jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yingxu Wang
Source: OSF Preprints (OSF Preprints) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Emergent Knowledge-Driven Computational Atom Reasoning for Molecular Property Prediction We present a novel gradient-free computational architecture where autonomous units interact through Compare-Adjust-Record (CAR) cycles to predict molecular properties from geometric features alone. Unlike deep learning approaches, our system requires no loss functions, backpropagation, or global optimization. Each unit maintains independent state representations [activation weight, validation score, feature vector] and communicates via tanh-bounded signals. The architecture features: (1) multi-perspective analysis of molecular features (global, local, uniform, diversity); (2) information diversity mechanisms that preserve low-similarity patterns; (3) emergent knowledge formation through iterative validation rather than supervised training. Evaluated on the QM9 dataset for HOMO-LUMO gap prediction, the CAR mechanism achieves MAE = 1.10 eV using only atomic types, atom counts, and inter-atomic distances—without any electronic structure information. The system accumulates 389 validated patterns through 3,000 inference iterations, demonstrating that useful chemical knowledge emerges from purely local interactions. This work establishes an alternative paradigm for scientific machine learning where intelligence emerges from interaction protocols rather than gradient optimization. Property (philosophy) ; Feature (linguistics) ; Inference ; Mechanism (biology) ; Atom (system on chip) ; Artificial neural network ; Reduction (mathematics) ; Computational model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Junfeng Lu | Huibo Chen | Yanlei Wang | Hongyan He
Source: Chemistry of Materials (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Neuroevolution Potentials for Imidazole Ionic Liquids: Capturing Ionic Correlations and Structure–Property Relationships Ionic liquids (ILs) possess exceptional physicochemical properties, such as negligible vapor pressure, wide electrochemical stability windows, and tunable molecular architectures, making them attractive candidates for advanced electrolyte applications. These properties arise from intricate ionic correlations and variable ionicity between cations and anions, which govern thermal stability, electrical conductivity, and other key behaviors. In this work, we employ the neuroevolution potential (NEP) framework to develop machine learning-based force fields for a series of imidazole ILs with four distinct anions. The NEP models are rigorously validated against density functional theory (DFT) calculations, showing excellent agreement in energies and forces, and outperforming classical force fields in reproducing structural properties. Molecular dynamics simulations based on the NEP accurately predicted physical properties, including density, self-diffusion coefficients, and viscosity, in close agreement with experimental measurements. Importantly, the ML-driven NEP framework enables simulations of much larger system sizes and longer time scales than are feasible with DFT, while achieving significant speedups over classical force fields. Analysis of ionicity revealed values near 1/3, consistent with previous studies, and provided molecular-level insights into the interplay between ionicity and structural features. This study highlights the potential of machine learning-driven force fields for efficient, accurate, and scalable modeling of complex IL systems, providing practical guidance for designing IL-based electrolytes with tailored properties. Ionic bonding ; Molecular dynamics ; Force field (fiction) ; Electrolyte ; Ionic liquid ; Scalability ; Imidazole ; Complex system Machine Learning in Materials Science ; Ionic liquids properties and applications ; Advanced Battery Materials and Technologies
Authors: Antoni Wadowski | Anshul D. S. Parmar | Filip Kaśkosz | Jesper Byggmästar | Jan Wróbel | Mikko J. Alava | Silvia Bonfanti
Source: Materials & Design (preprint, 2026-04-01)
Field: Chemistry — Inorganic Chemistry

Efficient training of machine learning potentials for metallic glasses: CuZrAl validation Interatomic potentials are key to uncovering microscopic structure-property relationships, essential for multiscale simulations and high-throughput experiments. For metallic glasses, their disordered atomic structure makes the development of potentials particularly challenging, resulting in the scarcity of chemistry-specific parametrizations for this important class of materials. We address this gap by introducing an efficient methodology to design machine learning interatomic potentials (MLIPs), benchmarked on the CuZrAl system. Using a Lennard-Jones surrogate model, swap-Monte Carlo sampling, and single-point Density Functional Theory (DFT) corrections, we capture amorphous structures spanning 14 decades of supercooling. These representative configurations, competing with the experimental time scale, enable robust model training across diverse states, while minimizing the need for extensive DFT datasets. The resulting MLIP matches the experimental data and predictions of the classical embedded atom method (EAM) for structural, dynamical, energetic, and mechanical properties. This approach offers a scalable path to develop accurate MLIPs for complex metallic glasses, including emerging multi-component and high-entropy systems. Training (meteorology) ; Artificial intelligence ; Machine learning ; Computer science ; Psychology ; Physics Pigment Synthesis and Properties ; Metallic Glasses and Amorphous Alloys ; Glass properties and applications
