jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alejandro A. Franco | Francisco Fernandez | Diego E. Galvez‐Aranda | Rashen Lou Omongos | Utkarsh Vijay
Source:  (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

A Multi-Technique Machine Learning Workflow for Optimizing the Manufacturing Process of Functional Layers in Electrochemical Energy Devices Abstract In this presentation we will share our latest developments revolving around the utilization of Artificial Intelligence/Machine Learning techniques to optimize the manufacturing process of functional layers in lithium ion batteries (electrodes) and polymer electrolyte membrane fuel cells (gas diffusion layers -GDL-). We have used several machine learning techniques (including unsupervised, supervised and deep learning techniques) to predict the influence of manufacturing process parameters (e.g. slurry formulation, drying temperature, electrode calendering degree, GDL fiber diameter) on the properties of the functional layers (e.g. conductivity, tortuosity factor, porosity). We have also applied deep learning techniques in order to derive surrogate models mimicking the behavior of physics-based numerical models simulating the manufacturing process of the electrodes. The so-derived machine learning models are then used in combination with optimization algorithms in order to perform inverse design of the manufacturing processes, i.e. predicting which manufacturing parameters we need to adopt in order to maximize and/or minimize given properties. In this presentation, we review these developments, including the different workflows that we designed and discuss our manufacturing digitalization vision towards accelerated design of functional layers for electrochemical energy device applications. We believe that this approach gives the promise to reduce costs and save time in the industrialization of these devices towards a more sustainable world. Process (computing) ; Workflow ; Flexibility (engineering) ; Automation ; Dimensionality reduction ; Advanced manufacturing ; Calendering ; Computer-integrated manufacturing ; Learning curve Machine Learning in Materials Science
Authors: Javier Gómez | Jesús Oroya | Jorge Doménech
Source:  (article, 2025-06-16)
Field: Materials Science — Polymers and Plastics

Advancing Polymeric Yarn Manufacturing: A Hybrid Modelling Approach Combining AI and Molecular Dynamics This study introduces a novel hybrid modelling framework for optimizing polymeric yarn extrusion process. The synthetic textile industry faces significant challenges, including high costs and inefficiencies due to the complex interplay between material properties and manufacturing conditions. Traditional trial-and-error methods are costly and inefficient. The proposed hybrid model combines atomistic simulations, surrogate modelling, and machine learning techniques, specifically neural networks, to predict and control the microstructural and mechanical properties of polymeric yarns. The atomistic model, implemented using coarse-grained molecular dynamics, captures essential molecular interactions and links crystallinity and orientation to mechanical properties. A surrogate model, trained on molecular dynamics data, provides real-time predictions of mechanical properties such as tenacity and stress-strain behaviour. The final hybrid model integrates these approaches, using neural networks to predict microstructural properties from process variables and mechanical properties from microstructural data. Validation through experimental comparisons demonstrates the model ability to enhance production efficiency. Yarn ; Computer science ; Molecular dynamics ; Dynamics (music) ; Materials science ; Nanotechnology ; Composite material ; Chemistry ; Physics ; Computational chemistry Textile materials and evaluations
Authors: Takeyuki Kato | Ryuki Kayano | Takahiro Ohkubo
Source: ChemRxiv (preprint, 2025-05-14)
Field: Materials Science — Ceramics and Composites

