jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: L. B. Fu | Longfei Lv | Fan Zhang | Si Zhu Zhou | W. Gao | Jijun Zhao
Source: ArXiv.org (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks Singlet fission (SF) provides a promising strategy for surpassing the Shockley-Queisser limit in photovoltaics. However, the identification of efficient SF materials is hindered by the limited availability of suitable molecular candidates and the high computational costs associated with conventional quantum-chemical methods for excited states. In this study, we introduce a high-throughput screening framework that integrates a graph neural network (GNN) with multi-level validation to accelerate the discovery of SF-active molecules. Trained on a previously reported FORMED database, the GNN achieves state-of-the-art accuracy in predicting SF-relevant excited-state properties, demonstrating a mean absolute error of about 0.1 eV for S1, T1, and T2 excitation energies. This capability facilitates the efficient screening of over 20 million molecular structures from both OE62 and QO2Mol databases. Our framework significantly reduces the computational demand associated with Time-Dependent Density Functional Theory validation by four orders of magnitude and identifies 180 potential SF molecules along with more than 1000 conformers. Subsequent assessments regarding synthetic accessibility, GW approximation and Bethe-Salpeter equation calculations further highlight a subset of experimentally feasible candidates among these SF candidates. The approach presented herein exemplifies an effective strategy for accelerating the discovery of functional molecules with optoelectronic applications. Limit (mathematics) ; Artificial neural network ; Density functional theory ; Identification (biology) ; Fission ; Excited state ; Graph theory Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Magnetism in coordination complexes
Authors: Chong Guo | Kailing Fang | Haojie Yue | Kun Guo | Ziliang Deng | Zhichao Gong | Kang Li | Huacheng Zhang | Zhiyong Liu | Bing Xie | Pu MAO | Cheng Tan | Kui Yao | Francis E. H. Tay
Source: Journal of Applied Physics (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Machine learning and first-principles guided design of BaTiO3-based materials for capacitor applications Addressing the requirements of advanced capacitor applications for high dielectric permittivity, low loss, and strong frequency stability necessitates the accelerated development of materials exhibiting weakly coupled relaxor ferroelectric characteristics and broad temperature stability. BaTiO3 systems have attracted considerable interest owing to their high dielectric constant and tunable properties. However, conventional trial-and-error approaches and complex doping strategies hinder rapid progress. In this study, a data-driven approach combining first-principles calculations with machine learning was employed to predict the variation trends in formation energies for 30 301 Sr, La co-doped BaTiO3 compositions. The ferroelectric properties, crystal structures, elastic anisotropy, and thermal properties were systematically investigated at four representative doping levels (0, 0.125, 0.25, and 0.375) to elucidate the microscopic mechanism responsible for the emergence of weakly coupled relaxor ferroelectric behavior and to enable rapid identification of doping ranges that yield both relaxor ferroelectric characteristics and broad temperature stability. The results show that Sr, La co-doping in the range of 0.125–0.25 introduces compositional and displacement disorder that progressively suppresses long-range ferroelectric order, promotes the formation of polar nanoregions, and drives the system toward a weakly coupled, relaxor-like dielectric response with reduced hysteretic loss, with the composition at 0.25 exhibiting enhanced mechanical and thermodynamic performance. These findings provide guidance for BaTiO3-based materials for high-frequency capacitor applications and offer a transferable framework for accelerating the study of doped material properties. Ferroelectricity ; Dielectric ; Doping ; Capacitor ; Thermal stability ; Material properties ; Displacement (psychology) ; Ferroelectric capacitor Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Ferroelectric and Negative Capacitance Devices
Authors: Chaorui Duan | Dazhong Wu
Source: arXiv (Cornell University) (preprint, 2026-03-15)
Field: Materials Science — Materials Chemistry

