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Polymer crystallization and properties
Authors: Antonia Vojtekova | Νικόλαος Νικολάου | Kai Hou Yip | O. Venot | A. F. Al-Refaie | Bruno Merín | Ingo P Waldmann
Source: RAS Techniques and Instruments (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Interpreting Atmospheric Neural Networks: Insights from Explainable AI Abstract We demonstrate how explainable artificial intelligence (XAI) methods can increase confidence in neural network predictions for exoplanet atmospheric modelling. We analysed a convolutional U-Net trained to reconstruct disequilibrium steady states from equilibrium chemistry states. We evaluated how the model encodes and uses physically meaningful features by combining exploratory data analysis with XAI techniques. Exploratory data analysis revealed patterns in the data depending on initial parameters such as the carbon-to-oxygen ratio, metallicity, and temperature. We observed the discovered dependencies throughout the network, confirming that the network learns relevant patterns rather than spurious relationships. By tracking the most influential molecules, we show that the network dynamically adjusts its focus with changing initial parameters, demonstrating that it captures physically meaningful differences between atmospheres. Furthermore, the tests performed also revealed limitations in the network that had not been considered previously. This work highlights how interpretability tools clarify learned representations to improve scientific reliability and can inform architectural refinements due to the limitations found. Interpretability ; Spurious relationship ; Artificial neural network ; Convolutional neural network ; Focus (optics) ; Exploratory analysis ; Perspective (graphical) ; Disequilibrium Machine Learning in Materials Science ; Astrophysics and Star Formation Studies ; Stellar, planetary, and galactic studies
Authors: Hoang Ngoc
Source: Nano-Structures & Nano-Objects (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Co-decorated 2D BeO/silicene heterostructure for selective detection of CH4, NH3 and NO Physisorption ; Heterojunction ; Chemisorption ; Density functional theory ; Adsorption ; Molecule ; Charge density Graphene research and applications ; 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Luna Liviero | Philipp Leclercq | Erwan Privat | Sergio Rampino | Antonino Polimeno
Source: Sustainability & Circularity NOW (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Predictive and Generative AI Approaches to the Design of Deep Eutectic Solvents Deep Eutectic Solvents (DESs) have emerged as promising sustainable alternatives to conventional solvents due to their potential low toxicity, biodegradability, and low flammability. While their complex nature makes experimental characterization costly and time consuming, the advent of Artificial Intelligence (AI) has recently paved the way to a revolutionary paradigm for chemical discovery, offering powerful tools not only for predicting properties of DES mixtures, but also for generating de novo DESs with optimized properties. After a concise overview of foundational concepts in AI, this Short Review explores the application of both predictive and generative machine learning (ML) models to the design and characterization of DESs. Generative grammar ; Characterization (materials science) ; Eutectic system ; Interpretability ; Deep learning Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Polymer Synthesis and Characterization
Authors: Lukman Ahamad | Sudeepta Pattanayak | Mohammad Danish | Smagghe Guy | Atul Kumar Srivastava | Sanwei Yang | Xin Xie
Source: Pesticide Biochemistry and Physiology (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Nano-pesticides in agroecosystems: Environmental fate, ecotoxicological impacts, and sustainable risk mitigation strategies Ecosystem services ; Ecotoxicity ; Mesocosm ; Sustainability ; Agroecology ; Agriculture ; Trophic level ; Sustainable agriculture ; Ecosystem Nanoparticles: synthesis and applications ; Environmental Toxicology and Ecotoxicology ; Pesticide and Herbicide Environmental Studies
Authors: Pengfei Liu | Shuang Ge | Jun Tao | Zhixiang Ren
Source: arXiv (Cornell University) (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