Machine-Learning Molecular Dynamics Study on the Structure and Glass Transition of Calcium Aluminosilicate Glasses Calcium aluminosilicate (CAS) glass systems represent an important class of materials for industrial applications due to their superior thermal and mechanical properties. Although CAS glasses have been extensively studied, the structure–property relationships--particularly in the peraluminous region (Al2O3/CaO >1)--remain insufficiently understood. Experimental studies have identified the presence of five-coordinated aluminum (Al5) depending on the Al2O3 content; however, classical molecular dynamics (CMD) simulations have struggled to accurately reproduce the aluminum coordination environment. To address this limitation, we developed a MLP tailored for CAS systems, trained on a comprehensive DFT-MD dataset and refined using a fine-tuning approach. Melt-quench simulations were then carried out to model CAS glass structures. The resulting structures from MLMD accurately reproduced both experimental glass densities and the fractions of Al5, including the observed increase in Al5 and oxygen triclusters (TBO) in the peraluminous region. In addition, we performed heating simulations to evaluate enthalpy changes and structural evolution as a function of temperature. Analogous to differential scanning calorimetry (DSC) experiments, the glass transition temperature was determined from the MLMD data. The compositional dependence of Al5 and TBO near the glass transition temperature (Tg) was quantitatively analyzed, providing insights into the role of aluminum in structural rearrangements. These findings demonstrate that MLMD enables accurate modeling of CAS glass structures and yields valuable insights into their thermal behavior. This approach offers a robust framework for understanding structure–property relationships in complex glass systems. Aluminosilicate ; Molecular dynamics ; Glass transition ; Calcium aluminosilicate ; Transition (genetics) ; Dynamics (music) ; Materials science ; Calcium ; Chemical physics ; Chemistry ; Composite material ; Metallurgy ; Computational chemistry ; Psychology ; Polymer ; Organic chemistry Glass properties and applications ; Pigment Synthesis and Properties
Authors: V. Diana Rakotonirina (22347515) | Marco Bragato (22347518) | Guido Falk von Rudorff (18556057) | O. Anatole von Lilienfeld (8220351)
Source: Figshare (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Hammett-InspiredProduct Baseline for Data-EfficientΔ‑ML in Chemical Space Data-hungry machine learning methods have become a new standard to efficiently navigate chemical compound space for molecular and materials design and discovery. Due to the severe scarcity and cost of high-quality experimental or synthetic simulated training data, however, data-acquisition costs can be considerable. Relying on reasonably accurate approximate legacy baseline labels with low computational complexity represents one of the most effective strategies to curb data-needs, e.g. through Δ-, transfer-, or multifidelity learning. A surprisingly effective and data-efficient baseline model is presented in the form of a generic coarse-graining Hammett-inspired product (HIP) Ansatz, generalizing the empirical Hammett equation toward arbitrary systems and properties. Numerical evidence for the applicability of HIP includes solvation free energies of molecules, formation energies of quaternary elpasolite crystals, carbon adsorption energies on heterogeneous catalytic surfaces, HOMO–LUMO gaps of metallorganic complexes, activation energies for S<sub>N</sub>2 reactions, and catalyst–substrate binding energies in cross-coupling reactions. After calibration on the same training sets, HIP yields an effective baseline for improved Δ-machine learning models with superior data-efficiency when compared to previously introduced specialized domain-specific models. Baseline (sea) ; Calibration ; Product (mathematics) ; Chemical space ; Space (punctuation) ; Computation ; Solvation ; Component (thermodynamics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Catalysis and Oxidation Reactions
Authors: Bhupalee Kalita | Roman Zubatyuk | Dylan M. Anstine | Maike Bergeler | Volker Settels | Conrad Stork | Sebastian Spicher | Olexandr Isayev
Source: ChemRxiv (article, 2025-12-07)
Field: Materials Science — Materials Chemistry

AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry Artificial neural network ; Generalizability theory ; Scaling ; Molecule ; Quantum chemical ; Training set ; Quantum chemistry ; Electronic structure ; Radical Machine Learning in Materials Science ; Radical Photochemical Reactions ; Advanced Chemical Physics Studies
Authors: V. Diana Rakotonirina | Marco Bragato | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: Journal of Chemical Theory and Computation (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Hammett-Inspired Product Baseline for Data-Efficient Δ-ML in Chemical Space Data-hungry machine learning methods have become a new standard to efficiently navigate chemical compound space for molecular and materials design and discovery. Due to the severe scarcity and cost of high-quality experimental or synthetic simulated training data, however, data-acquisition costs can be considerable. Relying on reasonably accurate approximate legacy baseline labels with low computational complexity represents one of the most effective strategies to curb data-needs, e.g. through Δ-, transfer-, or multifidelity learning. A surprisingly effective and data-efficient baseline model is presented in the form of a generic coarse-graining Hammett-inspired product (HIP) Ansatz, generalizing the empirical Hammett equation toward arbitrary systems and properties. Numerical evidence for the applicability of HIP includes solvation free energies of molecules, formation energies of quaternary elpasolite crystals, carbon adsorption energies on heterogeneous catalytic surfaces, HOMO-LUMO gaps of metallorganic complexes, activation energies for S<sub>N</sub>2 reactions, and catalyst-substrate binding energies in cross-coupling reactions. After calibration on the same training sets, HIP yields an effective baseline for improved Δ-machine learning models with superior data-efficiency when compared to previously introduced specialized domain-specific models. Baseline (sea) ; Chemical space ; Product (mathematics) ; Space (punctuation) ; Solvation ; Calibration ; Computation ; Data space Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Fault Detection and Control Systems
Authors: Bowen Han | Shuang Yu | Sibo Zhan | Xia Yan | Yang Li | Zhenghang Zhu | Hong Xin | Jawwad A. Darr | Zhongbiao Wu | Xiaole Weng
Source: Advanced Functional Materials (article, 2025-10-05)
Field: Materials Science — Materials Chemistry

Accelerating the Discovery of High‐Entropy Oxide Catalysts through High‐Throughput Synthesis and Rapid Screening Abstract High‐entropy oxides (HEOs) are emerging as a promising material for next‐generation catalysts. However, their versatile compositions and complex atomic arrangements present great challenges in rapid and scale‐up synthesis, identifying the active sites, and unraveling structure‐performance relationships. Herein, an all‐in‐one workflow is developed to synthesize, characterize, understand, and apply HEOs in catalysis, combining high‐throughput synthesis (≈100 g/h in pilot scale), rapid activity testing, and data‐driven screening. The workflow involves synthesis of HEOs via a continuous hydrothermal flow reactor, integrated with rapid thermal processing. Catalytic testing is conducted using a rapid Joule heating system and a machine learning‐based model is used to unveil the synthesis‐structure‐performance relationships. As a proof‐concept, this workflow is demonstrated to continually produce 52 HEOs existing in eight distinct crystal structures, with 16 metal elements. This rapid approach, supplemented by data‐driven analysis to identify the champion samples, represents ≈70% time saving compared to more conventional make‐and‐test methods. Workflow ; Catalysis ; Oxide ; Characterization (materials science) ; Hydrothermal circulation ; Metal ; Thermal Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions ; Magnetic and transport properties of perovskites and related materials
Authors: Sebastian Wissel | Jonas Scheunert | Aaron Dextre | Shamail Ahmed | Andreas Beyer | Kerstin Volz | Bai‐Xiang Xu
Source: Materials & Design (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

