jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yucheng Ouyang | Xin Chen | Ying Liu | Honghui Shang | Zhenchuan Chen | Rongfen Lin | Xingyu Gao | Lifang Wang | Fang Li | Jiahao Shan | Haifeng Song | Huimin Cui | Xiaobing Feng | Jingling Xue
Source:  (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

TENSORMD: Accelerating Molecular Dynamics with a High-Performance Machine Learning Interatomic Potential AI has been integrated into HPC across various scientific fields, significantly enhancing performance. In molecular dynamics simulations, HPC+AI facilitates the investigation of atomic-scale physical properties using machine-learning interatomic potentials (MLIPs). However, general-purpose ML tools (e.g., TensorFlow) used in MLIPs are not optimally matched, leading to missed optimization opportunities due to the higher computational complexity and greater diversity of HPC+AI applications compared to pure AI scenarios. To address this, we introduce TensorMD, an MLIP independent of existing ML tools, enabling flexible optimizations that standard ML frameworks cannot support. TensorMD outperforms a state-of-the-art MLIP—winner of the 2020 Gordon Bell Prize and built on an ML tool—by 1.88 × on NVIDIA A100 GPU. Additionally, TensorMD was evaluated on two supercomputers with different architectures, achieving significantly reduced time-to-solution and supporting molecular dynamics simulations at scales beyond 50 billion atoms. Molecular dynamics ; Interatomic potential ; Dynamics (music) ; Computational complexity theory ; Physical system Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Lingyun Dai | Man Li | Yongjie Hu
Source: Physical Review Materials (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Machine learning for thermal transport and phonon high-order anharmonicity in high thermal conductivity materials: A case study in boron arsenide Materials with high thermal conductivity are at the forefront of research in advancing thermal management, as benchmarked by the recent discovery of cubic BAs. In this study, we utilized BAs as a prototype material to assess the predictive capabilities of a machine learning approach for thermal transport, particularly in scenarios where high-order phonon anharmonicity plays a crucial role. We developed a training methodology for the moment tensor potential based on ab initio molecular dynamics, which provides accurate predictions of atomic energies, forces, stresses, phonon dispersion relations, elastic modulus, and thermal expansion coefficients. Our approach yields quantitative predictions of thermal conductivity and phonon mean free paths, closely matching first-principles calculations and experimental measurements under varied conditions and size confinements. The predictions of pressure-dependent thermal conductivity, taking into account complex interactions from phonon anharmonicity, isotope scattering, and defect scattering, reveal intrinsic behavior resulting from competing 3-phonon and 4-phonon processes in high-quality BAs, while also showing weak pressure dependence in samples dominated by defects. This study explores the feasibility of using machine learning for simulating high-order phonon scattering and demonstrates its potential as a high-throughput computational approach in advancing thermal management solutions. Anharmonicity ; Phonon ; Thermal conductivity ; Materials science ; Phonon scattering ; Scattering ; Condensed matter physics ; Ab initio ; Physics ; Optics ; Quantum mechanics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Yu Wang | Fei Wang | Ge Yan | Jun Wang | Guodong Niu | Jing Feng | Jian Mao | Yan Zhao
Source: Journal of Materials Informatics (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

MatSci-ML Studio: an interactive workflow toolkit for automated machine learning in materials science Machine learning (ML) has become a cornerstone of modern materials science, offering powerful tools for predicting material properties and accelerating experimental workflows. However, its widespread adoption is often hindered by the steep learning curve associated with programming languages such as Python, which presents a significant technical barrier for many domain experts. To address this challenge, we introduce MatSci-ML Studio: an interactive and user-friendly software toolkit designed to empower materials scientists with limited coding expertise. In contrast to traditional code-based frameworks, MatSci-ML Studio features an intuitive graphical user interface that encapsulates a comprehensive, end-to-end ML workflow. This integrated platform seamlessly guides users through data management, advanced preprocessing, multi-strategy feature selection, automated hyperparameter optimization, and model training, democratizing advanced computational analysis for the materials community. Notably, it incorporates advanced capabilities such as a SHapley Additive exPlanations-based interpretability analysis module for explaining model predictions and a multi-objective optimization engine for exploring complex design spaces. The practicality and effectiveness of MatSci-ML Studio are demonstrated through representative case studies, confirming its capacity to lower the technical barrier for ML applications, foster innovation, and significantly enhance the efficiency of data-driven materials science. Workflow ; Interpretability ; Programming by demonstration ; Domain (mathematical analysis) ; Application programming interface ; Learning curve ; Software ; Usability ; Feature (linguistics) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Explainable Artificial Intelligence (XAI)
Authors: Yufan Xia | Albert Thie | Joshua Soon | Giuseppe M. J. Barca
Source: ChemRxiv (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Machine-Learning Adaptivity, DFT-Level Accuracy, and Semi-Empirical Quantum-Chemistry Speed with Neural-Network Extended Tight-Binding Hamiltonian (control theory) ; Observable ; Quantum ; Artificial neural network ; Operator (biology) ; Set (abstract data type) Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Ioannis Papadimitriou | Ilias Gialampoukidis | Stefanos Vrochidis | Ioannis Kompatsiaris
Source:  (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