A Multi-task Large Reasoning Model for Molecular Science Advancements in artificial intelligence for molecular science are necessitating a paradigm shift from purely data-driven predictions to knowledge-guided computational reasoning. Existing molecular models are predominantly proprietary, lacking general molecular intelligence and generalizability. This underscores the necessity for computational methods that can effectively integrate scientific logic with deep learning architectures. Here we introduce a multi-task large reasoning model designed to emulate the cognitive processes of molecular scientists through structured reasoning and reflection. Our approach incorporates multi-specialist modules to provide versatile molecular expertise and a chain-of-thought (CoT) framework enhanced by reinforcement learning infused with molecular knowledge, enabling structured and reflective reasoning. Systematic evaluations across 10 molecular tasks and 47 metrics demonstrate that our model achieves an average 50.3% improvement over the base architecture, outperforming over 20 state-of-the-art baselines, including ultra-large-parameter foundation models, despite using significantly fewer training data and computational resources. This validates that embedding explicit reasoning mechanisms enables high-efficiency learning, allowing smaller-scale models to surpass massive counterparts in both efficacy and interpretability. The practical utility of this computational framework was validated through a case study on the design of central nervous system (CNS) drug candidates, illustrating its capacity to bridge data-driven and knowledge-integrated approaches for intelligent molecular design. Computational model ; Reinforcement learning ; Bridge (graph theory) ; Cognition ; Qualitative reasoning ; Embedding ; Model-based reasoning ; Task (project management) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: P. A. H. Nawoda | Gayan P. Hettiarachchi | M. D. T. Attygalle
Source: Journal of Applied Physics (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Predicting the critical temperature of superconductors and materials classification with a balanced dataset without prior knowledge The discovery of new superconductors through traditional trial-and-error experimental methods is both challenging and costly. A data-driven strategy can speed up the time-consuming exploration processes and also potentially reveal new insights into the complex correlations at play. Several material databases have been created for predicting the superconducting critical temperature (Tc), but they are confined solely to superconducting materials, limiting their prediction capabilities. In addressing this, we compiled a comprehensive open-source dataset encompassing both superconductors and non-superconductors. Our dataset contains 13 415 superconductors and 13 425 non-superconductors, each characterized by 212 features. Novel features, namely, CuO layers, ionic radius, heat of vaporization, cohesive energy, and thermal conductivity with respect to every element in the material were included. Another feature named “material type” was introduced to classify materials as low-temperature superconductors, high-temperature superconductors, and non-superconductors. Various machine learning techniques, including boosting and bagging methods, were employed to predict Tc. The light gradient boosting model emerged as the most effective, achieving a coefficient of determination of 93% and a mean absolute error of 2.93 K. While this novel, comprehensive material dataset is made available to enrich future research, a web application is also developed for predicting Tc of any material in relation to the material type based on the best of trained models. Boosting (machine learning) ; Superconductivity ; Gradient boosting ; Thermal conductivity ; Limiting ; High-temperature superconductivity Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; X-ray Diffraction in Crystallography
Authors: Yuanhao Li
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Fluid density regulation under nanoconfinement revealed by machine learning Fluids under nanoscale confinement differ - and often dramatically - from their bulk counterparts. A notorious feature of nanoconfined fluids is their inhomogeneous density profile along the confining dimension, which plays a key role in many fluid structural and transport phenomena in nanopores. Nearly five decades of theoretical efforts on predicting this phenomenon (fluid layering) have shown that its complexity resists purely analytical treatments; as a consequence, nearly all current approaches make extensive use of molecular simulations, and tend not to have generalizable predictive capabilities. In this work, we demonstrate that machine-learning-based models (in particular, a random forest model), trained upon large molecular simulation data sets, can serve as reliable surrogates in lieu of further molecular simulation. We show that this random forest model has excellent interpolative capabilities over a wide range of temperatures and confining lengthscales, and even has modest extrapolative ability. These results provide a promising pathway forward for developing models of nanoconfined fluid properties that are generalizable, lower cost than "pure" molecular simulation, and sufficiently predictive for fluids-in-nanopores practitioners. Range (aeronautics) ; Feature (linguistics) ; Random forest ; Key (lock) ; Current (fluid) ; Nanoscopic scale Block Copolymer Self-Assembly ; Nanopore and Nanochannel Transport Studies ; Phase Equilibria and Thermodynamics