STEM diffraction pattern analysis with deep learning networks Accurate grain orientation mapping is essential for understanding and optimising the performance of polycrystalline materials, particularly in energy applications. Lithium nickel oxide (LiNiO 2 ) is a promising cathode material for next-generation lithium-ion batteries, and its electrochemical behaviour is closely linked to microstructural features such as grain size and crystallographic orientation. Traditional orientation mapping methods—manual indexing, template matching (TM), or Hough transform-based techniques—are often slow and noise-sensitive, especially when processing complex or overlapping patterns, creating a bottleneck in large-scale microstructural analysis. This work presents a machine learning-based approach for predicting Euler angles directly from scanning transmission electron microscopy (STEM) diffraction patterns (DPs), enabling automated, high-resolution orientation mapping for nanoscale microstructure analysis. Three deep learning architectures—convolutional neural networks (CNNs), Dense Convolutional Networks (DenseNets), and Shifted Windows (Swin) Transformers—are evaluated using an experimentally acquired dataset labelled via a commercial TM algorithm. While the CNN model serves as a baseline, both DenseNets and Swin Transformers achieve superior performance, with the Swin Transformer yielding the highest evaluation scores and most consistent microstructural predictions. The resulting crystal maps reveal clear grain boundaries and coherent intra-grain orientation distributions, underscoring the potential of advanced machine learning models for high-throughput microstructural characterisation. • Machine learning enables fast, high-resolution STEM orientation mapping. • Deep networks trained on experimental 4D-SPED data predict Euler angles. • Swin Transformer yields highest accuracy and intra-grain consistency. • Workflow includes systematic hyperparameter optimisation for ML models. • Approach supports scalable microstructure analysis in energy materials. Deep learning ; Convolutional neural network ; Pattern recognition (psychology) ; Artificial neural network ; Microstructure ; Orientation (vector space) ; Electron backscatter diffraction ; Transformer Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Omid Tarkhaneh | Sharene D. Bungay | Raymond A. Poirier | Robert C. Mawhinney
Source: Theoretical Chemistry Accounts (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Neural network potentials using matrix-based descriptors Artificial neural network ; Matrix (chemical analysis) ; Chemistry ; Biological system ; Computer science ; Computational chemistry ; Artificial intelligence ; Biology ; Chromatography Machine Learning in Materials Science ; Neural Networks and Applications ; Computational Drug Discovery Methods
Authors: R.J. Wang | Yi Zhao | Ziqi Ye | Bowen Liu | Yucheng Li | DongLong Chen
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Automated microstructure segmentation in titanium alloys using deep learning techniques Deep learning ; Segmentation ; Titanium alloy ; Microstructure ; Image segmentation Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Metallurgy and Material Forming
Authors: Zhihui Wang | Chengyu Fu | Chengzhong Xu | Huaijuan Zang | Jing Fang | Yongsheng Ren | Shu Zhan
Source: Computational Materials Science (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Improved crystal graph networks with periodic invariance from a global perspective Perspective (graphical) ; Graph ; Theoretical physics ; Mathematics ; Theoretical computer science ; Computer science ; Physics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Chao Peng | Bing Wang | Lie Wu | Haoqu Jin | Yutang Li | Wenxia Gao | Jie Zhou | Guolai Jiang | Chen Wang | Jiahong Wang | Xingchen He | Denis Kramer | Paul K. Chu | Xue‐Feng Yu
Source: Angewandte Chemie (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

AI‐Driven Discovery and Molecular Engineering Design for Enhancing Interface Stability of Black Phosphorus Abstract Molecular engineering offers significant potential for developing advanced interfacial materials, yet the complexity of organic molecules poses challenges in discovering optimal structures. This study leveraged large language model (LLM) and machine learning (ML) to accelerate molecular discovery and guide molecular engineering for enhancing the stability of black phosphorus (BP), a promising 2D semiconductor but rapidly degrades when exposed to oxygen and moisture. By utilizing GPT‐4o, molecular groups such as ─SiR 3 , ─PR 2 , ─SH, and ═NH that interact effectively with BP were identified and a high‐throughput workflow employing graph neural networks (GNNs) models was developed to successfully predict and screen 662 promising candidates from over 117 million molecules. These candidates were validated by density functional theory (DFT) simulations and experiments, with synthesis protocols guided by GPT‐4o, achieving great interfacial stabilization of BP for up to 24 days under ambient conditions. Furthermore, a new synergistic molecular engineering strategy was proposed by incorporating functional head, linker, and tail groups of molecules to even enable the use of hydrophilic molecules to stabilize BP surface, overcoming traditional design limitations. This work highlights the AI technologies not only in optimizing BP interfacial stability but also in broader aspects of molecular engineering for various materials. Black phosphorus ; Molecular machine ; Workflow ; Molecular engineering ; Nanotechnology ; Computer science ; Molecule ; Linker ; Materials science ; Interface (matter) ; Combinatorial chemistry ; Biochemical engineering ; Chemistry ; Database ; Organic chemistry ; Engineering 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Na Yu | Maoqi Wang | Xiaodie Chen | Rong Liu | Liang Zou | Mao Shu
Source: Research Square (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

Molecular Design and Virtual Screening of EZH2 Inhibitors Based on GRU Networks Virtual screening ; Drug discovery ; Cheminformatics ; EZH2 ; Small molecule ; Deep learning ; Regulator Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Samed Ayhan Özsoy | Safiye Meriç Acıkel | Ahmet Uluslu | Cem Aydemir
Source: Plasma Processes and Polymers (article, 2025-10-31)
Field: Materials Science — Surfaces, Coatings and Films

Wettability Estimation on Polymer Film Surfaces With Artificial Neural Network Methodology ABSTRACT When printing on polymer film surfaces with low ink acceptance, the surface energy and wettability need to be increased to achieve good adhesion. To alter the surface energy, wettability or biocompatibility of polymer films, their surfaces need to be chemically functionalized. Nevertheless, given the myriad varieties of film materials to be printed, predicting their ideal surface energy for optimal printing proves to be a challenging task. In the last part of the study, low‐cost, fast, and safe artificial intelligence‐based modeling was used to predict the wettability, without performing contact angle testing. By using the improved structure of the ANN, the wettability of polymer films after surface treatment can be determined simply and quickly without the need for any other tests. Wetting ; Contact angle ; Polymer ; Surface energy ; Inkwell ; Surface (topology) ; Biocompatibility Surface Modification and Superhydrophobicity ; Nanomaterials and Printing Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Zhijian Liu | Yan Liu | Yuqi Zhang | Yeyu Deng | Zhong Zheng | Ruth Knibbe | Tianxiang Gao | Mingzhe Li | Ziye Wang | Qian Zhang | Xue Jia | Di Zhang | Heng Liu | Xiaohou Shao | Zhengyang Gao | Wei Li | Hao Li | Weijie Yang
Source: Angewandte Chemie International Edition (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Universal Catalyst Design Framework for Electrochemical Hydrogen Peroxide Synthesis Facilitated by Local Atomic Environment Descriptors A universal design framework for high-performance catalysts remains challenging due to diverse structures and active sites. We developed a framework integrating weighted atom-centered symmetry function (wACSF) descriptors with machine learning, microkinetic modeling, and high-throughput screening. The wACSF descriptors unify geometric and chemical characteristics of active sites across different catalyst families. ML models trained on wACSF accurately predicted adsorption free energies of hydroxyl (ΔG<sub>OH</sub> <sub>*</sub>, R<sup>2</sup> = 0.84) and oxygen (ΔG<sub>O*</sub>, R<sup>2</sup> = 0.91) for intermetallic alloys, metal oxides, perovskites, and single-atom catalysts in the two-electron water oxidation reaction (2e<sup>-</sup> WOR). Density functional theory and microkinetic modeling yielded a universal 2e<sup>-</sup> WOR volcano model that agreed well with experiments. High-throughput screening with ML-predicted ΔG<sub>OH*</sub> identified LiScO<sub>2</sub>, which achieved 90% H<sub>2</sub>O<sub>2</sub> Faradaic efficiency at 2.2 V vs. reversible hydrogen electrode (RHE) with 168-hour stability (82%-86% retention). Experimental activity (log(j) = 1.56) matched theoretical predictions (log(j) = 1.28) within 5% deviation at 2.4 V_RHE. This universal framework provides a general paradigm for rational catalyst design and is implemented in the Digital Catalysis Platform (DigCat), enabling efficient discovery across diverse material classes and electrochemical reactions. Catalysis ; Density functional theory ; Rational design ; Electrochemistry ; Adsorption ; Heterogeneous catalysis ; Hydrogen ; Transition metal ; Hydrogen peroxide Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction