jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiwen Xu | Guoqian Song | Xingwu Li | Yanju Wang | Aixue Sha | Yuanyuan Wei | Wenfeng Hao
Source: Materials (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

Low-Cycle Fatigue Life Prediction of Titanium-Based Intermetallic Alloys Using Machine Learning and Finite Element Analysis This study explores the low-cycle fatigue characteristics of three structural components fabricated from Ti<sub>2</sub>AlNb-based alloys utilizing Seeger's fatigue life theory and an improved Lemaitre damage evolution model. The validity and accuracy of the simulations based on these theoretical methods are verified by experimental fatigue life tests conducted at high temperatures. Additionally, the potential of employing long short-term memory (LSTM), extreme learning machine (ELM), and partial least squares (PLS) algorithms to predict the high-temperature, low-cycle fatigue life of Ti<sub>2</sub>AlNb alloy components is examined. Comparative analyses of the training effectiveness and practical applicability of these machine learning approaches are conducted, demonstrating that ELM exhibits superior predictive capability. This investigation thus provides a practical and efficient predictive methodology for assessing the low-cycle fatigue life of structural components composed of Ti<sub>2</sub>AlNb-based alloys. Intermetallic ; Finite element method ; Titanium alloy ; Low-cycle fatigue ; Materials science ; Titanium ; Metallurgy ; Fatigue testing ; Nickel titanium ; Structural engineering ; Shape-memory alloy ; Composite material ; Alloy ; Engineering Titanium Alloys Microstructure and Properties ; Aluminum Alloy Microstructure Properties ; High Temperature Alloys and Creep
Authors: Cheng Cai | Tao Wang
Source: Nature Communications (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Resolving chemical-motif similarity with enhanced atomic structure representations for accurately predicting descriptors at metallic interfaces Accurately predicting catalytic descriptors with machine learning (ML) methods is significant to achieving accelerated catalyst design, where a unique representation of the atomic structure of each system is the key to developing a universal, efficient, and accurate ML model that is capable of tackling diverse degrees of complexity in heterogeneous catalysis scenarios. Herein, we integrate equivariant message-passing-enhanced atomic structure representation to resolve chemical-motif similarity in highly complex catalytic systems. Our developed equivariant graph neural network (equivGNN) model achieves mean absolute errors <0.09 eV for different descriptors at metallic interfaces, including complex adsorbates with more diverse adsorption motifs on ordered catalyst surfaces, adsorption motifs on highly disordered surfaces of high-entropy alloys, and the complex structures of supported nanoparticles. The prediction accuracy and easy implementation attained by our model across various systems demonstrate its robustness and potentially broad applicability, laying a reasonable basis for achieving accelerated catalyst design. Robustness (evolution) ; Representation (politics) ; Similarity (geometry) ; Adsorption ; Artificial neural network ; Catalysis ; Complex system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Haolan Wang | Sikun Li | Junjie Zeng | Tao Zhang
Source: The Journal of Chemical Physics (article, 2025-03-10)
Field: Materials Science — Materials Chemistry