Authors: Clarence Shane Moreno | Jemuel James Penaso | Earl Valencia | Callista Joyce Cortez | Bren Daniel Ebriega | Jessa Pasaje | Renner Danielle Serognas | Allison Dale Panaligan | Rizalinda L de Leon | Karl Ezra Pilario
Source: Artificial Intelligence Chemistry (article, 2026-02-15)
Field: Chemistry — Inorganic Chemistry

Finding low-energy experimental metal-organic frameworks for carbon capture via Bayesian optimization in the manifold subspace of geometric descriptors Carbon capture, utilization, and storage (CCUS) technologies are now being developed to meet the global net-zero emissions target. Due to their high surface area and high porosity, metal-organic frameworks (MOFs) are promising solid sorbent candidates for post-combustion carbon capture. Recent studies now use machine learning (ML) to accelerate the high-throughput screening of MOFs. However, most studies only rely on supervised learning to do structure-to-property predictions and offer little understanding about the MOF chemical space. In this paper, we aim to provide a more interpretable ML workflow for finding best-performing MOFs for carbon capture using manifold learning and Bayesian optimization. We posed an optimization problem whose objective is to find MOFs from the CoRE MOF 2019 database that minimize the required energy when simulated in a pressure-swing adsorber, subject to having high CO 2 purity and high CO 2 /N 2 selectivity. Different from existing literature, we introduce uniform manifold approximation and projection (UMAP) to first embed the pore geometry descriptors data in a 2-D manifold subspace, hence visualizing the MOF chemical space. We then explored the possibility of performing Bayesian optimization in the UMAP subspace to search MOFs more efficiently. Our results include a 2-D mapping of MOFs, a ranking of best MOFs, and an analysis of regions in the geometric descriptor subspace that led to best process performance. We hope that these contributions can help increase our understanding of the relationships between the material-level and process-level metrics of MOFs for carbon capture. • Explored a 2-D chemical space of MOFs by using UMAP on their geometric descriptors. • Evaluated 50 MOFs in a PSA simulation via Aspen Adsorption Suite. • Used BayesOpt on the subspace to find low-energy, high purity, and selective MOFs. • Identified narrow ranges of descriptors that lead to cost-effective carbon capture. • Best MOFs are in the UMAP neighborhoods of IFEPUG and NIKZAJ. Subspace topology ; Bayesian optimization ; Workflow ; Manifold (fluid mechanics) ; Nonlinear dimensionality reduction ; Ranking (information retrieval) ; Bayesian probability ; Manifold alignment ; Projection (relational algebra) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Shimul Biswas | Krishna Kishore Mahato
Source: Physics of Life Reviews (article, 2026-01-29)
Field: Materials Science — Biomaterials

Biophysical approaches to cancer diagnostics: Collagen biomarkers in colorectal cancer Collagen remodeling in the extracellular matrix drives alterations in the physical, mechanical, and optical properties of tissues, representing a fundamental biophysical hallmark of colorectal cancer (CRC) progression. These changes, reflected in variations in collagen composition, molecular orientation, and cross-linking density, transform tissue stiffness, elasticity, and anisotropy, directly influencing cancer cell behavior and mechanotransduction. Understanding these structural and physical transitions requires analytical methods capable of probing the molecular origins of biomechanical alteration in situ. Spectroscopic and optical methodologies provide high-resolution, label-free detection of the vibrational, optical, and mechanical alteration associated with collagen remodeling. These techniques capture the shifts in molecular vibrational modes, nonlinear optical responses, and scattering anisotropy that arise from collagen fibril alignment, structural disorder, and cross-link formation, thereby connecting molecular architecture to measurable macroscale biomechanics. The integration of multimodal spectroscopy and incorporation of machine learning algorithms further enhance diagnostic performance by resolving complex tissue heterogeneity and improving tumor margin detection. Overall, this review demonstrates how spectroscopic techniques can be applied in the biomedical field for cancer detection by analyzing collagen remodeling and structural alterations. Extracellular matrix ; Colorectal cancer ; Cancer ; Force spectroscopy ; Collagen fibres ; Collagen fibril ; Extracellular ; Collagen VI ; Intracellular Collagen: Extraction and Characterization ; Spectroscopy Techniques in Biomedical and Chemical Research ; Optical Coherence Tomography Applications
Authors: Ravinder Gwal | Deepak Bhardwaj | Jatin Kumar | Vipin Kashyap | Sandeep Rathee | Deepak Chhabra
Source: Journal of Thermoplastic Composite Materials (article, 2026-03-28)
Field: Materials Science — Polymers and Plastics

Modelling and analysis of fatigue strength of bilayer ABS-PETG fabricated via FFF using FEA framework and hybrid algorithm The Acrylonitrile Butadiene Styrene (ABS) and Polyethylene Terephthalate Glycol (PETG) are widely used polymers in industrial applications and employed as functional parts which undergo cyclic loading. Bilayer fabrication i.e. ABS-PETG via Fused Filament Fabrication (FFF) uses the benefits of both polymers and is utilized in automotive, aerospace, biomedical and consumer sector. In the present work, 20 ABS-PETG bilayer specimens (runs) retrieved through optimal custom design (OCD) have been fabricated using FFF to investigate their fatigue behaviour by selecting three most important process parameters with three levels i.e. layer thickness (0.1 mm, 0.2 mm, 0.3 mm), material composition of ABS-PETG (40:60, 50:50, 60:40) and raster angle ( 0°, 45°, 90°). In total, 20 × 4 × 3 = 240 specimens have been fabricated as per ASTM D638 type-IV (flat dog bone) using Creality-Ender 3 V2 printer. The fatigue strength has been investigated by applying ASTM D7791-22 uniaxial tension-tension fatigue testing (UATTFT) method using Biss Nano fatigue testing machine. The finite element analysis (FEA) has been performed in ANSYS to validate the experimental fatigue strength of proposed specimen where the results has been found to be in good agreement with simulation data. The response surface method (RSM) and hybrid algorithm artificial neural network and genetic algorithm (NN-GA) have been applied for modelling and optimization. The NN-GA enhanced the tensile-tensile fatigue strength by 4.36 % on input layer thickness of 0.15 mm, composition (ABS-PETG) of 45:55 and raster angle 0.025°, which performed better than RSM. Enhanced fatigue strength attained by optimized parameters within the proposed framework makes components such as mounting fixtures, brackets and prosthetic achieve longer service life under cyclic loading. Acrylonitrile butadiene styrene ; Fatigue limit ; Fabrication ; Finite element method ; Bilayer ; Raster graphics ; Fused filament fabrication ; Polymer ; Polyethylene terephthalate Polymer crystallization and properties ; Injection Molding Process and Properties ; Composite Material Mechanics
Authors: Mingxing Zhang | Nicola Rossberg | Simone Innocente | Katarzyna Komolibus | Rekha Gautam | Barry O'Sullivan | Luca Longo | Andrea Visentin
Source: ArXiv.org (article, 2026-03-19)
Field: Chemistry — Analytical Chemistry

SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data In recent years, machine learning models have been increasingly applied to spectroscopic datasets for chemical and biomedical analysis. For their successful adoption, particularly in clinical and safety-critical settings, professionals and researchers must be able to understand and trust the reasoning behind model predictions. However, the inherently high dimensionality and strong collinearity of spectroscopy data pose a fundamental challenge to model explainability. These properties not only complicate model training but also undermine the stability and consistency of explanations, leading to fluctuations in feature importance across repeated training runs. Feature extraction techniques have been used to reduce the input dimensionality; these new features hinder the connection between the prediction and the original signal. This study proposes SHAPCA, an explainable machine learning pipeline that combines Principal Component Analysis (for dimensionality reduction) and Shapely Additive exPlanations (for post hoc explanation) to provide explanations in the original input space, which a practitioner can interpret and link back to the biological components. The proposed framework enables analysis from both global and local perspectives, revealing the spectral bands that drive overall model behaviour as well as the instance-specific features that influence individual predictions. Numerical analysis demonstrated the interpretability of the results and greater consistency across different runs. Interpretability ; Consistency (knowledge bases) ; Curse of dimensionality ; Stability (learning theory) ; Collinearity ; Pipeline (software) ; Feature (linguistics) ; Principal component analysis ; Exploratory data analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Machine Learning in Materials Science