DiMAT Materials Modeler (DiMM): An Interactive Framework for Materials Property Prediction and Optimization Using a Hybrid Machine Learning - Genetic Algorithm Approach We present DiMM (DiMAT Materials Modeler), an interactive framework that advances materials informatics through an effective integration of machine learning (ML) and genetic algorithms (GA). The framework addresses two fundamental challenges in materials engineering: accurate property prediction and constrained inverse design for small and medium-sized enterprises (SMEs). Our approach uniquely combines dimensionality reduction strategies with an interactive optimization interface, enabling users to efficiently explore material compositions and process parameters while satisfying physical and manufacturing constraints. The framework integrates Large Language Model (LLM) assistants to provide context-aware guidance, making advanced materials informatics accessible to non-expert users. Validation across multiple material systems demonstrates significant improvements in both prediction accuracy and optimization efficiency (e.g., >99% confidence scores and Mean Absolute Error (MAE) ≤ 8.5% for solutions with ≤ 25% free parameters) compared to traditional approaches, while maintaining computation times under two minutes for typical use cases. Computer science ; Property (philosophy) ; Genetic algorithm ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Electron and X-Ray Spectroscopy Techniques
Authors: Mustafa Ayyıldız
Source: Multidiscipline Modeling in Materials and Structures (article, 2025-05-07)
Field: Materials Science — Materials Chemistry

Optimization and prediction of surface roughness in the milling process of aluminum alloy using Taguchi method and artificial neural network Purpose This paper explores the machining of aluminum alloy, focusing on optimizing and predicting surface roughness through advanced methods. The study investigates the optimization and prediction of surface roughness in milling aluminum alloy using cryogenically treated and non-cryogenically treated cutting tools. Design/methodology/approach A Taguchi L18 orthogonal array was implemented to determine the effects of cutting tool type, cutting speed, feed rate and depth of cut on surface roughness. Additionally, an artificial neural network model was developed to predict surface roughness. The back-propagation algorithm was used for training, and various architectures and learning algorithms, including Levenberg–Marquardt (LM), scaled conjugate gradient (SCG), quasi-Newton back propagation (BFGS), resilient back propagation (RP) and conjugate gradient back propagation (CGP), were evaluated. Findings Optimization results indicated that feed rate was the most significant factor affecting surface quality, contributing 36.41% according to analysis of variance. In the ANN the best predictive performance was achieved using the BFGS algorithm with a 4-13-1 network structure, yielding correlation coefficients (R2) values above 0.97 and low root mean square error (RMSE) and mean error percentage (MEP) values for both training and testing datasets. Originality/value The study concludes that the Taguchi method effectively optimized machining parameters, and the artificial neural network model demonstrated strong predictive accuracy, confirming its suitability for estimating surface roughness in milling processes. Taguchi methods ; Materials science ; Surface roughness ; Artificial neural network ; Aluminium ; Alloy ; Process (computing) ; Surface finish ; Composite material ; Metallurgy ; Mechanical engineering ; Artificial intelligence ; Engineering ; Computer science Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; Advanced Machining and Optimization Techniques
Authors: Silke Maes | Frederik De Ceuster | M. Van de Sande | L. Decin
Source: The Journal of Open Source Software (article, 2025-04-04)
Field: Chemistry — Spectroscopy

MACE: a Machine-learning Approach to Chemistry Emulation Astrochemistry is Emulation ; Mace ; Computer science ; Artificial intelligence ; Machine learning ; Psychology ; Medicine ; Internal medicine Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Various Chemistry Research Topics
Authors: Ao Wang | Wenkai Liu | Xiaohan Jin | Hecheng Wu | Dongfei Zhang | Xianglong Han | Yang Liu | Zhen Li | Mingming Ding | Jiehua Li | Hong Tan
Source: Nano Letters (article, 2025-04-28)
Field: Materials Science — Biomaterials

Dynamics and Machine Learning Reveal the Link between Tripeptide Sequences and Evaporation-Driven Material Properties Previous research showed that a peptide composed of three tyrosines (YYY) can turn into organic glass and cause strong adhesion between substrates via evaporation. However, the mechanisms of these processes remain unclear, and the exploration of applications of other peptide sequences is necessary. In this study, an optimized evaporation method was employed in molecular dynamics. It was found that YYY evaporation products possess abundant internal hydrogen bonds, which may facilitate the amorphous glass state formation. Moderate hydrophilicity of a peptide enhances molecular mobility and self-healing ability, while excessive hydrophilicity causes a water plasticizing effect. Stronger hydrophilicity also brings a larger curvature of evaporation products on polydimethylsiloxane (PDMS) substrate. A machine learning model was developed to predict the evaporation contact angle of peptide evaporation products and agrees well with the experiment. This research aims to improve understanding of peptide structure-function relationships and aid in designing custom organic optical devices based on peptide sequences. Link (geometry) ; Evaporation ; Tripeptide ; Dynamics (music) ; Nanotechnology ; Materials science ; Computer science ; Artificial intelligence ; Chemistry ; Biological system ; Chemical physics ; Physics ; Biology ; Thermodynamics ; Peptide ; Biochemistry Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Lipid Membrane Structure and Behavior
Authors: Atwakyire Moses | Qiao Huang | Ding Chen
Source: Next Materials (article, 2025-11-17)
Field: Materials Science — Biomaterials

Accelerating age hardening behavior of Mg alloy using a data-driven screening strategy guided by machine learning In this work, we introduce a self-guided design framework that combines target property and feature importance evaluation to optimize the aging behavior of Mg alloys. The design efficiency was built by a precise and reliable two-step feature engineering training strategy (2SFE) guided by machine learning models capable of assessing a range of Mg alloy properties across high-dimensional datasets. A multi-objective hybrid algorithm combines efficient global optimization (EGO) and a gradient-based feature reduction is established within this framework. EGO incorporates an innovative multi-objective gradient designed to speed up the search process along the Pareto front scheme, while 2SFE reduces features to improve the design space. A novel two-step feature engineering selection strategy (2SFE) was introduced, reducing computational input features to six (6) while enhancing prediction accuracy. Compared to the traditional approach, the scaled super learner model improved accuracy from 0.86 to 0.97. This methodology was experimentally validated, resulting in the creation of a novel Mg-6Gd-Zr-6Y-Nd, Mg-9Zn-5Gd-Zr-8Y-3Nd, Mg-8Zn-Zr-3Y-Nd, and Mg-9Zn-Zr-2Y (wt%) alloy, processed under defined specific conditions. The integrated approach was validated and assessed by (a) comparing predicted values with experimental results and (b) benchmarking the model’s outcomes with existing experimental and theoretical findings reported for Mg alloys. This integrated approach offers a cost-effective, efficient pathway for designing advanced Mg alloys while significantly reducing manual input in the alloy development process. Benchmarking ; Feature selection ; Feature (linguistics) ; Process (computing) ; Engineering design process ; Feature engineering ; Multi-objective optimization Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Hydrogen Storage and Materials
Authors: Wenlong Liu | Ying Gao | Qi Zhu | Yuelin You | Bocong Xia
Source: Energy (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

A hybrid machine learning approach for optimising hydrocarbon injection control in diesel oxidation catalyst for diesel particulate filter active regeneration Diesel particulate filter ; Diesel fuel ; Diesel engine ; Artificial neural network ; Approximation error ; Support vector machine ; Mean squared error ; Mean absolute percentage error ; Hydrocarbon Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Metaheuristic Optimization Algorithms Research
Authors: Jingyun Ye | Chathura B. Wijethunga | M. McEwen
Source: The Journal of Physical Chemistry C (article, 2025-07-31)
Field: Chemistry — Organic Chemistry

The First Frustrated Lewis Pairs Database: Machine Learning and Cheminformatics-Aided Prediction of Small Molecule Activation Frustrated Lewis Pairs (FLPs) are a burgeoning field in chemistry, with novel FLPs and surprising new reactions continually being discovered. However, experimentally examining each FLP’s activity toward all possible small molecules (SMs) is impractical due to time constraints and potential dangers from toxic, flammable, or explosive SMs. Here, we developed the first open-access Frustrated Lewis pairs Database (FLPDB), with DFT-optimized atomic structures and computed binding free energies and electronic properties data for each FLP toward SMs, including H2, CO2, H2O, H2CO, HCOOH, and CH3OH, via high-throughput computational screening. Machine learning was employed to predict the small molecule binding free energies and identify the most important feature governing the binding free energies. Further integrating molecular fingerprints, the H2 binding free energies can be predicted with a single feature─hydride affinity─for FLPs with molecular similarity scores ≥0.55. The FLPDB and the identified structure–activity relationships in this work will enable countless applications for novel FLP design, new reaction discovery, or toxic gas sensing and detection. Cheminformatics ; Frustrated Lewis pair ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Database ; Lewis acids and bases ; Computational chemistry ; Organic chemistry Organoboron and organosilicon chemistry ; Boron Compounds in Chemistry ; Chemical Synthesis and Analysis
Authors: Charlie Adams | Joshua T. Horton | Lily Wang | Simon Boothroyd | David L. Mobley | David Wright | Daniel J. Cole
Source: ChemRxiv (preprint, 2025-06-03)
Field: Chemistry — Electrochemistry

A graph neural network charge model targeting accurate electrostatic properties of organic molecules Common methods for assigning atom-centered partial charges in computational chemistry, such as RESP and AM1-BCC, rely on quantum mechanical or semi-empirical calculations of the molecule of interest, which are expensive to compute and dependent on the choice of input molecular conformer(s). Graph neural network (GNN) based continuous atom embeddings have been shown to be a fast and flexible solution for partial charge assignment, but those developed so far for condensed phase modeling have usually been trained to reproduce AM1-BCC charges, which themselves seek to reproduce the HF/6-31G(d) molecular electrostatic potential. Here, we investigate the suitability of various common charge assignment schemes, including ESP and atoms-in-molecule (AIM) based approaches, as training targets for new GNN based charge models. We show that the strengths of both approaches can be combined by co-training GNN models to AIM charges and molecular dipoles and electrostatic potentials. We collect a dataset of quantum mechanical AIM properties computed at a high level of theory (wB97X-D/def2-tzvpp), in both vacuum and implicit solvent, and train new GNN charge models to each. Charges can be scaled between the vacuum and solvated charge sets, and combined with Lennard-Jones parameters optimized using the Open Force Field infrastructure, to yield force fields that are suitably polarized for condensed phase modeling. We further demonstrate that the charge models may be applied to explore electrostatics-driven structure-activity relationships in medicinal chemistry. The charge models are freely available at: https://github.com/cole-group/nagl-mbis/. Artificial neural network ; Organic molecules ; Charge (physics) ; Graph ; Molecule ; Materials science ; Chemical physics ; Computer science ; Nanotechnology ; Artificial intelligence ; Chemistry ; Theoretical computer science ; Physics ; Organic chemistry ; Quantum mechanics Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Juan Cheng | Khidhair Jasim Mahommdi | Elimam Aliy | Riadh Murzuki
Source: Case Studies in Construction Materials (article, 2025-07-30)
Field: Materials Science — Polymers and Plastics

Carbon additives to improve polymer performance in energy applications using machine learning The development of polymer composites enhanced with carbon-based additives has been investigated to reinforce their applicability in energy-related systems. Conductive fillers, such as graphene, Carbon Nano Tubes (CNTs), and Short-cut Carbon Fibers (SCFs), were incorporated into a Polydimethylsiloxane (PDMS) matrix to enhance electrical conductivity and thermal performance. Experimental evaluations included four-probe electrical conductivity testing and self-heating measurements under varied input voltages (8–12 V). Results showed that composites containing 6 mm Carbon Fibers (CF), (Long-CNT) achieved the highest electrical conductivity of 1.8 S/m, significantly outperforming the CNT-only control (Base-CNT, 0.1 S/m). Correspondingly, the Long-CNT samples exhibited the fastest thermal response, with heating time constants (τg) as low as 70.01 s and peak surface temperatures exceeding 150 °C under 12 V input. To guide composite optimization, a hybrid Machine Learning (ML) framework combining Random Forest Regression (RFR) and Support Vector Regression (SVR) was developed. This stacked model was trained on 60 samples and achieved high predictive accuracy across all key outputs, including Coefficient of Determination (R²) = 0.985 for conductivity and R² > 0.95 for heating rate (Hr+c), τg, τd, and temperature. Feature importance analysis revealed that carbon fiber length and input voltage were the dominant factors influencing thermal-electrical performance. The model was also used to simulate untested CF–voltage configurations, identifying optimal engineering windows (e.g., 4.5 mm CF at 11 V) that balanced high heating efficiency with manageable power consumption. The integration of data-driven modeling with experimental validation enabled the accurate prediction and strategic tuning of composite properties. This work provides a scalable framework for designing high-performance, self-healing polymer nanocomposites for thermal management, sensing, and energy conversion applications. Carbon fibers ; Polymer ; Energy (signal processing) ; Materials science ; Process engineering ; Nanotechnology ; Computer science ; Composite material ; Engineering ; Physics ; Composite number Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication