jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Henry Tischler | Wenting Li | Qi Tang | Danny Perez | Thomas Vogel
Source: ArXiv.org (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Predicting Atomistic Transitions with Transformers Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model. Computational model ; Transformer ; Domain (mathematical analysis) ; Molecular dynamics ; Physical system ; Complex system ; Experimental data Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Nanopore and Nanochannel Transport Studies
Authors: Yue Cheng | Chuanhui Huang | Zeyu Zhang
Source: Frontiers in Chemistry (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Editorial: Design and bioapplication of nanozymes Nanozymes, nanomaterials with intrinsic enzyme-like catalytic activities, have rapidly evolved into a versatile class of functional materials with broad biomedical relevance (Zhang et al., 2024;Zhang et al., 2025). Advances in materials chemistry, nanotechnology, and biomedicine have enabled increasingly sophisticated nanozyme designs with enhanced catalytic performance and expanded application scopes (Tian et al., 2025). Rational nanozyme engineering and its translation into antibacterial therapy, biosensing, and disease diagnostics have attracted growing interest (Jiang et al., 2019;Chen et al., 2023). Accordingly, this Research Topic "Design and Bioapplication of Nanozymes" was established to highlight recent progress at the interface of nanozyme design and biological applications, with an emphasis on precision, intelligence, and clinical relevance.The contributions collected in this Research Topic collectively reflect the current landscape of nanozyme research, emphasizing rational design, mechanistic understanding, and application-oriented innovation. Together, they illustrate the evolution of nanozymes from simple enzyme mimics into versatile biofunctional platforms.A key theme highlighted in this Topic is the use of nanozymes as alternatives to conventional antibiotics to address the growing challenge of antimicrobial resistance (AMR). Wang et al. Taken together, the contributions in this Research Topic highlight several unifying themes: rational nanozyme design remains central to achieving high catalytic efficiency and functional specificity; nanozyme bioapplications are increasingly advancing from proof-of-concept studies toward disease-relevant and clinically meaningful scenarios; and interdisciplinary integration, particularly with DNA nanotechnology and machine learning, is reshaping nanozyme discovery and optimization. Despite these advances, key challenges remain, including improving substrate specificity, ensuring long-term biosafety, elucidating in vivo catalytic behavior, and establishing standardized evaluation protocols. Addressing these issues will require continued interdisciplinary collaboration.In conclusion, Design and Bioapplication of Nanozymes offers a timely overview of a rapidly evolving field. We hope this Research Topic informs readers of recent progress and inspires further innovation toward intelligent, precise, and translational nanozyme technologies. We thank all contributing authors and reviewers for their valuable efforts and anticipate that nanozymes will play an increasingly important role in future bioanalytical and biomedical applications. Rational design ; Biomedicine ; Design elements and principles ; Relevance (law) ; Scope (computer science) Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Mehrab Hasan | Iman Ghamarian | Yingtao Liu
Source: Macromolecular Rapid Communications (article, 2026-01-20)
Field: Materials Science — Polymers and Plastics

Machine Learning Assisted Prediction of Recovered Angle of Carbon Fiber Reinforced Shape Memory Epoxy Carbon Composites Shape memory polymers and their composites are promising smart materials for a wide range of applications associated with smart material-based actuators. Carbon fiber-reinforced shape memory epoxy composites can enable electroactive shape recovery via Joule heating, offering precise, remote-controlled actuation. However, predicting time-dependent recovery behavior across varying current levels remains challenging due to limited experimental data. This study presents an overfitting-aware machine learning framework for predicting shape recovery angles of the shape memory epoxy composites activated by Joule heating as a function of time and applied current. Four regression algorithms: Random Forest (RF), Support Vector Regression (SVR), Regularized Polynomial, and Gaussian Process Regression (GPR) are considered for constructing the shape memory recovery angle prediction algorithm. In the models, overfitting constraints are integrated into Bayesian hyperparameter optimization using a hybrid objective function that penalizes excessive generalization gaps. Stratified fivefold cross-validation ensures balanced current distribution across folds. Results demonstrate that kernel-based methods outperform tree ensembles: SVR achieves the highest accuracy with controlled generalization (R<sup>2</sup> = 0.944) while RF exhibits limited effectiveness (R<sup>2</sup> = 0.842), confirming tree-based methods are less suitable for small, smooth datasets. SVR provides optimal accuracy-generalization balance for reliable engineering predictions. Hyperparameter ; Overfitting ; Shape-memory alloy ; Support vector machine ; Random forest ; Shape-memory polymer ; Bayesian optimization ; Epoxy ; Gaussian process ; Kriging Polymer composites and self-healing ; Shape Memory Alloy Transformations ; Aeroelasticity and Vibration Control
Authors: Qinyi Zhang | Duanyu Feng | R.J. Han | Yangshuai Wang | Hao Henry Wang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction Simulating microstructure evolution (MicroEvo) is vital for materials design but demands high numerical accuracy, efficiency, and physical fidelity. Although recent studies on deep learning (DL) offers a promising alternative to traditional solvers, the field lacks standardized benchmarks. Existing studies are flawed due to a lack of comparing specialized MicroEvo DL models with state-of-the-art spatio-temporal architectures, an overemphasis on numerical accuracy over physical fidelity, and a failure to analyze error propagation over time. To address these gaps, we introduce MicroEvoEval, the first comprehensive benchmark for image-based microstructure evolution prediction. We evaluate 14 models, encompassing both domain-specific and general-purpose architectures, across four representative MicroEvo tasks with datasets specifically structured for both short- and long-term assessment. Our multi-faceted evaluation framework goes beyond numerical accuracy and computational cost, incorporating a curated set of structure-preserving metrics to assess physical fidelity. Our extensive evaluations yield several key insights. Notably, we find that modern architectures (e.g., VMamba), not only achieve superior long-term stability and physical fidelity but also operate with an order-of-magnitude greater computational efficiency. The results highlight the necessity of holistic evaluation and identify these modern architectures as a highly promising direction for developing efficient and reliable surrogate models in data-driven materials science. Benchmark (surveying) ; Key (lock) ; Field (mathematics) ; Stability (learning theory) ; Fidelity ; Set (abstract data type) ; Computational model Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Model Reduction and Neural Networks
Authors: Henry Tischler | Wenting Li | Qi Tang | Danny Perez | Thomas Vogel
Source: Open MIND (preprint, 2026-03-05)
Field: Materials Science — Materials Chemistry

Predicting Atomistic Transitions with Transformers Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model. Computational model ; Transformer ; Domain (mathematical analysis) ; Molecular dynamics ; Physical system ; Complex system ; Experimental data Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Nanopore and Nanochannel Transport Studies
Authors: Tomomi Shimazaki | MASANORI TACHIKAWA
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Theoretical study on the analyzability of modified convex regression for radical reaction Analyzing data and extracting meaningful insights is essential across various research fields. To address acrylate and methacrylate radical reaction data, we propose a modified convex clustering (regression) method, in which representative points are directly selected from the training data to describe the dataset. Although machine learning (ML) models are often regarded as black boxes, making their predictions difficult to interpret, the (modified) convex clustering approach allows for straightforward analysis of model behavior. This study emphasizes the importance of selecting representative points to enhance the interpretability and transparency of ML models. We demonstrate that radical reaction energy barriers can be effectively described and predicted based on the contributions of similar reactions. The simplicity and transparency of the modified convex clustering (regression) method enable in-depth analysis of physicochemical data. Interpretability ; Cluster analysis ; Regular polygon ; Transparency (behavior) ; Convex optimization ; Data point ; Regression ; Simplicity Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Radical Photochemical Reactions
Authors: Zun Wang | Wenhui Duan | Zuzhang Lin
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Machine Learning for Electron-phonon Interactions From Finite Difference First-principles investigations of electron-phonon interactions (EPIs) play a crucial role in understanding a wide range of phenomena in physics and materials science. Among various approaches, the finite difference method offers a direct route to capture higher-order EPIs and is compatible with diverse electronic structure solvers. However, its considerable computational cost limits its broader application. To overcome this bottleneck, we present a machine learning electron-phonon interaction (MLEPI) pipeline that predicts force constants and electronic Hamiltonians for modeling EPIs from finite difference calculations, improving efficiency by orders of magnitude without compromising accuracy. The performance of MLEPI is validated by studying the temperature dependence of the electronic band properties in bilayer graphene, where both first- and second order EPIs are treated on an equal footing. Using a heterogeneous edge network, the pipeline integrates both interlayer and intralayer interactions, making it particularly suitable for studying multilayer materials. With its inherent adaptability and ease of transfer to other applications, our methodology provides a robust tool with a very favorable accuracy/efficiency balance for investigating EPIs in large-scale material systems. Pipeline (software) ; Adaptability ; Finite difference ; Range (aeronautics) ; Flexibility (engineering) ; Enhanced Data Rates for GSM Evolution Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Xiangyu Ma | Peng Lin | Shuyong Jiang | Xiao Jing Feng | Jingru Chen | Xinxin Bai | Pengle Kong
Source: Journal of Alloys and Compounds (article, 2026-01-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Investigation on magnetic properties of Sm2Co17-type permanent magnets based on machine learning Gradient boosting ; Magnet ; Boosting (machine learning) ; Random forest ; Decision tree ; Magnetic separation ; Regression Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Jing Yang | Bingxin Li | Samuel Henrique Mattoso | Ahmed Abdelkawy | Mira Todorova | Jörg Neugebauer
Source: Physical Review Letters (article, 2026-02-17)
Field: Chemistry — Electrochemistry

Macro-Dipole-Constrained Learning of Atomic Charges for Accurate Electrostatic Potentials at Electrochemical Interfaces Large thermal fluctuations of the liquid phase obscure the weak macroscopic electric field that drives electrochemical reactions, rendering the extraction of reliable interfacial charge distributions from ab initio molecular dynamics extremely challenging. We introduce SMILE (Scalar Macro-dipole Integrated LEarning) charge partition, a macro-dipole-constrained scheme that infers atomic charges using only the instantaneous atomic coordinates and the total dipole moment of the simulation cell-quantities routinely available from standard density functional theory calculations. SMILE preserves both the global electrostatic field and the local potential without invoking any explicit charge-partitioning scheme. Benchmarks on three representative electrochemical interfaces-nanoconfined water, Mg^{2+} dissolution in water, and a kinked Mg vicinal surface under anodic bias-show that SMILE eliminates the qualitative errors observed for unconstrained charge decompositions. The method is computationally inexpensive and data-efficient, opening the door to charge-aware machine-learning potentials capable of bias-controlled, nanosecond-scale simulations of realistic electrochemical systems. Electrochemistry ; Charge (physics) ; Electrostatics ; Electric potential ; Electrostatic induction ; Electrode Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Electrostatics and Colloid Interactions
Authors: Liang Chengtian
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Artificial Intelligence-Driven Discovery of Magnetic Higher-Order Topological Corner States: A Review from Theoretical Framework to Large-Scale Screening This review explores the integration of artificial intelligence (AI) with condensed matter physics, specifically focusing on the prediction of magnetic higher-order topological corner states. We examine the theoretical foundations of higher-order topological insulators (HOTIs), the unique challenges posed by magnetic systems, and the application of equivariant graph neural networks in high-throughput screening. The article discusses active learning strategies for navigating vast chemical configuration spaces and addresses current limitations regarding data scarcity and strong electronic correlations. Topology (electrical circuits) ; Topological insulator ; Artificial neural network ; Topological data analysis ; Graph theory ; Graph Machine Learning in Materials Science ; Topological Materials and Phenomena ; Quantum many-body systems
Authors: Nico Unglert | Michael Ketter | G. K. H. Madsen
Source: npj Computational Materials (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling Abstract Accurate prediction of materials phase diagrams from first principles remains a central challenge in computational materials science. Machine-learning interatomic potentials can provide near-DFT accuracy at a fraction of the cost, but their reliability crucially depends on the availability of representative training data that span all relevant regions of the potential-energy surface. Here, we present a fully automated active-learning (AL) strategy based on replica-exchange nested sampling (RENS) for the generation of training data and the computation of complete pressure-temperature phase diagrams. In our framework, RENS acts as both the exploration engine and the acquisition mechanism: its intrinsic diversity and likelihood-constrained sampling ensure that the configurations selected for DFT labeling are both informative and thermodynamically representative. We apply the approach to silicon, germanium, and titanium using potentials trained at the r2SCAN level of theory. For all systems, the AL process converges within ~ 10–15 iterations, yielding transferable potentials that reproduce known phase transitions and thermodynamic trends. These results demonstrate that RENS-based AL provides a general and autonomous route to constructing machine-learning interatomic potentials and predicting first-principles phase diagrams across broad thermodynamic conditions. Sampling (signal processing) ; Computation ; Process (computing) ; Phase (matter) ; Phase diagram ; Fraction (chemistry) ; Reliability (semiconductor) ; Training set Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Ji Huang | Jiali Chen
Source: ChemRxiv (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

A minimal machine-learning correction of DFTB Mulliken charges towards DFT-level MBIS populations Density-functional tight binding (DFTB) enables efficient quantum-based simulations for large molecular systems, but atomic charges are typically reported via Mulliken population analysis and often deviate from density-based charge definitions at the density functional theory (DFT) level. Minimal basis iterative stockholder (MBIS) charges are attractive due to numerical stability and reduced basis-set sensitivity, yet MBIS is not available in standard DFTB workflows due to the lack of an explicit real-space electron density. Here we develop a minimal, physics-informed machine-learning (ML) correction that maps DFTB Mulliken charges to DFT reference MBIS charges at the atom-wise level. The model uses only atomic identity, DFTB Mulliken charge, and inexpensive local-geometry descriptors derived from the molecular structure, while enforcing exact molecular charge conservation at inference by a per-molecule shift. Using 5000 molecules from the QM9 database split into 4000/500/500 for training, validation, and testing, we show that nonlinearity alone provides limited improvement over an element-wise linear baseline, whereas incorporating local geometric descriptors yields a substantial accuracy gain, particularly for carbon and nitrogen. The proposed correction provides DFT-level charge quality at a computational cost comparable to standard DFTB calculations and can be readily integrated into existing DFTB-based workflows. Mulliken population analysis ; Charge (physics) ; Population ; Inference ; Nonlinear system ; Stability (learning theory) ; Density functional theory ; Atomic charge Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Liang Chengtian
Source: Open MIND (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Artificial Intelligence-Driven Discovery of Magnetic Higher-Order Topological Corner States: A Review from Theoretical Framework to Large-Scale Screening This review explores the integration of artificial intelligence (AI) with condensed matter physics, specifically focusing on the prediction of magnetic higher-order topological corner states. We examine the theoretical foundations of higher-order topological insulators (HOTIs), the unique challenges posed by magnetic systems, and the application of equivariant graph neural networks in high-throughput screening. The article discusses active learning strategies for navigating vast chemical configuration spaces and addresses current limitations regarding data scarcity and strong electronic correlations. Topology (electrical circuits) ; Topological insulator ; Artificial neural network ; Topological data analysis ; Graph theory ; Graph Machine Learning in Materials Science ; Topological Materials and Phenomena ; Quantum many-body systems
Authors: Caili Tang | Xiangxuan Chao | Wei Feng | Xinyue Dai | Yu Chen | Shengjian Zhang
Source: Journal of the American Chemical Society (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Screening of High-Entropy Sub-1 nm Nanowires for Ultrasound-Augmented Pancatalytic Tumor Therapy The rational design of catalytic nanomaterials, particularly high-entropy alloy (HEA) nanomaterials with their unique structures, is crucial for advancing pancatalytic therapy and holds promise for catalytic biomedical applications. Nevertheless, conventional trial-and-error approaches to HEA development persist in being inefficient and resource-demanding, underscoring the critical need for data-driven strategies to accelerate the design of next-generation catalytic platforms. Herein, we report a machine learning (ML)-assisted framework for the rational design of subnanometer HEAs with enhanced multicatalytic activities for the treatment of triple-negative breast cancer. Using an extensively curated dataset of nanozyme compositions and activities, ML algorithms identified key metal elements with the highest contribution to catalytic performance. Guided by these insights, a distinct PtFeMoCoNiRu HEA subnanowire (HESNW) was synthesized, in which the subnanostructure confers maximized active-site exposure and superior atomic utilization, enabling exceptional reactive oxygen species (ROS) generation. Ultrasound (US), characterized by its noninvasive and deep penetration, is employed to activate catalytic reactions. Excessive ROS production with HESNW under US irradiation induces extensive DNA damage, activating the cGAS-STING signaling pathway and leading to PANoptosis. This ML-guided design strategy enables the precise tailoring of multicatalytic HEA nanomaterials and provides a generalizable blueprint for accelerating the discovery of multifunctional nanocatalysts through data-driven methodologies. Rational design ; Nanomaterials ; Nanomaterial-based catalyst ; Catalysis ; Nanowire ; Reactive oxygen species Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; High Entropy Alloys Studies