jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: E. Elaine Ferguson | Felipe Oggioni | Daniel Peppe | Sandra P. Whiteside | Henry Fieldsend | Burcu Tasdemir
Source: Materials Today Communications (article, 2025-11-20)
Field: Materials Science — Polymers and Plastics

The influence of volume fraction on the compressive behaviour of a polymeric syntactic foam: From manufacturing to machine learning-based modelling This study presents a comprehensive investigation of an epoxy-based syntactic foam with microsphere volume fractions (VFs) ranging from 0 % to 60 %, encompassing manufacturing, experimental testing, and both data-driven and numerical modelling. The manufacturing approach was designed to improve microsphere dispersion, material homogeneity, and process reproducibility. By integrating degassing and mould rotation during curing, the approach significantly reduced void content and improved uniformity compared to conventional mix-and-cast techniques. Compression experiments were conducted across all VFs following the manufacturing process, revealing a significant influence of microsphere content on the compressive strength,which decreased from 102 MPa at 0 % VF to 27 MPa at 60 % VF. To model the mechanical behaviour of the manufactured foams, two complementary strategies were developed and evaluated: (1) machine learning (ML) models trained on experimental stress-strain data, and (2) a finite element (FE) model using a representative volume element (RVE) in Abaqus CAE. The best-case machine learning model achieved an overall mean stress prediction error of 5 MPa (4.16 %), outperforming the RVE model (6.88 MPa) and providing reliable predictions even in the post-densification regime. The ML models achieved highly accurate predictions of the full stress-strain response and demonstrated strong potential as efficient and reliable tools for reproducing the mechanical behaviour of syntactic foams. • Enhanced microsphere dispersion achi-eved through degassing and mould rotation. • Microsphere volume fraction significantly influences compressive strength. • Data-driven model accurately predicts post-yield behaviour and energy absorption. • Machine learning model outperforms finite element model in accuracy and efficiency. Syntactic foam ; Volume fraction ; Volume (thermodynamics) ; Compression (physics) ; Finite element method ; Compressive strength ; Rotation (mathematics) ; Material properties ; Process (computing) Polymer Foaming and Composites ; Epoxy Resin Curing Processes ; Polymer composites and self-healing
Authors: Guo-Yong Shi | Zhicheng Zhong | Yuehua Su | Ri He | Chao Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Unveiling the Atomic-Level Mechanisms of Negative Thermal Expansion in Zn(Cn)₂ Using Machine Learning-Enabled Molecular Dynamics Molecular dynamics ; Negative thermal expansion ; Thermal expansion ; Chemical physics ; Dynamics (music) ; Chemistry ; Materials science ; Nanotechnology ; Computer science ; Computational chemistry ; Physics ; Composite material Thermal Expansion and Ionic Conductivity ; Thermal and Kinetic Analysis ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Tristan Maxson (18009352) | Ademola Soyemi (12246034) | Xinglong Zhang (2378422) | Benjamin W. J. Chen (14077266) | Tibor Szilvási (1355940)
Source: Figshare (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

MS25: MaterialsScience-Focused Benchmark Data Setfor Machine Learning Interatomic Potentials We present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5–2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr–O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr–O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research. Benchmarking ; Benchmark (surveying) ; Observable ; Set (abstract data type) ; Energy (signal processing) ; Data set ; Experimental data ; Simple (philosophy) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Xuxin Kang (20476806) | Wenjing Zhou (181057) | Ziyuan Li (1603000) | Zhaoqin Chu (2121340) | Hanqing Yin (1640440) | Shan Gao (46743) | Aijun Du (1276998) | Xiangmei Duan (1938826)
Source: Figshare (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Prediction ofCO<sub>2</sub> Reduction Reaction Intermediatesand Products on Transition Metal-Doped γ‑GeSe Monolayers:A Combined DFT and Machine Learning Approach Accurate prediction of free energy changes (Δ<i>G</i>) for the vast network of reaction intermediates in the electrocatalytic CO<sub>2</sub> reduction reaction (CO<sub>2</sub>RR) is essential for evaluating catalytic performance. We combined density functional theory (DFT) and machine learning (ML) to screen 25 single-atom catalysts (SACs) on defective γ-GeSe monolayers for CO<sub>2</sub> reduction to methanol, methane, and formic acid. Among nine ML models evaluated with 14 features, the XGBoost performed best (R<sup>2</sup> = 0.92 and MAE = 0.24 eV), identifying Ni, Ru, and Rh@GeSe as prospective catalysts. Feature importance analysis highlighted CO<sub>2</sub> activation with ∠O–C–O and IP<sup>C–O1</sup> as the key attributes. The trained ML model’s Δ<i>G</i> predictions closely match DFT-calculated values for the reported Ti@N<sub>4</sub>–C, Fe@g-C<sub>6</sub>N<sub>6</sub>, and Co@g-C<sub>6</sub>N<sub>6</sub>. Incorporating non-DFT-based features enabled rapid predictions while retaining model performance. This work identifies effective SACs for CO<sub>2</sub>RR and offers insights for efficient catalyst design. Catalysis ; Reduction (mathematics) ; Density functional theory ; Formic acid ; Feature (linguistics) ; Work (physics) ; Reaction intermediate ; Reaction mechanism Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Ammonia Synthesis and Nitrogen Reduction