Authors: Wenfeng Fu | YanXin Li | Xiaojin Yang | Junwei Zhao | Tongao Yao | Shuai Dong | Zhijun Gao | Weijie Yang
Source: ENGINEERING Chemical Engineering (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Toward intelligent design of solid-state hydrogen storage: trends, challenges, and machine learning insights Software deployment ; Artificial neural network ; Transferability ; Big data ; Computation ; Convolutional neural network Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Electrocatalysts for Energy Conversion
Authors: Yousef Alqurashi
Source: Sustainability (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Tensile Strength in Heat-Treated Steels Using Random Forests for Sustainable Materials Design Accurate prediction of ultimate tensile strength (UTS) is central to the design and optimization of heat-treated steels but is traditionally achieved through costly and iterative experimental trials. This study presents a transparent, physics-aware machine learning (ML) framework for predicting UTS using an open-access steel database. A curated dataset of 1255 steel samples was constructed by combining 18 chemical composition variables with 7 processing descriptors extracted from free-text heat-treatment records and filtering them using physically justified consistency criteria. To avoid information leakage arising from repeated measurements, model development and evaluation were conducted under a group-aware validation framework based on thermomechanical states. A Random Forest (RF) regression model achieved robust, conservative test-set performance (R2 ≈ 0.90, MAE ≈ 40 MPa), with unbiased residuals and realistic generalization across diverse composition–processing conditions. Performance robustness was further examined using repeated group-aware resampling and strength-stratified error analysis, highlighting increased uncertainty in sparsely populated high-strength regimes. Model interpretability was assessed using SHAP-based feature importance and partial dependence analysis, revealing that UTS is primarily governed by the overall alloying level, carbon content, and processing parameters controlling transformation kinetics, particularly bar diameter and tempering temperature. The results demonstrate that reliable predictions and physically meaningful insights can be obtained from publicly available data using a conservative, reproducible machine-learning workflow. Random forest ; Interpretability ; Resampling ; Ultimate tensile strength ; Robustness (evolution) ; Consistency (knowledge bases) ; Generalization ; Feature selection ; Boosting (machine learning) Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Rong Wang | Di Jin | Junkai Hu | Wenbo Liu | Yuning Zhang | Irfan H. Abidi | Sumeet Walia | Baohua Jia | Duan Huang | Jiayang Wu | David Moss
Source: Chip (article, 2026-02-01)
Field: Materials Science — Surfaces, Coatings and Films

AI-guided design and optimization of 2D-material-based optical polarizers On-chip integration of highly anisotropic two-dimensional (2D) materials offers new opportunities for realizing high performance polarization selective devices. Obtaining optimized designs for such devices requires extensively sweeping large parameter spaces, which in conventional approaches relies on massive mode simulations that demand considerable computational resources. Here, we address this limitation by developing a machine learning (ML) model based on fully connected neural networks (FCNNs). Trained by using mode simulation results for low resolution structural parameters, the FCNN model can accurately predict polarizer figures of merits (FOMs) for high resolution parameters and rapidly map the global variation trend across the entire parameter space. We test the performance of the FCNN model using two types of polarizers with 2D graphene oxide (GO) and molybdenum disulfide (MoS2). Results show that, compared to conventional mode simulation approach, our approach can not only reduce the overall computing time by about 4 orders of magnitude, but also achieve highly accurate FOM predictions with an average deviation of less than 0.04. In addition, the measured FOM values for the fabricated devices show good agreement with the predicted ones, with discrepancies remaining below 0.2. These results validate artificial intelligence (AI) as an effective approach for designing and optimizing 2D-material based optical polarizers with high efficiency. Polarizer ; Beam splitter ; Reflectivity ; Optical fiber Optical Coatings and Gratings ; Metamaterials and Metasurfaces Applications ; Machine Learning in Materials Science
Authors: Yuze Ren | Xuetao Yan | Rongwei Fang | Haibo Deng | Yingying Chen | Zhenzhen Li | Lingyan Feng | Xiaogang Qu
Source: Journal of Materials Informatics (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Advancing carbon dots research with machine learning: a comprehensive review Carbon-based nanomaterials, particularly carbon dots (CDs), have attracted growing attention due to their unique optical properties and cost-effective synthesis. Despite their promise, challenges remain in elucidating luminescence mechanisms and achieving controlled synthesis. Traditional trial-and-error approaches are inefficient, while machine learning (ML) offers powerful tools to accelerate materials discovery by capturing complex relationships. This review summarizes recent progress in applying ML to CDs, focusing on three key areas: enhancing the regulation of intrinsic properties, improving detection sensitivity and multicomponent recognition through the analysis of high-dimensional spectral data, and uncovering correlations between molecular features, experimental parameters, and CD performance with explainable ML. These advances enable more rational and efficient design of multifunctional CDs. Finally, we discuss future directions for CD informatics, including the development of structured data resources, the integration of large language models, interpretable ML techniques, and automated experimental platforms. These trends are expected to provide new insights and drive continued innovation in the multifunctional applications of CDs. Key (lock) ; Identification (biology) ; Rational design ; Sensitivity (control systems) Carbon and Quantum Dots Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Han Liu | Liantang Li
Source: Materials & Design (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Regularized ensemble: integrating frequentist-and-bayesian approach to polymer processing-to-property uncertainty • A regularized ensemble (RE) strategy is introduced to quantify model uncertainty. • This strategy leverages both advantages of frequentist and Bayesian approach. • It provides a state-of-the-art accurate yet efficient uncertainty estimation. • A processing stability metric is derived to identify high quality process windows. • The RE strategy is generic to any material fabrication–property uncertainty. Data-driven modeling of polymer processing–property relationship is key to predict and design polymeric product properties. However, this modeling is generally challenged by the lack of prediction uncertainty and reliability evaluation. Here, relying on Bayesian ensembling (BE) approach, we introduce a “regularized ensemble” (RE) strategy—featured by implementation convenience and uncertainty tunability—to evaluate polymer processing–property uncertainty, exemplified by neural network modeling of injection-molded polypropylene toughness. Inherited from BE approach to leverage and integrate Bayesian theory into frequentist statistics, we find that the RE model offers an accurate yet efficient uncertainty estimation when compared to baseline models using classical frequentist or Bayesian approach, including deep ensemble, Bayesian neural network, and Gaussian process regression. Impressively, trained by the real-world small, noisy dataset of 27 samplings in a 4-dimension design space of molding conditions, the RE model exhibits up to two times toughness predictability compared to baseline models, with the estimated uncertainty properly covering the experimental variations, enabling us to derive a stability metric to identify the promising process windows that yield stable specimens of high toughness. Overall, this work establishes an optimal uncertainty evaluation paradigm empowered by RE strategy, versatile to quantify prediction reliability of polymer processing–property modeling and generically material property designs. Frequentist inference ; Uncertainty quantification ; Bayesian probability ; Leverage (statistics) ; Metric (unit) ; Stability (learning theory) ; Bayesian inference ; Gaussian process ; Robustness (evolution) Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; Gaussian Processes and Bayesian Inference
Authors: Siting Hou | Zejie Zhang | Changjian Xie
Source: Journal of Chemical Theory and Computation (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Constructing Diabatic Potential Energy Matrices with Quantum Dynamic Accuracy: A Neural Network Based Δ-Machine Learning Approach A neural network (NN) based Δ-machine learning (Δ-ML) approach, which is based on inexpensive low-level energy data and a small number of high-level energies, for constructing the global diabatic potential energy matrices (PEMs) of the coupled states in molecular systems is proposed. Particularly in this so-called NN-based Δ-ML approach, two schemes, namely, training the low-level and high-level adiabatic energy data simultaneously (scheme A) or separately (scheme B), are introduced to construct the final high-level diabatic PEMs. Taking the nonadiabatic reaction Na(3<i>p</i>) + H<sub>2</sub> → NaH(Σ<sup>+</sup>) + H and photodissociation reaction NH<sub>3</sub> + <i>hv</i> → H + NH<sub>2</sub><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>(</mml:mo><mml:mover><mml:mi>A</mml:mi><mml:mo>~</mml:mo></mml:mover><mml:mo>/</mml:mo><mml:mover><mml:mi>X</mml:mi><mml:mo>~</mml:mo></mml:mover><mml:mo>)</mml:mo></mml:math> for example, this NN-based Δ-ML approach with both schemes A and B is demonstrated to represent the global diabatic PEMs effectively and accurately, as evidenced by not only ∼87% of the high-level calculation costs saved for both two systems, but also by the reproduction of the nonadiabatic reaction probability for the former and absorption spectra and product branching ratios for the latter in further quantum dynamical calculations. Furthermore, it is found that the training efficiency of the schemes A and B is quite similar in the triatomic NaH<sub>2</sub> system, while in the NH<sub>3</sub> system the scheme B behaves much better than scheme A due to its higher degrees of freedom. Diabatic ; Adiabatic process ; Potential energy ; Photodissociation ; Artificial neural network ; Triatomic molecule ; Quantum ; Adiabatic theorem ; Energy (signal processing) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Ahmed Elhalawani | R. Shi | Mateo Londoño | Michał Tomza | Jesús Pérez-Ríos
Source: ACS Omega (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

What is the Diatomic Molecule with the Largest Dipole Moment? We present a machine learning model to predict the electric dipole moment of diatomic molecules using only the atomic properties of the constituent atoms. The model can screen the entire periodic table to identify the molecules with the largest dipole moments for applications in cold molecular sciences or to find the molecule with the largest dipole moment that contains a given atom. Similarly, our model identifies useful trends that explain molecular dipole moments, improving our intuition in chemical physics beyond the paradigm of electronegativity differences. Finally, we condense our model into an analytical expression to predict the dipole moment in terms of atomic properties. Diatomic molecule ; Dipole ; Bond dipole moment ; Moment (physics) ; Electric dipole moment ; Molecule Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Jakub Martinka | Mikołaj Martyka | Biman Medhi | Jiří Pittner | Pavlo O. Dral
Source: Journal of Chemical Theory and Computation (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Flexible Framework for Surface Hopping: From Hybrid Schemes for Machine Learning to Benchmarkable Nonadiabatic Dynamics Nonadiabatic molecular dynamics is a key technique for investigating a broad range of photochemical and photophysical processes. Among the established approaches, surface hopping schemes are widely used and can be easily integrated with various quantum chemistry programs or machine learning models. We present a flexible framework in MLatom that includes a newly implemented Tully's fewest-switches surface hopping algorithm and its time-dependent Baeck-An variant. The capabilities of this framework are demonstrated through three representative examples corresponding to typical stages of a surface hopping study. First, we focus on methods providing energy, energy gradients, and nonadiabatic couplings. We show that the flexibility of user-defined custom models can save computational time and that it is useful for benchmarking machine learning models. Next, we compare curvature-driven surface hopping schemes and show that the Landau-Zener approach outperforms the time-dependent Baeck-An scheme. Finally, we showcase easy-to-use analysis tools for both individual trajectories and trajectory ensembles. This framework enables accelerated development of machine learning models and provides deeper insight into nonadiabatic dynamics. It is available as a part of the open-source MLatom package. Surface hopping ; Flexibility (engineering) ; Surface (topology) ; Benchmarking ; Focus (optics) ; Range (aeronautics) ; Key (lock) ; Potential energy surface Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Zihan Yan | Zheyong Fan | Yizhou Zhu
Source: Journal of Chemical Information and Modeling (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Improving Robustness and Training Efficiency of Machine-Learned Potentials by Incorporating Short-Range Empirical Potentials Machine learning force fields (MLFFs) are powerful tools for materials modeling, but their performance is often limited by training data set quality, particularly the lack of rare event configurations. This limitation undermines their accuracy and robustness in long- and large-scale molecular dynamics simulations. In this work, we demonstrate a simple yet general approach to improving robustness and training efficiency by utilizing a hybrid MLFF framework that integrates an empirical short-range repulsive potential. Using solid electrolyte Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> (LLZO) as a model system, we show that purely data-driven MLFFs fail to prevent unphysical atomistic clustering in extended simulations due to inadequate short-range repulsion. In contrast, the hybrid force field eliminates these artifacts, enabling stable long-time simulations, which are critical for studying various properties of LLZO. Our approach also reduces the need for extensive active learning and performs well with just 25 training configurations. By combining physics-driven constraints with data-driven flexibility, this approach is compatible with most existing MLFF architectures and establishes a universal paradigm for developing robust, training-efficient force fields for complex material systems. Robustness (evolution) ; Training set ; Cluster analysis ; Force field (fiction) ; Field (mathematics) ; Molecular dynamics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Inorganic Chemistry and Materials
Authors: Yan Li | S. Kim | Santosh Shaw | WooPoung Kim | Mohammad Atif Faiz Afzal | H. Shaun Kwak | David A. Nicholson
Source: Journal of Electronic Materials (article, 2026-01-03)
Field: Materials Science — Polymers and Plastics

Material Property Simulation for Advanced Packaging Reliability (semiconductor) ; Dielectric ; Material properties ; Shrinkage ; Thermal expansion ; Thermal ; Polyimide ; Electronic packaging ; Property (philosophy) Synthesis and properties of polymers ; 3D IC and TSV technologies ; Electronic Packaging and Soldering Technologies