Predicting Stress-strain Behaviors of Additively Manufactured Materials via Loss-based and Activation-based Physics-informed Machine Learning Predicting the stress-strain behaviors of additively manufactured materials is crucial for part qualification in additive manufacturing (AM). Conventional physics-based constitutive models often oversimplify material properties, while data-driven machine learning (ML) models often lack physical consistency and interpretability. To address these issues, we propose a physics-informed machine learning (PIML) framework to improve the predictive performance and physical consistency for predicting the stress-strain curves of additively manufactured polymers and metals. A polynomial regression model is used to predict the yield point from AM process parameters, then stress-strain curves are segmented into elastic and plastic regions. Two long short-term memory (LSTM) models are trained to predict two regions separately. For the elastic region, Hooke's law is embedded into the LSTM model for both polymer and metal. For the plastic region, Voce hardening law and Hollomon's law are embedded into the LSTM model for polymer and metal, respectively. The loss-based and activation-based PIML architectures are developed by embedding the physical laws into the loss and activation functions, respectively. The performance of the two PIML architectures are compared with two LSTM-based ML models, three additional ML models, and a physics-based constitutive model. These models are built on experimental data collected from two additively manufactured polymers (i.e., Nylon and carbon fiber-acrylonitrile butadiene styrene) and two additively manufactured metals (i.e., AlSi10Mg and Ti6Al4V). Experimental results demonstrate that two PIML architectures consistently outperform the other models. The segmental predictive model with activation-based PIML architecture achieves the lowest MAPE of 10.46+/-0.81% and the highest R^2 of 0.82+/-0.05 arocss four datasets. Consistency (knowledge bases) ; Hardening (computing) ; Universal testing machine ; Strain hardening exponent ; Polymer ; Embedding ; Constitutive equation ; Polynomial ; Point (geometry) Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Additive Manufacturing and 3D Printing Technologies
Authors: Rong Zhang | Haiyan Cui | Yongfan Li | Yongjian Wang | Junwei Pei | Chen Zhang | Liping Zhong
Source: Journal of Materials Engineering and Performance (article, 2026-01-21)
Field: Materials Science — Biomaterials

MOPSO-Based Data Augmentation and Hardness-Assisted Interpretable Machine Learning for Accurate Prediction of Mechanical Properties of Mg Alloys Particle swarm optimization ; Toughness ; Experimental data ; Ultimate tensile strength ; Generalization ; Set (abstract data type) ; Core (optical fiber) ; Alloy Magnesium Alloys: Properties and Applications ; Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties
Authors: Qiuyi Fu | Hao Yuan | H. Wang | Wenbin Liu | Guobing Zhou | Zhen Yang
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning molecular dynamics simulations of coordination and diffusion behaviors in lithiated gallium electrodes Liquid gallium (Ga) has emerged as a promising anode material for flexible lithium-ion batteries owing to its exceptional fluidity, intrinsic self-healing capability, and high theoretical capacity. However, the understanding of structure and transport properties across diverse Li-Ga alloy (LGA) phases formed during lithiation remains limited. Here, we develop machine learning force fields (MLFFs) for four experimentally identified LGAs (Li<sub>3</sub>Ga<sub>14</sub>, Li<sub>2</sub>Ga<sub>7</sub>, LiGa, and Li<sub>2</sub>Ga) and perform large-scale molecular dynamics simulations to investigate local coordination and diffusion behaviors. Our simulations reveal a lithiation-induced evolution of Li local environments from Ga-dominated coordination shells in Li<sub>3</sub>Ga<sub>14</sub> and Li<sub>2</sub>Ga<sub>7</sub> to Li-rich networks in LiGa and Li<sub>2</sub>Ga. Polyhedral template matching further indicates that all four LGA phases remain predominantly disordered, while the fraction of short-range ordered motifs increases upon lithiation. Consistently, Li exhibits liquid-like mobility in Li<sub>3</sub>Ga<sub>14</sub> and Li<sub>2</sub>Ga<sub>7</sub>, but strongly localized, solid-like dynamics in LiGa and Li<sub>2</sub>Ga. The Li diffusion coefficient in Li<sub>3</sub>Ga<sub>14</sub> (4.46 × 10<sup>-11</sup> m<sup>2</sup> s<sup>-1</sup>) is nearly an order of magnitude higher than that in Li<sub>2</sub>Ga<sub>7</sub> (3.14 × 10<sup>-12</sup> m<sup>2</sup> s<sup>-1</sup>), primarily due to the weaker interactions between Li and the surrounding Li/Ga in the former system. Finally, van Hove analysis and trajectory visualizations uncover intermittent residence-jump (hopping-like) dynamics in Li<sub>2</sub>Ga<sub>7</sub> and Li<sub>2</sub>Ga. Overall, our findings clarify the structure-diffusion relationship across different LGAs and offer important theoretical insights into the structural evolution of Ga-based anodes during the lithiation process. Molecular dynamics ; Gallium ; Diffusion ; Coordination number ; Anode ; Electrode Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Yu Bao | Keke Song | Jiahui Liu | Yanzhou Wang | Yifei Ning | Penghua Ying | Ping Qian