Authors: Yongxiang Zhang | Yueying Liang | Zimu Wang | Liang Yu | Xingcai Lü
Source: Combustion and Flame (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Machine learning-driven prediction of NH2-n-alkane reactions for constructing high-precision ammonia-macromolecule hydrocarbon combustion models The co-combustion of ammonia and large hydrocarbon fuels has been proven to be an effective strategy for reducing carbon emissions. However, the insufficient understanding of C-N cross-reactions poses significant challenges to combustion modeling. Due to the lack of data on the reaction NH 2 + macromolecular n-alkane, this reaction relys heavily on estimation derived from small alkanes. Neural networks (NN) provide viable alternative method for predicting rate constants for macromolecules. To establish a dataset for the NN model, the rate constants of NH 2 + C n H 2n+2 (n= 1-4, 6, 8, 10, 12) were theoretically calculated at the DLPNO-CCSD(T)/CBS(T-Q)//M06-2X/def2-TZVP level. A variety of descriptors (including RDKIT descriptors and quantum chemistry descriptors) were comprehensively considered, and redundant descriptors were eliminated based on the stepwise regression algorithm and Pearson correlation coefficient. The optimal hyperparameter was obtained via OPTUNA code [ 1 ], and the repetitive training of different random seeds further proved the reliability and efficiency of the hyperparameters. Based on the partial reaction rate database (NH 2 /C 1 -C 10 ), a DNN model was trained and its ability to extrapolate to predict the reaction rates of larger molecule (NH 2 /C 12 ) was validated. Training results show that the NN model exhibit good performance with high determination coefficient R 2 and small RMSE, but deviations are still observed in the thermodynamic limit (near 300 K or 2000 K) and the medium-high temperature regions. The predicted rate constants of NH 2 + C 16 H 34 by NN model differ from RMG-py and rate rules, but overall they are distributed within the range of uncertainty. Kinetic modelling shows that when using the rate constants of NN model, the mechanism can simulate the ignition delay of the NH 3 /C 16 H 34 mixture. In summary, the proposed method is supposed to provide accurate and affordable rate constants prediction of macromolecular reaction systems, accelerating the development process of macromolecular mechanisms in real transportation fuel. Hydrocarbon ; Alkane ; Macromolecule ; Combustion ; Ammonia ; Chemistry ; Biological system ; Organic chemistry ; Biology ; Biochemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Combustion Engine Technologies
Authors: Yuanyuan Cui | Chengyu Zhang | Ling Niu | Zheng Jiao | Xin Liu | Sihan Yang | Yanfeng Gao
Source: Advanced Physics Research (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Integrating First Principles Calculations and Machine Learning to Study the <i>Re</i>NiO<sub>2</sub>/Ti<sub>3</sub>C<sub>2</sub> Heterojunctions for Sodium Ion Batteries Abstract Due to the large size of sodium ions and their slow redox kinetics in electrochemical processes, the sodium ion batteries currently are still far from satisfactory. This study investigates the electrical transport properties of Re NiO 2 / Ti 3 C 2 heterojunctions in sodium ion batteries through a combination of first principles calculations and machine learning analysis. The Re NiO 2 /Ti 3 C 2 heterojunctions exhibit metallic characteristics and enhanced electronic conductivity due to the hybridization of p‐d orbitals and the strengthening of Ni─Ti metal bonds. The sodium ion migration energy barrier decreases with increasing rare earth atomic number, facilitating ion transport. Machine learning analysis identifies key factors influencing ion and electron transport rates, including strain, lattice constants, and doping concentration. These findings provide theoretical guidance for designing more efficient negative electrodes for sodium ion batteries. Non-blocking I/O ; Heterojunction ; Ion ; Sodium ; Materials science ; Chemistry ; Optoelectronics ; Metallurgy MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices ; 2D Materials and Applications
Authors: Xujian Wang (19731251) | Xiongwu Wu (818771) | Bernard R. Brooks (603888) | Junmei Wang (141197)
Source: Figshare (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Accurate Free Energy Calculation via Multiscale SimulationsDriven by Hybrid Machine Learning and Molecular Mechanics Potentials This work develops a hybrid machine learning/molecular mechanics (ML/MM) interface integrated into the AMBER molecular simulation package. The resulting platform is highly versatile, accommodating several advanced machine learning interatomic potential (MLIP) models while providing stable simulation capabilities and supporting high-performance computations. Building upon this robust foundation, we developed new computational protocols to enable pathway-based and end point-based free energy calculation methods utilizing ML/MM hybrid potential. In particular, we proposed an ML/MM-compatible thermodynamic integration (TI) framework that adequately addressed the challenge of applying MLIPs in TI calculations due to its indivisible nature of energy and force. Our results demonstrated that the hydration free energies calculated using this framework achieved an accuracy of 1.0 kcal/mol, outperforming the traditional approaches. Moreover, ML/MM enables more precise sampling of conformational ensembles for improved end point-based free energy calculations. Overall, our efficient, stable, and highly compatible interface not only broadens the application of MLIPs in multiscale simulations but also enhances the accuracy of free energy calculations from multiple aspects. By introducing a novel ML/MM-compatible thermodynamic integration framework, we offered a novel foundation for combining advanced multiscale simulation methodologies with highly accurate free energy calculation techniques, thereby opening new avenues and providing a robust theoretical framework for future developments in this field. Thermodynamic integration ; Energy (signal processing) ; Interface (matter) ; Work (physics) ; Interatomic potential ; Molecular dynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Simone Vari | Carlo de Falco | Carlo Cavallotti
Source: Journal of Chemical Theory and Computation (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

NN-VRCTST: Neural Network Potentials Meet Variable Reaction Coordinate Transition State Theory for the Rate Constant Determination of Barrierless Reactions The determination of rate constants for barrierless reactions poses severe problems from a theoretical perspective. The main challenges concern the proper description of the electronic structure of the reacting system, which may have multireference character, the anharmonicity of the relative motions of the fragments, and the proper definition of the reaction coordinate. The literature state of the art in the context of transition state theory is its variable reaction coordinate implementation (VRC-TST), which overcomes these difficulties in determining the number of transition state ro-vibrational states through a Monte Carlo sampling of the potential energy surface (PES) defined by the relative orientation of the two fragments. Although approaching the accuracy of experiments, VRC-TST requires tens of thousands of single-point energy (SPE) evaluations, thus being computationally demanding. The approach developed in this work, named NN-VRCTST, aims at fitting the PES with physics-inspired artificial neural network (ANN) models to be used as surrogate potentials in VRC-TST simulations. The ANN efficacy is evaluated in the computation of high-pressure limit rate constants for gas-phase barrierless reactions and validated over state-of-the-art VRC-TST simulations. It is shown that the NN-VRCTST tool reaches an accuracy within 20% with respect to VRC-TST simulations performed by using traditional approaches. While lowering the number of SPE needed by at least a factor of 4, the computational framework devised here allows one to decouple ANN training and VRC-TST calculations, enabling the optimization of the SPE evaluations as well as the quality inspection of the employed data points. We believe that the NN-VRCTST approach has the potential to evolve into a robust and computationally efficient framework for performing VRC-TST calculations for barrierless reactions. Reaction coordinate ; Anharmonicity ; Potential energy surface ; Context (archaeology) ; Artificial neural network ; Computation ; Variable (mathematics) ; Transition state theory ; Monte Carlo method ; Reaction rate constant Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Min Cheng (86295) | Minggao Feng (18438955) | Li Zhou (54356) | Shihui Wang (653643) | Zhiyuan Zhang (93194) | Chong Liu (450204) | Xu Ji (343848)
Source: Figshare (article, 2025-08-20)
Field: Chemistry — Inorganic Chemistry

A Machine Learning-BoostedHigh-Throughput Screeningof Metal–Organic Frameworks for Ethane/Ethylene Separation:From Molecular Simulation to Process Modeling Ethane/ethylene (C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub>) separation is crucial in the petrochemical industry. This study proposes a multiscale framework combining machine learning and molecular modeling to identify optimal C<sub>2</sub>H<sub>6</sub>-selective metal–organic frameworks (MOFs) for this separation. A database of 505,973 experimental/hypothetical MOFs underwent geometric/chemical prescreening (254,500 retained). Grand canonical Monte Carlo (GCMC) simulations on sampled MOFs trained a Random Forest model to predict adsorption behaviors, enabling rapid screening of the remaining candidates. This yielded 14,927 MOFs verified using GCMC, with 10 top performers selected. Process-level evaluation via vacuum pressure swing adsorption modeling confirmed their robustness under industrial conditions (varying temperatures, pressures, and gas compositions). Comparative analysis demonstrated superior performance over literature-reported MOFs, particularly under realistic operational parameters. The integrated approach establishes an efficient pathway for MOF discovery, bridging molecular properties with industrial process requirements while reducing reliance on energy-intensive distillation. Robustness (evolution) ; Bridging (networking) ; Process (computing) ; Monte Carlo method ; Process simulation ; Process modeling ; Petrochemical ; Work in process Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Process Optimization and Integration
Authors: Federico Atzori (22074379) | Federico Zedda (22074382) | Giacomo Cao (3899707) | Alessandro Concas (13028775)
Source: Figshare (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

A Multilinear RegressionProcedure for Solving theInverse Problem with Physics-Informed Neural Networks: Applicationto Three Case Studies in Chemical Engineering Physics-Informed Neural Networks (PINNs) are gaining increasing interest in the field of modeling and simulation as they enhance the extrapolation ability of standard Neural Networks (NNs), which typically fail to predict system behavior beyond the experimental range. Integrating governing equations into the training algorithm leads to physics-driven networks capable of extrapolating system trends from limited training datasets. In this context, this work proposes an innovative approach that employs PINNs to solve inverse problems that arise when the parameters of the governing equations are unknown. Leveraging a multilinear regression procedure, the proposed method analytically computes model parameters during training without burdening the network’s parameter optimization and reducing the risk of getting stuck in local minima. Three case studies are considered for validation: (i) a continuous stirred tank reactor with proportional control, (ii) an autocatalytic process described with a Lotka–Volterra model, and (iii) a plug flow reactor with multiple reactions, which demonstrate the method’s effectiveness in predicting the system behavior and simultaneously identifying model parameters. Extrapolation ; Multilinear map ; Artificial neural network ; Inverse problem ; Regression ; Field (mathematics) ; Process (computing) ; Inverse Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Process Optimization and Integration
Authors: Ke Shu | Dong‐Yun Gui | Wei-Xin Yan | Chun‐Hai Wang
Source: Journal of Chemical Information and Modeling (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Machine Learning Tackles the Challenge of Powder X-ray Diffraction Indexing for All Crystal Systems The indexing of powder X-ray diffraction (PXRD) in <i>ab initio</i> unknown structure determinations is a critical yet challenging step in crystallography, particularly for low-symmetry systems (e.g., monoclinic, triclinic) and/or large unit cell systems (<i>V</i> > 1000 Å<sup>3</sup>). In this work, a machine learning-based indexing method is presented, which achieves high-precision, end-to-end prediction of crystal symmetry and unit cell parameters from powder diffraction peaks for all crystal systems. The trained models (denoted as AIdex) achieve a top-5 accuracy of ∼97% in extinction group (symmetry class) identification, and a mean absolute percentage error (MAPE) <5% for indexing, demonstrating significant improvements in both accuracy and time consumed compared to traditional algorithms (TREOR/ITO/DICVOL). AIdex also shows high capacity for experimental applications, maintaining a success rate of ∼90% even under extreme conditions involving zero-shift error (±0.6°) and uncertainty noise (±0.15°). Applied to practical PXRD data, AIdex gives predicted unit cell parameters close to the experimentally refined ones (MAPE < 5%), serving as ideal initial inputs for further Pawley refinements. This leads to a new paradigm for rapid indexing in <i>ab initio</i> unknown structure determination, facilitating the advancement of crystallographic analysis toward automation and intelligent methodologies. Diffraction ; Search engine indexing ; Powder diffraction ; Automation ; Extreme learning machine ; Ideal (ethics) ; Crystal (programming language) ; Noise (video) X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions