jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alessandro Caruso | Jacopo Venturin | Lorenzo Giambagli | Edoardo Rolando | Zakariya El‐Machachi | Frank Noé | Cecilia Clementi
Source: Nature Communications (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Extending the range of graph neural networks with global encodings Graph Neural Networks (GNNs) are routinely used in molecular physics, social sciences, and economics to model many-body interactions in graph-like systems. However, GNNs are inherently local and can suffer from information flow bottlenecks. This is particularly problematic when modeling large molecular systems, where dispersion forces and local electric field variations drive collective structural changes. We introduce RANGE, a model-agnostic framework that employs an attention-based aggregation-broadcast mechanism that significantly reduces oversquashing effects, and achieves remarkable accuracy in capturing long-range interactions with linear scaling. Notably, RANGE integrates attention with positional encodings and regularization to dynamically expand virtual representations in virtual-node message-passing implementations. Across multiple state-of-the-art baselines, RANGE consistently restores long-range information, enabling the models to correctly predict electrostatic and dispersion-driven behavior even in out-of-distribution extrapolation tasks, where other unmodified baselines fail. Compared with other long-range paradigms, RANGE achieves the highest accuracy while requiring significantly less computational overhead, and it enables stable and scalable molecular dynamic simulations. RANGE offers accurate and efficient modeling of long-range interactions for simulating large molecular systems. Scalability ; Extrapolation ; Range (aeronautics) ; Regularization (linguistics) ; Graph ; Artificial neural network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Scientific Computing and Data Management
Authors: Haonan Peng | Ashish Rajyaguru | Enzo Curti | Daniel Grolimund | Sergey V. Churakov | Nikolaos I. Prasianakis
Source: Geochimica et Cosmochimica Acta (article, 2026-02-20)
Field: Materials Science — Biomaterials

Bridging in-situ chemical imaging and pore-scale reactive transport modelling: mechanistic insight into CaCO3 polymorph dynamics Understanding the growth and dissolution behavior of calcium carbonate (CaCO 3 ) polymorphs is fundamental for studying biomineralization, environmental geochemistry, and reactive transport processes in porous systems. Among numerous carbonate phases, amorphous calcium carbonate (ACC) often plays the role of a metastable precursor that dissolves, releasing ions needed to form more stable crystalline phases such as calcite and vaterite. A complex dissolution–precipitation process of CaCO 3 polymorphs was observed in dedicated counter-diffusion experiments through silica-gel. To describe the reactive transport phenomena observed in the experimental system, a pore-scale three-dimensional reactive transport model was developed. This approach couples the lattice Boltzmann method, which simulates solute diffusion, with the growth and dissolution kinetics of calcite, vaterite, and ACC. To facilitate high-resolution simulations, a machine learning technique was applied to accelerate geochemical speciation calculations. The reactive transport model provides spatially and temporally resolved saturation indices at the micrometer scale, from which local growth and dissolution rates of the CaCO 3 polymorphs are computed. All key spatial and temporal features observed by in-situ X-ray imaging within the targeted experimental window are well reproduced, providing mechanistic insights into the chemical evolution of the system. In particular, the model reveals how local undersaturation triggers ACC dissolution, releasing Ca 2+ and CO 3 2- ions that sustain and spatially promotes the further growth of the pre-existing calcite and vaterite crystals identified by imaging within the targeted time window. More generally, it shows the tight coupling between mineral stability, ion availability, and growth kinetics, enabling a mechanistic interpretation of the coupled ACC dissolution and calcite/vaterite growth under diffusion-limited, chemically heterogeneous conditions. This approach advances the understanding of CaCO 3 transformations by combining in-situ imaging with detailed mechanistic modelling, and it lays the groundwork for future integration into real-time, simulation-assisted experimental workflows. Dissolution ; Calcite ; Vaterite ; Carbonate ; Calcium carbonate ; Biomineralization ; Chemical imaging ; Amorphous calcium carbonate Calcium Carbonate Crystallization and Inhibition ; CO2 Sequestration and Geologic Interactions ; Microbial Applications in Construction Materials
Authors: Vivienne Pelletier | Vedant Bhat | Daniel J. Rivera | Steven A. Wilson | Christopher L. Muhich
Source: arXiv (Cornell University) (preprint, 2026-03-25)
Field: Materials Science — Materials Chemistry

Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning Machine-learned interatomic potentials (MLIPs), particularly graph neural network (GNN)-based models, offer a promising route to achieving near-density functional theory (DFT) accuracy at significantly reduced computational cost. However, their practical deployment is often limited by the large volumes of expensive quantum mechanical training data required. In this work, we introduce a transfer learning framework, Transfer-PaiNN (T-PaiNN), that substantially improves the data efficiency of GNN-MLIPs by leveraging inexpensive classical force field data. The approach consists of pretraining a PaiNN MLIP architecture on large-scale datasets generated from classical molecular simulations, followed by fine-tuning (dubbed autotuning) using a comparatively small DFT dataset. We demonstrate the effectiveness of autotuning T-PaiNN on both gas-phase molecular systems (QM9 dataset) and condensed-phase liquid water. Across all cases, T-PaiNN significantly outperforms models trained solely on DFT data, achieving order-of-magnitude reductions in mean absolute error while accelerating training convergence. For example, using the QM9 data set, error reductions of up to 25 times are observed in low-data regimes, while liquid water simulations show improved predictions of energies, forces, and experimentally relevant properties such as density and diffusion. These gains arise from the model's ability to learn general features of the potential energy surface from extensive classical sampling, which are subsequently refined to quantum accuracy. Overall, this work establishes transfer learning from classical force fields as a practical and computationally efficient strategy for developing high-accuracy, data-efficient GNN interatomic potentials, enabling broader application of MLIPs to complex chemical systems. Density functional theory ; Transfer of learning ; Quantum ; Quantum chemical ; Interatomic potential ; Development (topology) ; Artificial neural network ; Force field (fiction) ; Molecular dynamics ; Work (physics) Machine Learning in Materials Science ; Quantum many-body systems ; Quantum, superfluid, helium dynamics
Authors: Zun Wang | Wenhui Duan | Zuzhang Lin
Source: Open MIND (preprint, 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: Yu Zhang | Yong Zhang | Yi Yang | Zi-Meng Wang | Yu Jia
Source: International Journal of Fatigue (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

Machine-learning-guided design of gradient-microstructured nickel: synergistic enhancement of strength, ductility, and fatigue life Ductility (Earth science) ; Finite element method ; Stacking ; Microstructure ; Inverse ; Service life ; Key (lock) ; Surrogate model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; High Temperature Alloys and Creep
Authors: Vivienne Pelletier | Vedant Bhat | Daniel J. Rivera | Steven A. Wilson | Christopher L. Muhich
Source: ArXiv.org (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning Machine-learned interatomic potentials (MLIPs), particularly graph neural network (GNN)-based models, offer a promising route to achieving near-density functional theory (DFT) accuracy at significantly reduced computational cost. However, their practical deployment is often limited by the large volumes of expensive quantum mechanical training data required. In this work, we introduce a transfer learning framework, Transfer-PaiNN (T-PaiNN), that substantially improves the data efficiency of GNN-MLIPs by leveraging inexpensive classical force field data. The approach consists of pretraining a PaiNN MLIP architecture on large-scale datasets generated from classical molecular simulations, followed by fine-tuning (dubbed autotuning) using a comparatively small DFT dataset. We demonstrate the effectiveness of autotuning T-PaiNN on both gas-phase molecular systems (QM9 dataset) and condensed-phase liquid water. Across all cases, T-PaiNN significantly outperforms models trained solely on DFT data, achieving order-of-magnitude reductions in mean absolute error while accelerating training convergence. For example, using the QM9 data set, error reductions of up to 25 times are observed in low-data regimes, while liquid water simulations show improved predictions of energies, forces, and experimentally relevant properties such as density and diffusion. These gains arise from the model's ability to learn general features of the potential energy surface from extensive classical sampling, which are subsequently refined to quantum accuracy. Overall, this work establishes transfer learning from classical force fields as a practical and computationally efficient strategy for developing high-accuracy, data-efficient GNN interatomic potentials, enabling broader application of MLIPs to complex chemical systems. Density functional theory ; Transfer of learning ; Quantum ; Quantum chemical ; Interatomic potential ; Development (topology) ; Artificial neural network ; Force field (fiction) ; Molecular dynamics ; Work (physics) Machine Learning in Materials Science ; Quantum many-body systems ; Quantum, superfluid, helium dynamics
Authors: Lei Cui | Peiyuan Ni | Wei Lv | Ying Li
Source: Journal of Non-Crystalline Solids (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

Viscosity and composition prediction of B2O3-SiO2-based glass lubricants using machine learning Viscosity ; Mean squared error ; Depolymerization ; Viscosity index ; Viscometer ; Composition (language) ; Extrusion ; Relative viscosity Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; Injection Molding Process and Properties
Authors: Yaohui Guo | Ling Xu | Xianyu Chen | Zifeng Zhao
Source: Infrastructures (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

A Two-Stage Feature Reduction (FIRRE) Framework for Improving Artificial Neural Network Predictions in Civil Engineering Applications Artificial neural networks (ANNs) are widely used in engineering prediction, but excessive input dimensionality can reduce both accuracy and efficiency. This study proposes a two-stage feature-reduction framework, Feature Importance Ranking and Redundancy Elimination (FIRRE), to optimize ANN inputs by removing weakly informative and redundant variables. In Stage 1, four complementary ranking methods, namely Pearson correlation, recursive feature elimination, random forest importance, and F-test scoring, are combined into an ensemble importance score. In Stage 2, highly collinear features (ρ &gt; 0.95) are pruned while retaining the more informative variable in each pair. FIRRE is evaluated on 32 civil engineering datasets spanning materials, structural, and environmental applications, and benchmarked against Principal Component Analysis, variance-threshold filtering, random feature selection, and K-means clustering. Across the benchmark suite, FIRRE consistently achieves competitive or improved predictive performance while reducing input dimensionality by 40% on average and decreasing computation time by 10–60%. A dynamic modulus case study further demonstrates its practical value, improving R2 from 0.926 to 0.966 while reducing inputs from 25 to 7. Overall, FIRRE provides a practical, robust framework for simplifying ANN inputs and improving efficiency in civil engineering prediction tasks. Artificial neural network ; Dimensionality reduction ; Principal component analysis ; Redundancy (engineering) ; Curse of dimensionality ; Feature engineering ; Feature (linguistics) ; Ranking (information retrieval) ; Benchmark (surveying) Machine Learning in Materials Science ; Machine Learning and Data Classification ; Advanced Multi-Objective Optimization Algorithms
Authors: Shuo Liu | Yongsi Liu | Xinyun Zhou | Wanglong Wu | Bingkun Wang | Ruiying Ma | Le Yuan | Lingjie Zhao | Mianzeng Zhong
Source: The Journal of Physical Chemistry Letters (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Polarization-Programmable 2D Sb <sub>2</sub> S <sub>2</sub> O Photodetectors for High-Precision Object Identification Two-dimensional (2D) antimony-based materials have garnered significant interest due to their intrinsic structural anisotropy, tunable electronic band structures, and superior carrier transport properties, rendering them highly promising for next-generation microelectronic and optoelectronic applications. Nevertheless, research on ternary antimony-based compounds remains at an early stage, despite their compositional versatility and potential for synergistically modulating electronic and optical properties through element-specific engineering. Here, we report a novel 2D ternary antimony chalcogenide oxide, Sb<sub>2</sub>S<sub>2</sub>O, which features a low-symmetry layered structure and pronounced in-plane anisotropy. The layered Sb<sub>2</sub>S<sub>2</sub>O crystals exhibit excellent broadband photoresponse performance across the 254-940 nm range, achieving a remarkable responsivity of 11.3 A W<sup>-1</sup> and a specific detectivity of 6.5 × 10<sup>11</sup> Jones. Moreover, the photodetectors demonstrate polarization-angle-dependent sensitivity spanning 266-808 nm, with the structural anisotropy of Sb<sub>2</sub>S<sub>2</sub>O giving rise to a maximum dichroic ratio of approximately 1.48 at 633 nm. Notably, modulation of the polarization angle enables dynamic control over the spatial distribution of photoexcited carriers and the interfacial potential landscape, thereby allowing efficient tuning of the device's optical response. This polarization-dependent tunability further allows the photodetector to operate in a dual-mode configuration for intelligent imaging, simultaneously achieving high-sensitivity detection and programmable contrast enhancement. The integration of deep learning algorithms with the multifunctional optoelectronic characteristics of Sb<sub>2</sub>S<sub>2</sub>O positions this material as a promising candidate for next-generation intelligent photodetection and adaptive vision technologies. Photodetector ; Responsivity ; Photodetection ; Microelectronics ; Broadband ; Detector ; Ternary operation ; Polarization (electrochemistry) 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Chalcogenide Semiconductor Thin Films
Authors: Mohammed Azeez Khan | Aaron D'Souza | Vijay Choyal
Source: arXiv (Cornell University) (preprint, 2026-01-11)
Field: Materials Science — Materials Chemistry

Active Learning Strategies for Efficient Machine-Learned Interatomic Potentials Across Diverse Material Systems Efficient materials discovery requires reducing costly first-principles calculations for training machine-learned interatomic potentials (MLIPs). We develop an active learning (AL) framework that iteratively selects informative structures from the Materials Project and Open Quantum Materials Database (OQMD) using compositional and property-based descriptors with a neural network ensemble model. Query-by-Committee enables real-time uncertainty quantification. We compare four strategies: random sampling (baseline), uncertainty-based sampling, diversity-based sampling (k-means clustering with farthest-point refinement), and a hybrid approach. Experiments across four material systems (C, Si, Fe, and TiO2) with 5 random seeds demonstrate that diversity sampling achieves competitive or superior performance, with 10.9% improvement on TiO2. Our approach achieves equivalent accuracy with 5-13% fewer labeled samples than random baselines. The complete pipeline executes on Google Colab in under 4 hours per system using less than 8 GB RAM, democratizing MLIP development for resource-limited researchers. Open-source code and configurations are available on GitHub. This multi-system evaluation provides practical guidelines for data-efficient MLIP training and highlights integration with symmetry-aware architectures as a promising future direction. Sampling (signal processing) ; Pipeline (software) ; Cluster analysis ; Active learning (machine learning) ; Artificial neural network ; Ensemble learning ; Code (set theory) ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Inorganic Chemistry and Materials
Authors: Mohammed Azeez Khan | Aaron D'Souza | Vijay Choyal
Source: ArXiv.org (article, 2026-01-11)
Field: Materials Science — Materials Chemistry

Active Learning Strategies for Efficient Machine-Learned Interatomic Potentials Across Diverse Material Systems Efficient materials discovery requires reducing costly first-principles calculations for training machine-learned interatomic potentials (MLIPs). We develop an active learning (AL) framework that iteratively selects informative structures from the Materials Project and Open Quantum Materials Database (OQMD) using compositional and property-based descriptors with a neural network ensemble model. Query-by-Committee enables real-time uncertainty quantification. We compare four strategies: random sampling (baseline), uncertainty-based sampling, diversity-based sampling (k-means clustering with farthest-point refinement), and a hybrid approach. Experiments across four material systems (C, Si, Fe, and TiO2) with 5 random seeds demonstrate that diversity sampling achieves competitive or superior performance, with 10.9% improvement on TiO2. Our approach achieves equivalent accuracy with 5-13% fewer labeled samples than random baselines. The complete pipeline executes on Google Colab in under 4 hours per system using less than 8 GB RAM, democratizing MLIP development for resource-limited researchers. Open-source code and configurations are available on GitHub. This multi-system evaluation provides practical guidelines for data-efficient MLIP training and highlights integration with symmetry-aware architectures as a promising future direction. Sampling (signal processing) ; Pipeline (software) ; Cluster analysis ; Active learning (machine learning) ; Artificial neural network ; Ensemble learning ; Code (set theory) ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Inorganic Chemistry and Materials
Authors: Yixin Ma | Lei Yang | Y. Z. Zhang | Zhiwei Zhang | Haoliang Wang | Jingcai Cheng | Chao Yang
Source: AIChE Journal (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

Data‐driven measuring and hybrid modeling of racemic methionine crystallization thermodynamics in <scp> K <sub>2</sub> CO <sub>3</sub> </scp> aqueous systems Abstract Considering the intrinsic difficulty of accurately determining the thermodynamic data of complex impurity‐containing systems in industrial crystallization processes, this study creatively establishes a predictive correlation between infrared spectra, operating conditions, and the thermodynamics of the racemic methionine ( dl ‐methionine) crystallization system in K 2 CO 3 aqueous systems. Using principal component analysis, random forest, and Gaussian process regression architecture, a concentration prediction machine learning sub‐model was constructed, yielding a coefficient of determination of 0.988 in the test set. Subsequently, a first‐principle sub‐model combining equilibrium conditions with the electrolyte activity coefficient model was established by developing a parameter optimization program fitted experimental data to obtain a predicting error of 2.09%. The solubilization effect of carbonate salt on methionine was quantitatively characterized. The influence of temperature, pH, and carbonate component on the distribution and activity coefficients of dl ‐methionine species was thoroughly investigated. The hybrid model can better serve for the analysis of industrial dl ‐methionine crystallization processes. Crystallization ; Activity coefficient ; Aqueous solution ; Dimethyl carbonate ; Correlation coefficient ; Electrolyte ; Principal component analysis ; Component (thermodynamics) ; Salt (chemistry) Crystallization and Solubility Studies ; Chemical and Physical Properties in Aqueous Solutions ; Spectroscopy and Chemometric Analyses