Accelerating polymer self-consistent field simulation and inverse DSA-lithography with deep neural networks Self-consistent field theory (SCFT) is a powerful polymer field-theoretic simulation tool that plays a crucial role in the study of block copolymer (BCP) self-assembly. However, the computational cost of implementing SCFT simulations is comparatively high, particularly in computationally demanding applications where repeated forward simulations are needed. Herein, we propose a deep learning-based method to accelerate the SCFT simulations. By directly mapping early SCFT results to equilibrium structures using a deep neural network (DNN), this method bypasses most of the time-consuming SCFT iterations, significantly reducing the simulation time. We first applied this method to two- and three-dimensional large-cell bulk system simulations. Both results demonstrate that a DNN can be trained to predict equilibrium states based on early iteration outputs accurately. The number of early SCFT iterations can be tailored to optimize the trade-off between computational speed and predictive accuracy. The effect of training set size on DNN performance was also examined, offering guidance on minimizing dataset generation costs. Furthermore, we applied this method to the more computationally demanding inverse directed self-assembly-lithography problem. A covariance matrix adaptation evolution strategy-based inverse design method was proposed. By replacing the forward simulation model in this method with a trained DNN, we were able to determine the guiding template shapes that direct the BCP to self-assemble into the target structure with certain constraints, eliminating the need for any SCFT simulations. This improved the inverse design efficiency by a factor of 100, and the computational cost for training the network can be easily averaged out over repeated tasks. Computer science ; Artificial neural network ; Inverse ; Field (mathematics) ; Block (permutation group theory) ; Algorithm ; Inverse problem ; Set (abstract data type) ; Mathematical optimization ; Artificial intelligence ; Mathematics Block Copolymer Self-Assembly ; Model Reduction and Neural Networks ; Advancements in Photolithography Techniques
Authors: Md. Kawsarul Islam Kabbo | Md. Habibur Rahman Sobuz | Mita Khatun | Mohamed Ghalla | Mohammed Jameel | Noor Md. Sadiqul Hasan | Sani Aliyu Abubakar
Source: Geoenvironmental Disasters (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Analyzing the influence of chemical components of incinerated bottom ash on compressive strength of magnesium phosphate cement using machine learning analysis Abstract Background The use of incinerated bottom ash (IBA) as a sustainable construction material offers potential environmental benefits but introduces complex interactions with cement chemistry. Magnesium phosphate cement (MPC), known for its rapid hardening and superior bonding, can be optimized through the controlled incorporation of IBA. However, limited studies have addressed how the chemical components of IBA affect the compressive strength of MPC, particularly using data-driven approaches. Methods A database of 396 experimental samples was compiled from previous studies considering mix proportions, oxide compositions, and curing conditions. Four ensemble machine learning algorithms—Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), Gradient Boosting Regressor (GBR), and Random Forest (RFR)—were employed to predict compressive strength. Model robustness was validated through 5-fold cross-validation. Feature interpretation was achieved using SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) to quantify individual and interactive effects of chemical and physical parameters. Results The XGB model achieved the highest predictive accuracy, with mean training and testing R2 values greater than 0.90 and 0.80, and the lowest mean absolute percentage error of 16.71%. SHAP analysis identified curing age as the most dominant factor, followed by FA/C, W/C, and MgO/PO4 ratios. IBA content and specific oxides such as Fe2O3 and Al2O3 contributed positively to strength within optimal ranges. PDP confirmed nonlinear dependencies, indicating a 26% reduction in strength as W/C increased from 0.1 to 0.6, while extended curing up to 28 days improved performance substantially. Conclusion The integration of SHAP and PDP provided a transparent interpretation of feature interactions in IBA-modified MPC. The developed XGB model demonstrated strong generalization and interpretability. The combined modeling approach offers a reliable predictive framework for optimizing IBA incorporation in sustainable binder systems and advancing eco-efficient material design. Compressive strength ; Gradient boosting ; Cement ; Incineration ; Boosting (machine learning) ; Hardening (computing) ; Residual ; Combustion Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research ; Magnesium Alloys: Properties and Applications
Authors: Shubha R. Kharel | Fanchen Meng | Xiaohui Qu | Matthew R. Carbone | Deyu Lu
Source: Physical Review Materials (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

OmniXAS: A universal deep-learning framework for materials x-ray absorption spectra X-ray absorption spectroscopy (XAS) is a powerful technique for probing local chemical environments. However, interpreting XAS spectra remains challenging, due to high computational costs and the need for domain expertise. To overcome these barriers, we introduce OmniXAS, a graph neural network framework that leverages transfer learning to directly predict XAS spectra from atomic structures. By capturing the shared spectral trends across the 3d transition metal family, OmniXAS learns a universal model that can be effectively fine-tuned on each specific element. OmniXAS achieves high predictive accuracy as demonstrated on the K-edge spectra of eight 3d transition metals (Ti--Cu), enabling real-time prediction of XAS spectra with minimal computational overhead. Materials science ; X-ray ; Absorption (acoustics) ; Spectral line ; Engineering physics ; Nanotechnology ; Composite material ; Optics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: Sebastien Röcken | Julija Zavadlav
Source: Journal of Chemical Information and Modeling (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements Machine learning potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availability of considerable data sets to ensure robust generalization across chemical space and thermodynamic conditions. The generation of such data sets can be labor-intensive, highlighting the need for innovative methods to train MLPs in data-scarce scenarios. Here, we introduce transfer learning of potential energy surfaces between chemically similar elements. Specifically, we leverage the trained MLP for silicon to initialize and expedite the training of an MLP for germanium. Utilizing classical force field and ab initio data sets, we demonstrate that transfer learning surpasses traditional training from scratch in force prediction, leading to more stable simulations and improved temperature transferability. These advantages become even more pronounced as the training data set size decreases. We also observe positive transfer learning effects for most out-of-target properties. Our findings demonstrate that transfer learning across chemical elements is a promising technique for developing accurate and numerically stable MLPs, particularly in a data-scarce regime. Transfer of learning ; Computer science ; Transfer (computing) ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Mahia V. Solout | Jahan B. Ghasemi
Source: Scientific Reports (article, 2025-05-31)
Field: Materials Science — Materials Chemistry

Predicting photodegradation rate constants of water pollutants on TiO2 using graph neural network and combined experimental-graph features Photocatalytic materials, which are known for their ability to harness solar energy to facilitate chemical reactions, are widely used in environmental remediation, including wastewater treatment. The efficiency of photocatalytic reactions in degrading pollutants is influenced by several factors, making parameter optimization time-consuming. In this context, machine learning techniques provide an appropriate solution for designing optimal photocatalysts. In addition to experimental variables such as solution pH and temperature, the molecular structure of the contaminant significantly affects the reaction efficiency. Molecular graphs are powerful tools for representing molecular structures for machine learning methods. This study integrates structural and five common experimental features in photocatalysis to develop graph neural networks (GNNs) for predicting the degradation rate constants of the organic water contaminants using TiO<sub>2</sub>. Three GNN models were developed: Graph Convolutional Network (GCN), Graph Attention Network (GAT), and a combined GAT-GCN model. Among these, the GAT performed better, achieving RMSE of 0.17, MAE of 0.13, and R<sup>2</sup> of 0.90. The results demonstrate the effectiveness of integrating structural and experimental features, which can be further applied to predict other properties of the materials. Photodegradation ; Graph ; Artificial neural network ; Reaction rate constant ; Computer science ; Pollutant ; Artificial intelligence ; Chemistry ; Photocatalysis ; Theoretical computer science ; Kinetics ; Organic chemistry ; Physics ; Catalysis Machine Learning in Materials Science ; Water Quality Monitoring and Analysis
Authors: Attila Cangi | Lenz Fiedler | Bartosz Brzoza | Karan Shah | Timothy J. Callow | Daniel Kotik | Steve Schmerler | Matthew C. Barry | James Goff | Andrew Rohskopf | Dayton J. Vogel | Normand A. Modine | Aidan P. Thompson | Sivasankaran Rajamanickam
Source: Computer Physics Communications (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations We present the Materials Learning Algorithms ( MALA ) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA 's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA 's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research. Computer science ; Scale (ratio) ; Scalability ; Computational science ; Electronic structure ; Algorithm ; Statistical physics ; Machine learning ; Artificial intelligence ; Physics ; Condensed matter physics ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Mikkel Ohm Sauer | Peder Lyngby | Kristian S. Thygesen
Source: Physical Review Materials (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

Dispersion-corrected machine learning potentials for 2D van der Waals materials Two-dimensional (2D) materials and their heterostructures continue to attract significant research interest owing to their unique physical properties and promising technological applications. The weak van der Waals interactions between individual layers give rise to atomically sharp interfaces and intricate moire patterns, often involving unit cells with thousands of atoms. These structural complexities present major challenges for $a\phantom{\rule{0}{0ex}}b$ $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o$ simulations. In this study, the authors systematically evaluate the performance of several universal machine learning interatomic potentials (MLIPs), employing a range of structural and electronic similarity metrics tailored to 2D van der Waals heterostructures. The results indicate that the most accurate MLIPs reach a level of precision comparable to the intrinsic uncertainty of density functional theory due to the choice of exchange correlation functional. Materials science ; van der Waals force ; Dispersion (optics) ; Van der Waals strain ; London dispersion force ; Chemical physics ; Van der Waals radius ; Condensed matter physics ; Nanotechnology ; Quantum mechanics ; Physics ; Molecule 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: John L. Weber | Rishabh D. Guha | Garvit Agarwal | Yuyang Wei | Aidan A. Fike | Xiaowei Xie | James Stevenson | Biswajit Santra | Richard A. Friesner | Karl Leswing | Mathew D. Halls | Robert Abel | Leif D. Jacobson
Source: ArXiv.org (preprint, 2025-05-09)
Field: Materials Science — Materials Chemistry

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties Machine learning force fields (MLFFs) have emerged as a sophisticated tool for cost-efficient atomistic simulations approaching DFT accuracy, with recent message passing MLFFs able to cover the entire periodic table. We present an invariant message passing MLFF architecture (MPNICE) which iteratively predicts atomic partial charges, including long-range interactions, enabling the prediction of charge-dependent properties while achieving 5-20x faster inference versus models with comparable accuracy. We train direct and delta-learned MPNICE models for organic systems, and benchmark against experimental properties of liquid and solid systems. We also benchmark the energetics of finite systems, contributing a new set of torsion scans with charged species and a new set of DLPNO-CCSD(T) references for the TorsionNet500 benchmark. We additionally train and benchmark MPNICE models for bulk inorganic crystals, focusing on structural ranking and mechanical properties. Finally, we explore multi-task models for both inorganic and organic systems, which exhibit slightly decreased performance on domain-specific tasks but surprising generalization, stably predicting the gas phase structure of $\simeq500$ Pt/Ir organometallic complexes despite never training to organometallic complexes of any kind. Benchmark (surveying) ; Inference ; Set (abstract data type) ; Invariant (physics) ; Ranking (information retrieval) ; Material properties ; Message passing Machine Learning in Materials Science ; Crystallography and molecular interactions ; Block Copolymer Self-Assembly
Authors: Joo Young Roh | Kang-Ting Huang | Anmol Choudhury | T. J. Quan | Chun‐Jen Huang | Priyank V. Kumar | Cyrille Boyer
Source: ACS Polymers Au (article, 2025-08-05)
Field: Chemistry — Organic Chemistry

Machine Learning-Guided Design of High-Performance Antifungal Polymers against <i>Candida albicans</i> This study leverages machine learning (ML) to accelerate the development of antifungal synthetic polymers. An in-house curated data set was used to train a Random Forest binary classification model, which identified five critical features for predicting antifungal activity against the fungal pathogen <i>Candida albicans</i>. This model shows that antifungal polymers should contain a hydrophilic composition of at least 30%, a calculated partition coefficient (cLogP) of 0 to +0.5, hydrophobic composition limited to 20%, degree of polymerization (DP) of 18 or less, and cationic composition limited to 50% for the polymer to have an MIC<sub>90</sub> of 64 μg/mL or less. Based on these insights, a library of polymers was synthesized via reversible addition-fragmentation chain transfer (RAFT) polymerization using 2-(butylthiocarbonothioylthio)-propanoic acid (BTPA) as the RAFT agent, exploring combinations of these features. A polymer exhibiting all five feature characteristics for an effective antifungal polymer presented an MIC<sub>90</sub> of 32 μg/mLlower than the 64 μg/mL Class 1 "good" polymer threshold. After optimizing the polymer for significant antifungal activity by fulfilling the five features of the antifungal impact, we improved its biocompatibility. We did this by replacing the BTPA RAFT agent with 2-dodecyl thiocarbonothioylthio-propanoic acid (DTPA), which has a longer, more hydrophobic end-group. This modification resulted in a polymer with an MIC90 of 16 μg/mL and a hemolysis concentration (HC50 exceeding 2000 μg/mL, yielding a selectivity index greater than 125aligning with the most competent polymer involved in the ML data set. This work validates the efficiency of ML-guided design for developing advanced antifungal polymers with enhanced potency and biocompatibility. Candida albicans ; Antifungal ; Microbiology ; Chemistry ; Materials science ; Biology Antimicrobial agents and applications ; Antimicrobial Peptides and Activities ; Bacterial biofilms and quorum sensing
Authors: Nikola Koutná | Shuyao Lin | Lars Hultman | Davide G. Sangiovanni | P.H. Mayrhofer
Source: Materials & Design (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Machine-learning potentials for structurally and chemically complex MAB phases: Strain hardening and ripplocation-mediated plasticity Though offering unprecedented pathways to molecular dynamics (MD) simulations of technologically-relevant materials and conditions, machine-learning interatomic potentials (MLIPs) are typically trained for “simple” materials and properties with minor size effects. Our study of MAB phases (MABs)—alternating transition metal boride (MB) and group A element layers—exemplifies that MLIPs for complex materials can be fitted and used in a high-throughput fashion: for predicting structural and mechanical properties across a large chemical/phase/temperature space. Considering group 4–6 transition metal based MABs, with A = Al and the 222, 212, and 314 type phases, three MLIPs are trained and tested, including lattice and elastic constants calculations at temperatures T ∈ { 0 , 300 , 1200 } K, extrapolation grade and energy (force, stress) error analysis for ≈ 3 ⋅ 10 6 ab initio MD snapshots. Subsequently, nanoscale tensile tests serve to quantify upper limits of strength and toughness attainable in single-crystal MABs at 300 K as well as their temperature evolution. In-plane tensile deformation is characterised by relatively high strength, {110}〈001〉 type slipping, and failure by shear banding. The response to [001] loading is softer, triggers work hardening, and failure by kinking and layer delamination. Furthermore, W 2 AlB 2 able to retard fracture via ripplocations and twinning from 300 up to 1200 K. • Semi-automatic dataset generation for the training of ML potentials by simulating series of atomic-scale mechanical tests triggering growth of realistic defective environments (e.g., stacking faults, voids, surfaces). • Identification of two general deformation mechanisms in MAB phases: (i) 110〈001〉type slipping and failure by shear banding subject to in-plane loading; (ii) layer buckling and failure by kinking and delamination subject to out-ot-plane loading. • Design suggestions: W2AlB2 offers outstanding toughness via strain-activated nucleation of ripplocations and mechanical twins, Ta2AlB2 and W3AlB4 promote stacking fault formation, and Ti3AlB4 provides the highest theoretical tensile strength. Materials science ; Plasticity ; Strain hardening exponent ; Strain (injury) ; Hardening (computing) ; Composite material ; Crystallography ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Titanium Alloys Microstructure and Properties