jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kento Murakami | Yudai Yamaguchi | Yo Kato | Kazuki Ishikawa | Naoto Tanibata | Hayami Takeda | Masanobu Nakayama | Masayuki Karasuyama
Source: Digital Discovery (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Deep learning based SEM image analysis for predicting ionic conductivity in LiZr <sub>2</sub> (PO <sub>4</sub> ) <sub>3</sub> -based solid electrolytes Lithium-ion-conductive oxide materials have attracted considerable attention as solid electrolytes for all-solid-state batteries. Ionic conductivity ; Electrolyte ; Conductivity ; Deep learning ; Image processing ; Fast ion conductor ; Image (mathematics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Han-Xu Zhang | Han Cai | Junyu Chen | Gang Fang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

A Crystal Plasticity Surrogate Model to Predict the Mechanical Response of Magnesium Alloys Plasticity ; Magnesium ; Ultimate tensile strength ; Anisotropy ; Microstructure ; Deformation (meteorology) ; Reliability (semiconductor) ; Surrogate model ; Experimental data Magnesium Alloys: Properties and Applications ; Metal Forming Simulation Techniques ; Elasticity and Material Modeling
Authors: Mahdi Kchaou | Francesco Contino | Diederik Coppitters
Source: arXiv (Cornell University) (preprint, 2025-12-29)
Field: Materials Science — Materials Chemistry

Revealing design archetypes and flexibility in e-molecule import pathways using Modeling to Generate Alternatives and interpretable machine learning Given the central role of green e-molecule imports in the European energy transition, many studies optimize import pathways and identify a single cost-optimal solution. However, cost optimality is fragile, as real-world implementation depends on regulatory, spatial, and stakeholder constraints that are difficult to represent in optimization models and can render cost-optimal designs infeasible. To address this limitation, we generate a diverse set of near-cost-optimal alternatives within an acceptable cost margin using Modeling to Generate Alternatives, accounting for unmodeled uncertainties. Interpretable machine learning is then applied to extract insights from the resulting solution space. The approach is applied to hydrogen import pathways considering hydrogen, ammonia, methane, and methanol as carriers. Results reveal a broad near-optimal space with great flexibility: solar, wind, and storage are not strictly required to remain within 10% of the cost optimum. Wind constraints favor solar-storage methanol pathways, while limited storage favors wind-based ammonia or methane pathways. Flexibility (engineering) ; Set (abstract data type) ; Margin (machine learning) ; Renewable energy ; Supervised learning ; Space (punctuation) Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Carbon dioxide utilization in catalysis
Authors: Salifu, Samuel Enemona
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-07)
Field: Materials Science — Materials Chemistry

Digital Analytical Systems: Python–SQL Simulation of Industrial Chemical Behaviour and Process Control Digital analytical systems are transforming modern process chemistry and industrial optimisation by enabling rapid simulation of chemical behaviour using integrated computational tools. This technical project presents a Python–SQL framework designed to model reaction behaviour, monitor process parameters, and support predictive decision-making in industrial environments. The system applies core principles of analytical chemistry alongside machine-learning-ready data pipelines to simulate chemical kinetics, contaminant behaviour, and process deviations. The workflow demonstrates how digital tools can enhance chemical quality control, process safety, and efficiency in resource-constrained settings. This work contributes to ongoing efforts to digitise industrial chemistry operations and provides a pathway for developing scalable analytical automation systems in emerging economies. Workflow ; Process control ; Process (computing) ; Automation ; Process modeling ; Chemical process ; Work in process ; Process systems ; Chemical industry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Process Optimization and Integration
Authors: Seokhyun Choung (12907609) | Miyeon Kim (84303) | Jinuk Moon (22640122) | Jeong Woo Han (1276569)
Source: Figshare (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

From AtomicMotif to Realistic Single Atom Catalyststhrough Machine Learning Interatomic Potentials Metal–nitrogen–carbon (M-N-C) catalysts demonstrate exceptional electrochemical performance, with density functional theory (DFT) simulations successfully guiding atomic-scale optimization of coordination environments. However, recent experiments reveal that catalyst performance depends on phenomena beyond DFT’s spatiotemporal limits. This Perspective examines how machine learning interatomic potentials (MLIPs) bridge this critical gap, achieving orders-of-magnitude acceleration while maintaining near-DFT accuracy. MLIPs capture previously inaccessible phenomena spanning atomic to mesoscopic scales, including structural complexity and electrolyte dynamics. These capabilities reveal how support architecture, collective site interactions, solvation, and reaction kinetics at the mesoscale determine rate-limiting steps in electrochemical reactions. By connecting atomic-level understanding to experimentally relevant scales, MLIPs transform catalyst design from isolated site optimization to comprehensive multiscale engineering. Mesoscopic physics ; Catalysis ; Molecular dynamics ; Interatomic potential ; Density functional theory ; Atom (system on chip) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Alexander Müller | Juan Cardenas-Cartagena | Robert Pollice
Source: ChemRxiv (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Uncovering Internal Prediction Mechanisms of Transformer-Based Chemical Foundation Models Supervised deep learning has become a standard approach to deliver competitive predictive tools that allow relating the structure of molecules and their physicochemical features to properties such as binding to protein targets, performance as electronic materials, and reactivity. However, efforts to understand how these models learn and how specific predictions can be explained are still limited in chemistry, which hinders trust in these predictions. To overcome both the current widespread approach of considering supervised deep learning models as black boxes and the limitations of explainable deep learning based on feature attribution, we introduce two general methods from the field of mechanistic interpretability to molecular property prediction. Specifically, we leverage ablation and adapt existing techniques into the regression lens to inspect model predictions of the Transformer-based ChemBERTa foundation model. Our results for 3 ChemBERTa-based models finetuned on distinct datasets allow us to propose the internal mechanisms operable within each of the corresponding model layers that lead to the final predictions. Our results are a stepping stone towards more trustworthy deep learning models in the molecular domain.<br/> Interpretability ; Deep learning ; Leverage (statistics) ; Field (mathematics) ; Trustworthiness ; Black box ; Foundation (evidence) ; Feature (linguistics) ; Supervised learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Y. Jin
Source: Applied and Computational Engineering (article, 2025-01-12)
Field: Chemistry — Inorganic Chemistry

Computational Simulations of Metal-Organic Framework Structures Based on Second Building Units Metal-organic frameworks (MOFs) are crystalline porous materials composed of metal clusters and organic linkers with tunable structures and large surface areas, making them ideal for gas storage, separation, and catalytic applications. The development of new MOFs and the screening of MOFs after synthesis are usually experimental, but the cost and time required for experiments are very large. Interestingly, computational simulations have become a powerful tool to accelerate the discovery of new MOFs structures. This study focuses on the computational design of MOFs based on secondary building units (SBUs) and explores the effects of different linkers on their geometry and properties. Using techniques such as density functional theory (DFT) and machine learning (ML), MOFs can be screened for great gas adsorption and storage properties. The results show that computational methods have the potential to predict and optimize MOFs structures, but they are still limited in accurately simulating dynamic environmental conditions and complex chemical reactions. These findings help advance the design of MOFs with tailored properties for specific industrial applications. Computer science ; Environmental science Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Shah Md. Maruf Hasan | Mahmuda Akter
Source: Tekstilna industrija (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Predicting the effect of fabric properties on the thermal insulation of sportswear by using artificial neural network and multiple linear regression approaches The study looks at two machine learning methods-multiple linear regression and artificial neural networks-to predict the thermal insulation value, known as CLO, of knit sportswear fabrics. The artificial neural network is created using feed-forward backpropagation and the trainlm training function in MATLAB. Its weights and basic values are adjusted using the Levenberg-Marquardt optimization method. The network uses the sigmoid transfer function to set the layer output and track its performance. When it comes to accuracy and how well it handles complex situations, the artificial neural network outperforms the multiple linear regression model. It better captures how thermal insulation values relate to fabric qualities. For the artificial neural network model, the mean absolute error percentage was 2.2771, the root mean squared error was 0.0017, and the coefficient of determination was 0.9268. The multiple linear regression model had a coefficient of determination of 0.8046 and mean absolute error percentages of 4.4170 and 0.0027. Compared to a time-consuming trial-and-error approach, the study shows that artificial neural networks are a better way to predict thermal insulation in textiles. This emphasizes how important these models are for creating energy-efficient designs and improving material engineering. Artificial neural network ; Sigmoid function ; Linear regression ; Backpropagation ; Mean squared error ; Approximation error ; Mean absolute percentage error ; Coefficient of determination ; Transfer function Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Thermoregulation and physiological responses
Authors: Freederia AI Researcher
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-21)
Field: Materials Science — Surfaces, Coatings and Films

Automated Resolution Enhancement and Artifact Mitigation in AES Data Analysis Using Adaptive Sparse Filtering and Deep Learning Reconstruction (AES-ASFDLR) **Abstract:** This paper introduces a novel approach to enhance spatial resolution and mitigate artifacts in Auger electron spectroscopy (AES) data, a critical limitation in materials characterization. Addressing the constraint of limited signal-to-noise ratio and scattering effects, AES-ASFDLR integrates adaptive sparse filtering (ASF) with a deep learning reconstruction network. ASF selectively filters noise while preserving crucial spectral information, followed by a convolutional neural network (CNN) architecture trained on synthetic and real AES data to reconstruct high-resolution images. This framework allows for a 10-billion-fold amplification recognize subtle features – represents a significant advancement for spatial analysis in Auger spectroscopy. The system's adaptability and numerically enhanced resolution open new avenues for materials science, particularly in semiconductor and thin-film applications. It is designed for immediate implementation by AES researchers and engineers. Deep learning ; Convolutional neural network ; Noise (video) ; Artifact (error) ; Pattern recognition (psychology) ; Artificial neural network ; Iterative reconstruction ; Flexibility (engineering) ; Adaptability Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Shaojing Dong | Weihan Sun | Xiuli SHEN
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction Method for Crystal Plasticity Constitutive Parameters in Single-Crystal Superalloys via Two-Stage Tiered Mapping Neural Network Based on Nanoindentation Nanoindentation ; Crystal plasticity ; Plasticity ; Superalloy ; Artificial neural network ; Constitutive equation Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Metallurgy and Material Forming
Authors: Chaoqiang Feng (6879674) | Bin Jiang (196018)
Source: Figshare (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Machine LearningAccelerated Finite-Field Simulationsfor Electrochemical Interfaces Electrochemical interfaces are of fundamental importance in electrocatalysis, batteries, and metal corrosion. Finite-field methods are one of the most reliable approaches for modeling electrochemical interfaces in complete cells under realistic constant-potential conditions. However, previous finite-field studies have been limited to either expensive ab initio molecular dynamics or less accurate classical descriptions of electrodes and electrolytes. To overcome these limitations, we present a machine learning-based finite-field approach that combines two neural network models: one predicts atomic forces under applied electric fields, while the other describes the corresponding charge response. Both models are trained entirely on first-principles data without employing any classical approximations. As a proof-of-concept demonstration in a prototypical Au(100)/NaCl(aq) system, this approach accelerates fully first-principles finite-field simulations by roughly 4 orders of magnitude compared to ab initio molecular dynamics, allowing the extrapolation to cell potentials beyond the training range and accurate prediction of Helmholtz capacitance. Interestingly, we reveal a turnover of both density and orientation distributions of interfacial water molecules at the anode, arising from competing interactions between the positively charged anode and adsorbed Cl<sup>–</sup> ions with water molecules as the applied potential increases. This novel computational scheme shows great promise in efficient first-principles modeling of large-scale electrochemical interfaces under potential control. Extrapolation ; Helmholtz free energy ; Artificial neural network ; Ab initio ; Molecular dynamics ; Electrochemistry ; Orientation (vector space) ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Spectroscopy and Quantum Chemical Studies
Authors: Dmitriy D. Matyushin | Anastasia Yu. Sholokhova
Source: Russian Journal of Physical Chemistry A (article, 2025-10-28)
Field: Chemistry — Spectroscopy

A Comparison of Different Approaches to Predicting Gas Chromatographic Retention Indices for Aromatic and Heterocyclic Compounds Predicting retention indices is of great importance when improving the reliability of chromatography-mass spectrometry analysis. The main means of predicting was once linear equations, constructed for narrow classes of chemical compounds using molecular descriptors. Universal models based on neural networks trained with large databases, have in recent years acquired great importance. The problem of predicting retention indices by means of quantum chemistry has also been discussed in the literature. The aim of this work was to compare all these approaches using 45 aromatic compounds (mostly nitrogen-containing heterocycles) as an example. It is shown that the AIRI neural network is the most accurate and versatile model. Narrowly focused models based on linear regression and molecular descriptors can achieve almost the same (or even better accuracy) for a narrow class of compounds. The least resource-intensive means of quantum chemistry and molecular dynamics cannot reliably estimate the energy of intermolecular interactions, so predicting retention indices ab initio remains difficult for the foreseeable future. Artificial neural network ; Kovats retention index ; Linear regression ; Quantum chemistry ; Quantum chemical ; Intermolecular force ; Reliability (semiconductor) ; Ab initio ; Retention time Analytical Chemistry and Chromatography ; Adsorption, diffusion, and thermodynamic properties of materials ; Chemical Thermodynamics and Molecular Structure
Authors: Hemant Gupta | Hemang Seth | S. Kaushik | Rohit Mogli | Riddhika Mahalanabis | B. Ashok
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting glass transition temperature using molecular structure factors: organic molecular compounds &amp; polymers Glass transition ; Amorphous solid ; Molecular descriptor ; Polymer ; Radius of gyration ; Molecular model ; Molar mass ; Melting point Machine Learning in Materials Science ; Material Dynamics and Properties ; Phase Equilibria and Thermodynamics
Authors: Hyun Gyu Park (22113670) | Gi Beom Sim (20664504) | Jung Woon Yang (1796149) | Soohaeng Yoo Willow (1390212) | David ChangMo Yang (22113673) | Chang Woo Myung (3800725)
Source: Figshare (article, 2025-08-23)
Field: Materials Science — Materials Chemistry

A Bayesian CommitteeMachine-Based Force Field forOrganic Nitrogen Compounds Large-scale simulations of atomic systems play a vital role in fields, including chemistry, materials science, and more. However, they often struggle with efficiency due to high computational costs. To overcome this, machine learning force fields (MLFFs) have emerged as a promising alternative, providing accuracy comparable to density functional theory (DFT) while lowering costs. Nonetheless, variations in potential energy based on atomic environments make it challenging to apply kernel-based models across different compounds. To address this issue, we present an MLFF using the Robust Bayesian Committee Machine (RBCM) for various compounds containing carbon, nitrogen, and hydrogen (C–N–H). The MLFF is trained with first-principles calculations and molecular dynamics simulations of various C–N–H molecules. Testing these models with longer amine structures and two Diels–Alder reactions shows excellent agreement with DFT results, demonstrating that machine learning models accurately predict potential energy surfaces of organic molecules and offer opportunity for studying a wide range of C–N–H compounds. Force field (fiction) ; Bayesian probability ; Field (mathematics) ; Density functional theory ; Range (aeronautics) ; Molecular dynamics ; Organic molecules ; Bayesian inference ; Potential field Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Guikun Chen | Xu Zhang | Xiaolin Hu | Yong Liu | Yi Yang | Wenguan Wang
Source: ArXiv.org (preprint, 2025-02-26)
Field: Materials Science — Materials Chemistry

Chemical knowledge-informed framework for privacy-aware retrosynthesis learning Chemical reaction data is a pivotal asset, driving advances in competitive fields such as pharmaceuticals, materials science, and industrial chemistry. Its proprietary nature renders it sensitive, as it often includes confidential insights and competitive advantages organizations strive to protect. However, in contrast to this need for confidentiality, the current standard training paradigm for machine learning-based retrosynthesis gathers reaction data from multiple sources into one single edge to train prediction models. This paradigm poses considerable privacy risks as it necessitates broad data availability across organizational boundaries and frequent data transmission between entities, potentially exposing proprietary information to unauthorized access or interception during storage and transfer. In the present study, we introduce the chemical knowledge-informed framework (CKIF), a privacy-preserving approach for learning retrosynthesis models. CKIF enables distributed training across multiple chemical organizations without compromising the confidentiality of proprietary reaction data. Instead of gathering raw reaction data, CKIF learns retrosynthesis models through iterative, chemical knowledge-informed aggregation of model parameters. In particular, the chemical properties of predicted reactants are leveraged to quantitatively assess the observable behaviors of individual models, which in turn determines the adaptive weights used for model aggregation. On a variety of reaction datasets, CKIF outperforms several strong baselines by a clear margin. Retrosynthetic analysis ; Variety (cybernetics) ; Raw data ; Confidentiality ; Key (lock) ; Competitive advantage ; Enhanced Data Rates for GSM Evolution Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Jingdan Chen (5719433) | Nicholas E. Jackson (1348692)
Source: Figshare (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

A MachineLearning Model for Copolymer Radical ReactivityRatio Predictions with Frontier-Orbital Insights Accurate predictions of reactivity ratios (RRs) are crucial for understanding and controlling copolymerization kinetics and the resulting copolymer microstructure. While various methods have been proposed for RR prediction, prior efforts have been limited by a lack of data accessibility, model interpretability, and out-of-distribution performance on new chemical spaces. We address these challenges by assembling a data set of copolymer RRs extracted from the experimental literature and then developing a machine learning model that demonstrates robustness in predicting RRs for diverse monomers and radical chemistries. The Shapley additive explanations (SHAP) analysis of the machine learning model reveals the significant role of frontier molecular orbital (FMO) interactions, corroborating earlier RR prediction models emphasizing the bipolar reactivity of radicals in copolymerization. Importantly, this machine learning model leads to an intuitive argument based on the relative chemical potential and chemical hardness of comonomers that enables predictions of copolymerization regimes based on simple density functional theory calculations. Copolymer ; Robustness (evolution) ; Reactivity (psychology) ; Monomer ; Argument (complex analysis) ; Training set ; Set (abstract data type) ; Radical Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Organometallic Complex Synthesis and Catalysis
Authors: Annayath Maqbool | Noor Zaman Khan | Arshad Noor Siddiquee
Source: Vacuum (article, 2025-05-14)
Field: Materials Science — Biomaterials

Hybrid GA-ANN modelling for microhardness prediction in FSPed Mg-Y-Nd-Zr alloys using autoencoder-synthesized data Autoencoder ; Indentation hardness ; Materials science ; Artificial neural network ; Computer science ; Metallurgy ; Artificial intelligence ; Microstructure Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Aluminum Alloys Composites Properties