Source: npj Computational Materials (preprint, 2026-02-05)
Field: Materials Science — Metals and Alloys

Atomistic understanding of hydrogen bubble-induced embrittlement in tungsten enabled by machine learning molecular dynamics Hydrogen bubble formation within nanoscale voids is a critical mechanism underlying the embrittlement of metallic materials, yet its atomistic origins remains elusive. Here, we present an accurate and transferable machine-learned potential (MLP) for the tungsten-hydrogen binary system within the neuroevolution potential (NEP) framework, trained through active learning on extensive density functional theory data. The developed NEP-WH model reproduces a wide range of lattice and defect properties in tungsten systems, as well as hydrogen solubility, with near first-principles accuracy, while retaining the efficiency of empirical potentials. Crucially, it is the first MLP capable of capturing hydrogen trapping and H2 formation in nanovoids, with quantitative fidelity. Large-scale machine-learning molecular dynamics simulations reveal a distinct aggregation pathway where planar hydrogen clusters nucleate and grow along {100} planes near voids, with hexagonal close-packed structures emerging at their intersections. Under uniaxial tension, these aggregates promote bubble fracture and the development of regular {100} cracks, suppressing dislocation activity and resulting in brittle fracture behavior. This work provides detailed atomistic insights into hydrogen bubble evolution and fracture in nanovoids, enables predictive modeling of structural degradation in extreme environments, and advances fundamental understanding of hydrogen-induced damage in structural metals. Hydrogen embrittlement ; Molecular dynamics ; Nucleation ; Tungsten ; Dislocation ; Brittleness ; Hydrogen ; Fracture (geology) Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Advanced Materials Characterization Techniques
Authors: Yu Bao | Keke Song | Jiahui Liu | Y. Wang | Yifei Ning | Penghua Ying | Qian Pu
Source: npj Computational Materials (article, 2026-02-05)
Field: Materials Science — Metals and Alloys

Atomistic understanding of hydrogen bubble-induced embrittlement in tungsten enabled by machine learning molecular dynamics Hydrogen bubble formation within nanoscale voids is a critical mechanism underlying the embrittlement of metallic materials, yet its atomistic origins remains elusive. Here, we present an accurate and transferable machine-learned potential (MLP) for the tungsten-hydrogen binary system within the neuroevolution potential (NEP) framework, trained through active learning on extensive density functional theory data. The developed NEP-WH model reproduces a wide range of lattice and defect properties in tungsten systems, as well as hydrogen solubility, with near first-principles accuracy, while retaining the efficiency of empirical potentials. Crucially, it is the first MLP capable of capturing hydrogen trapping and H2 formation in nanovoids, with quantitative fidelity. Large-scale machine-learning molecular dynamics simulations reveal a distinct aggregation pathway where planar hydrogen clusters nucleate and grow along {100} planes near voids, with hexagonal close-packed structures emerging at their intersections. Under uniaxial tension, these aggregates promote bubble fracture and the development of regular {100} cracks, suppressing dislocation activity and resulting in brittle fracture behavior. This work provides detailed atomistic insights into hydrogen bubble evolution and fracture in nanovoids, enables predictive modeling of structural degradation in extreme environments, and advances fundamental understanding of hydrogen-induced damage in structural metals. Hydrogen embrittlement ; Molecular dynamics ; Nucleation ; Tungsten ; Dislocation ; Hydrogen ; Brittleness ; Fracture (geology) Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Yong Hu | Shuyan Liu | Shangyuan Jiang | Qingyang Duan | Shaohua Wang | Zhijie Yan | Xin Song | Xin Liu
Source: Journal of Materials Engineering and Performance (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Optimization of Hot Rolling Process and Conductivity Prediction for Cu-Fe-P Alloy Based on GA-BP and PSO-BP Neural Networks Artificial neural network ; Particle swarm optimization ; Microstructure ; Alloy ; Process (computing) ; Backpropagation ; Process optimization ; Genetic algorithm Microstructure and mechanical properties ; Metallurgy and Material Forming ; Metallurgy and Material Science
Authors: M. Lebeda | Jan Drahokoupil | Petr Veřtát | Petr Vlčák
Source: ArXiv.org (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Rust-accelerated powder X-ray diffraction simulation for high-throughput and machine-learning-driven materials science High-throughput powder X-ray diffraction (XRD) simulations are a key prerequisite for generating large datasets used in the development of machine-learning models for XRD-based materials analysis. However, the widely used pymatgen powder XRD calculator, implemented entirely in Python, can be computationally inefficient for large-scale workloads, limiting throughput. We present XRD-Rust, a Rust-accelerated implementation of the pymatgen powder XRD calculator that maintains full compatibility with existing Python-based workflows. The method retains pymatgen for crystal structure handling and symmetry analysis while reimplementing the computationally intensive parts of the XRD calculation in Rust. Performance benchmarks were carried out on large crystallographic datasets from the Materials Cloud Three-Dimensional Structure Database (MC3D, 33 142 structures) and the Crystallography Open Database (COD 515 181). For the MC3D dataset, XRD-Rust achieves an average speedup of 4.7 +- 1.6 and a maximum speedup of 25, reducing computation from 34.9 s to 1.4 s. For the COD dataset, the average speedup is 6.1 +- 4.6 with a maximum speedup of 719 (1437 min to 2 min). These benchmarks demonstrate that XRD-Rust significantly accelerates powder XRD simulations, enabling efficient high-throughput dataset generation and improved performance in interactive diffraction analysis applications. Speedup ; Computation ; Diffraction ; Powder diffraction ; Limiting ; X-ray crystallography X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Microstructure and mechanical properties
Authors: Martin Callsen | Tai-Ting Lee | Mei-Yin Chou
Source: Open MIND (preprint, 2026-01-29)
Field: Materials Science — Materials Chemistry

Dynamically training machine-learning-based force fields for strongly anharmonic materials Machine learning (ML) force fields have emerged as a powerful tool for computing materials properties at finite temperatures, particularly in regimes where traditional phonon-based perturbation theories fail or cannot be extended beyond the harmonic approximation. These approaches offer accuracy comparable to ab initio molecular dynamics (MD), but at a fraction of the computational cost. However, their reliability critically depends on the quality and representativeness of the training data. In particular, static training datasets often lead to failure when the force field encounters previously unseen atomic configurations during MD simulations. In this work, we present a framework for dynamically training ML force fields and demonstrate its effectiveness across materials with varying degrees of anharmonicity, including cubic boron arsenide (c-BAs), silicon (Si), and tin selenide (SnSe). Our method builds on the conventional lattice dynamics expansion of total energy and incorporates Bayesian error estimation to guide adaptive data acquisition during simulation. Specifically, we show that trajectory-averaged Bayesian errors enable efficient and targeted exploration of the configuration space, significantly enhancing the robustness and transferability of the resulting force fields. We further demonstrate how Bayesian error estimation can be applied to determine the convergence of the dynamic training without requiring additional ab initio data. This proposed framework offers a practical and easily implementable scheme to improve the training process, which is the most critical step in developing reliable ML force fields. Force field (fiction) ; Ab initio ; Bayesian probability ; Robustness (evolution) ; Anharmonicity ; Bayesian inference ; Convergence (economics) Machine Learning in Materials Science ; Thermal properties of materials ; Inorganic Chemistry and Materials
Authors: Wuyang Lin | Devis Di Tommaso
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Machine Learning-Accelerated Discovery of Curvature-Induced Binding Mode Switching in Dual-Atom Catalysts Promoting C–C Coupling Supporting structures, data, scripts and forcefields files for " Machine Learning-Accelerated Discovery of Curvature-Induced Binding Mode Switching in Dual-Atom Catalysts Promoting C–C Coupling" Catalysis ; Coupling (piping) ; Mode (computer interface) ; Scripting language Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Porfirio López-Domínguez | ANTONIO FLORES-TLACUAHUAC | María Esther Trevino | Iván Zapata-González | Enrique Saldívar-Guerra
Source: Chemical Engineering Science (article, 2026-03-17)
Field: Chemistry — Organic Chemistry

Modeling ab initio emulsion polymerization using thermodynamic and artificial neural network approaches for the vinyl monomer partition Monomer ; Emulsion polymerization ; Polymerization ; Ab initio ; Styrene ; Partition (number theory) ; Sodium persulfate ; Chain transfer Advanced Polymer Synthesis and Characterization ; Polymer Science and PVC ; Hydrogels: synthesis, properties, applications
Authors: Peter Eastman | Evan Pretti | Thomas E. Markland
Source: ArXiv.org (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations The rapid development of pretrained Machine Learning Interatomic Potentials (MLIPs) that cover a wide range of molecular species has made it challenging to select the best model for a given application. We benchmark 15 pretrained MLIPs, evaluating each one on accuracy, speed, memory use, and ability to produce stable simulations. This provides an objective basis for practitioners to select the most appropriate MLIP for their own simulations, and offers insight into which factors most strongly influence model accuracy. We find that the number of model parameters and the size of the training set are both strongly correlated with accuracy, while training on charged molecules and including explicit Coulomb energy terms are less essential than one might expect. Speed and memory use are determined as much by the model architecture as by the size of the model. Benchmark (surveying) ; Set (abstract data type) ; Range (aeronautics) ; Training set ; Energy (signal processing) ; Basis (linear algebra) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Physical and Chemical Molecular Interactions
Authors: Peter Eastman | Evan Pretti | Thomas E. Markland
Source: arXiv (Cornell University) (preprint, 2026-01-22)
Field: Materials Science — Materials Chemistry

Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations The rapid development of pretrained Machine Learning Interatomic Potentials (MLIPs) that cover a wide range of molecular species has made it challenging to select the best model for a given application. We benchmark 15 pretrained MLIPs, evaluating each one on accuracy, speed, memory use, and ability to produce stable simulations. This provides an objective basis for practitioners to select the most appropriate MLIP for their own simulations, and offers insight into which factors most strongly influence model accuracy. We find that the number of model parameters and the size of the training set are both strongly correlated with accuracy, while training on charged molecules and including explicit Coulomb energy terms are less essential than one might expect. Speed and memory use are determined as much by the model architecture as by the size of the model. Benchmark (surveying) ; Set (abstract data type) ; Range (aeronautics) ; Training set ; Energy (signal processing) ; Basis (linear algebra) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Physical and Chemical Molecular Interactions
Authors: Shirin Sabokdast | Pedram Mirchi | Ali Rajabpour
Source: International Journal of Heat and Mass Transfer (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Deep learning potential for predicting interfacial thermal conductance in ZnO/GaN heterostructures: A non-equilibrium molecular dynamics study Molecular dynamics ; Thermal conductivity ; Thermal ; Deep learning ; Conductance ; Dynamics (music) Machine Learning in Materials Science ; Thermal properties of materials ; GaN-based semiconductor devices and materials
Authors: Jingsong Zhang | Chuanguo Zhang | Guo Chen | Xianlong Wang
Source: Computational Materials Science (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

The effect of vacancy defects on the stability of atomic metallic hydrogen: A deep neural network molecular dynamics study Vacancy defect ; Molecular dynamics ; Nucleation ; Diffusion ; Phase (matter) ; Hydrogen ; Thermal diffusivity ; Metal ; Atomic diffusion Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals