jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liudmila A. Klimova | Ivan Kruglov | Georgy A. Ermolaev | Dmitriy Grudinin | Aleksey Arsenin | Valentyn S. Volkov | Kostya S. Novoselov
Source: ACS Nano (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Materials Informatics Framework for Accelerated Discovery of High-Refractive-Index 2D Materials Accurate and efficient prediction of the optical properties of two-dimensional (2D) materials is crucial for photonic applications but remains a challenging task due to discrepancies between theoretical and experimental approaches. Here, we present a physics-guided machine learning (ML) framework for accelerated screening of 2D materials. It combines first-principles density functional theory (DFT) calculations and graph neural network models backed by experimental spectroscopic validation and Cauchy-model integration. Within this framework, we collected a database of more than 1000 monolayers of transition metal dichalcogenides (TMDs) along with their optical properties. We also proposed a universal method for defining a physically meaningful thickness of two-dimensional structures, which enabled a correction of the optical properties obtained from PBE-based density functional theory. Using the collected database, we developed a Cauchy-model-based machine learning model to calculate refractive indices in the near-infrared (near-IR) region (755-1064 nm). The developed approach reflects correlations between the atomic structures of monolayers and their optical properties, which are confirmed by extensive testing against independent 2D materials databases. In this way, our ML-driven strategy offers a powerful tool for the rapid screening of novel monolayer materials with tailored optical functionalities, significantly accelerating the discovery and design of next-generation photonic materials. As an application, we further demonstrate how high-refractive-index candidates such as Bi<sub>2</sub>Te<sub>2</sub>Se enable enhanced field confinement and long crosstalk lengths in monolayer waveguides, highlighting their promise for integrated photonics. Photonics ; Monolayer ; Materials informatics ; Crosstalk ; Density functional theory ; Artificial neural network ; Nanophotonics ; Field (mathematics) 2D Materials and Applications ; Topological Materials and Phenomena ; Graphene research and applications
Authors: Yu Harabuchi | Ruben Staub | Min Gao | Nobuya Tsuji | B. List | Alexandre Varnek | Satoshi Maeda
Source: ACS Central Science (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Predicting Enantioselectivity via Kinetic Simulations on Gigantic Reaction Path Networks Asymmetric catalysts exert control over the reactivity and enantioselectivity of chemical reactions, often through their large size and highly flexible geometries. Most computational approaches rely on a predefined selectivity-determining step and assume that selectivity follows the Boltzmann distribution of transition-state conformers. Going beyond this conventional framework, we constructed a reaction path network that captures the kinetically accessible regions of the potential energy surface. A delta-learning neural network potential (ΔNNP) was constructed and applied to an asymmetric organocatalyst of the imidodiphosphorimidates family, comprising over 200 atoms. The ΔNNP achieved DFT-level accuracy with GFN2-xTB as the baseline. Using the ΔNNP-based single component-artificial force induced reaction (denoted by NNP/AFIR) method, we constructed a reaction path network containing 48,463 paths. Kinetic simulations based on this network predicted the reaction mechanism and enantioselectivity. Within the network, numerous paths, including asynchronous concerted and stepwise mechanisms, were found to be energetically competitive and thus to contribute to the overall kinetics. Traffic volume analysis further revealed that intermediates with negligible yields can still play important kinetic roles. Overall, the NNP/AFIR approach provides a powerful framework not only for deepening mechanistic understanding but also for accelerating the rational design of asymmetric catalytic systems with high reactivity and enantioselectivity. Path (computing) ; Reactivity (psychology) ; Kinetic energy ; Catalysis ; Potential energy ; Reaction mechanism ; Selectivity ; Asynchronous communication Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Authors: A. Najla Hosseini | Kristian Kříž | David van der Spoel
Source: Journal of Chemical Theory and Computation (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Beyond Partitioning: Using Force Field Science to Evaluate Electrostatics Models Accurate models for electrostatic and induction interactions are fundamental for computational molecular science, including drug discovery, studies of biomolecular systems and materials design. Given a precise model of the entire charge distributions, the electrostatic interaction between molecules can be calculated accurately using Coulomb's law. Here, we evaluate partitioning methods for deriving charges from electron density as well as the popular method of fitting point charges for use in force field calculations to the electrostatic potential (ESP). For the data set used in this work, which consists of charged amino-acid side chain analogs, inorganic ions and water, the best of these methods yield a root-mean-square deviation (RMSD) of 17 kJ/mol. By combining positive point charges (PC) with Gaussian or Slater distributed negative charges, ESP-fitted models predict electrostatic interactions approximately 30% better than just point charges (RMSD 12 kJ/mol), similar to the Minimal Basis Iterative Stockholder (MBIS-S) method [Verstraelen, T. . <i>J. Chem. Theory Comput.</i> 2016, 12, 3894-3912.]that employs a PC and a Slater charge as well. Since interaction energies are perhaps the most important deliverable of force field calculations, it may be advantageous to train models directly to reproduce energy components from symmetry-adapted perturbation theory (SAPT) calculations, rather than taking a detour through monomer-based charge models. To this end, we employ machine learning using the Alexandria Chemistry Toolkit [van der Spoel, D. . <i>Digit. Discovery</i> 2025, 4, 1925-1935.] to generate parameters for multiple physics-based models that reproduce electrostatic and, optionally, induction interaction energies from SAPT calculations of compound dimers. For a nonpolarizable model combining a PC and a Gaussian distributed charge on the core, the RMSD drops to 3 kJ/mol thanks to direct training on dimer energy components. The approach outlined in this work consists of applying force field science to make apples-to-apples comparisons between models and machine learning to design physics-based force fields that yield interaction energies consistent with SAPT calculations. Together, these tools will enable rapid progress in force field development and enhance the predictive power of molecular simulations for applications in many fields of science. Electrostatics ; Force field (fiction) ; Charge (physics) ; Perturbation theory (quantum mechanics) ; Point particle ; Field (mathematics) ; Point (geometry) ; Electric potential energy ; Molecular dynamics ; Gaussian Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Yue Wang | Caijuan Yang | Xiaoyan Sun | Feng Li | Wenying Zhao | Li Xia | Shuguang Xiang
Source: Polymer (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Data-driven models toward polymer glass transition temperature embedded in structures Glass transition ; Monte Carlo method ; Polymer ; Test set ; Gaussian ; Universality (dynamical systems) ; Linear regression ; Monte Carlo algorithm ; Gaussian process Machine Learning in Materials Science ; Material Dynamics and Properties ; Polymer crystallization and properties
Authors: Salah A. M. Elshourbagy
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Quantum-Classical Performance Benchmark in Nanomaterial Informatics: A Comprehensive Evaluation of Hybrid Quantum Neural Networks versus Deep Learning Architectures Benchmark (surveying) ; Deep learning ; Artificial neural network ; Convergence (economics) ; Quantum ; Deep neural networks ; Hybrid system Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Artificial Intelligence in Healthcare and Education
Authors: Kevin Han | Haolin Cong | Bowen Deng | Amir Barati Farimani
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Smooth Dynamic Cutoffs for Machine Learning Interatomic Potentials Machine learning interatomic potentials (MLIPs) have proven to be wildly useful for molecular dynamics simulations, powering countless drug and materials discovery applications. However, MLIPs face two primary bottlenecks preventing them from reaching realistic simulation scales: inference time and memory consumption. In this work, we address both issues by challenging the long-held belief that the cutoff radius for the MLIP must be held to a fixed, constant value. For the first time, we introduce a dynamic cutoff formulation that still leads to stable, long timescale molecular dynamics simulation. In introducing the dynamic cutoff, we are able to induce sparsity onto the underlying atom graph by targeting a specific number of neighbors per atom, significantly reducing both memory consumption and inference time. We show the effectiveness of a dynamic cutoff by implementing it onto 4 state of the art MLIPs: MACE, Nequip, Orbv3, and TensorNet, leading to 2.26x less memory consumption and 2.04x faster inference time, depending on the model and atomic system. We also perform an extensive error analysis and find that the dynamic cutoff models exhibit minimal accuracy dropoff compared to their fixed cutoff counterparts on both materials and molecular datasets. All model implementations and training code will be fully open sourced. Cutoff ; Inference ; Molecular dynamics ; Atom (system on chip) ; Graph ; Code (set theory) ; Cutoff frequency Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Jonas Wortmann | Larry Luer | Chao Liu | Jerrit Wagner | Tobias Osterrieder | Simon Arnold | Jens Hauch | Thomas Heumüller | Christoph J. Brabec
Source: Advanced Energy Materials (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Accelerating the Development of Organic Solar Cells: A Standardized Protocol with Machine Learning Integration ABSTRACT Organic Photovoltaic (OPV) devices show a large gap between laboratory‐recorded cells with over 20% efficiency and commercial roll‐to‐roll printed modules reaching a maximum half that efficiency. A novel OPV material not only needs high efficiency, but must be processable in architectures suited for large‐scale applications and provide sufficient stability under various stress factors. We present a holistic screening protocol to cover all relevant aspects of OPV material development. Using machine learning techniques together with systematic experimental protocols, only a minimum amount of a novel semiconductor is necessary. We utilize process parameters, optical features, and IV data to explore the processing window, benchmark process stability, and enable structure‐property predictions. We implement a combinatorial degradation protocol that investigates key stress factors, like temperature, oxygen, and illumination, at different stages of device fabrication. Testing partially finished devices, conventional and inverted architectures, as well as hole‐only and electron‐only devices, enables the identification of individual layers responsible for degradation. The protocol includes a solvent test to investigate processability with green solvents. The systematic data collected in this protocol provides a general and reliable basis for material development and the imminent creation of digital twins for OPV. Protocol (science) ; Benchmark (surveying) ; Photovoltaic system ; Process (computing) ; Organic solar cell ; Organic semiconductor Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Pengcheng Li | Nengrong Tu | Yang Yang | Junxiang Li | Huilin Hou | Dongjiang Yang | Weiyou Yang
Source: Nature Communications (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Multi-field coupling enhanced plasmonic Moδ+ active site to efficiently hydrolyze ammonia borane Rapid recombination of photogenerated carriers and weak driving forces to inject hot electrons are critical bottlenecks in solar-driven ammonia borane hydrolysis. Herein, aided by machine learning, plasmon polarization-induced multi-field coupling is developed to enhance ammonia borane hydrolytic activity. The reconstructed surface unsaturated Mo<sup>δ+</sup> active sites exhibit well activity and high stability over 100 hours in AB hydrolysis, which deliver a turnover frequency up to 5806 min<sup>-1</sup>, representing competitiveness compared to non-noble and noble-metal based catalysts ever reported. It is verified that the polarized electric field facilitates carrier separation through incorporating polarization components (O<sub>v</sub> and -OH), thereby promoting electron accumulation around Mo<sup>δ+</sup> active sites. Meanwhile, the local electric field enables highly delocalized hot electrons through plasmon oscillation, thus lowering the reaction barrier between Mo<sup>δ+</sup> and AB. In this work, the hot electrons are efficiently channeled via an enhanced feedback pathway, facilitating their transfer into B-H antibonding orbitals toward boosted AB hydrolysis. Ammonia borane ; Active site ; Plasmon ; Coupling (piping) ; Ammonia ; Hydrolysis Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Boron and Carbon Nanomaterials Research
Authors: Guangbao Mi | Yuanzhi Sun | Ruochen Sun | Peijie Li | Nan Sui | Liangju He | Fuli Dong
Source: Materials & Design (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

Corrigendum to “Machine learning assisted optimization design and microstructure-property evolution mechanisms of 600 °C high temperature burn resistant titanium alloys” [Mater. Des. 261 (2026) 115381] Titanium ; Titanium alloy ; Burn-in ; Design elements and principles Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Metallurgy and Material Forming
Authors: Augusto D. Alvarenga | Marcos R.V. Lanza
Source: Journal of Environmental Management (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Application of XGBoost using data from the oxygen reduction reaction of carbonaceous materials for H2O2 electrosynthesis Carbon-based materials, such as carbon black, have emerged as prominent candidates for the cathodic electrosynthesis of H<sub>2</sub>O<sub>2</sub> due to their low cost, durability, and ease of chemical modification. While recent advances in catalyst design and parameter optimization have improved the electroasynthesis efficiency, the large number of interdependent features introduces complexity and unpredictability in the development of the system. In this context, machine learning (ML) algorithms have demonstrated great potential when used in interpreting complex datasets and deriving meaningful insights in diverse areas, including electrochemistry. While the selectivity and efficiency of metal-free carbon-based catalysts are strongly influenced by their surface functional groups, having a precise control over these functional groups remains a challenging task, and this affects the interpretation of experimental results. In this study, the XGBoost algorithm was used to analyze literature data on experimental conditions for H<sub>2</sub>O<sub>2</sub> electrosynthesis, physicochemical characterizations, and the application of materials and their influence on H<sub>2</sub>O<sub>2</sub> production efficiency. XPS analysis, Raman spectroscopy, BET surface area analysis, contact angle measurements, and electrical property analyses were used to characterize the electrocatalysts. The developed model demonstrated strong generalization capabilities, even when tested on external datasets not included in the training program. Furthermore, hyperparameter optimization analysis was used to refine the database, where the most relevant catalyst features were identified. This analysis enabled the creation of a more efficient model with reduced computational demands. Finally, thorough discussions were put forth regarding the challenges involving the construction of a robust database from fragmented experimental literature. This work provides useful contributions and insights into the application of ML in H<sub>2</sub>O<sub>2</sub> electrosynthesis from experimental data, the construction of a structured database of carbonaceous catalysts, and the systematic interpretation of modeling results, paving the way for the rational design of new materials. Electrosynthesis ; X-ray photoelectron spectroscopy ; Generalization ; Experimental data ; Catalysis ; Response surface methodology ; Hyperparameter ; Cathodic protection Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Lixin Fang | LiQin Qin | Limin Zhang | Hao Zhou | Xudong He | Zekun Ren | Tongyi Zhang | Yi Liu
Source: Preprints.org (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

Transfer Learning from Homogeneous to Heterogeneous: Fine-Tuning a Pretrained Interatomic Potential for Multicomponent Mo Alloys with Localized Substitutional Alloying Machine learning interatomic potentials (MLIPs) are typically constructed for homogeneous crystalline systems that exhibit only minimal local deviations from equilibrium configurations. However, substitutional alloying elements in multicomponent engineering alloys are often distributed in a locally heterogeneous form. To address this, we develop a fine tuned MLIP based on the MACE foundation model, specifically tailored for Mo based dilute alloys containing one or two out of 20 substitutional elements: Cr, Fe, Mn, Nb, Re, Ta, Ti, V, W, Y, Zr, Al, Zn, Cu, Ag, Au, Hg, Co, Ni, and Hf. The model is trained on more than 7,000 non equilibrium structures derived from first principles density functional theory (DFT) calculations. The optimized large scale fine tuned model attains state of the art accuracy, with mean absolute error (MAE) and root mean square error (RMSE) of 2.27 meV/atom and 3.79 meV/atom for energy predictions, and 13.83 meV/Å and 24.26 meV/Å for force predictions, respectively. Systematic evaluation of model transferability to unseen alloying elements under different data splitting protocols demonstrates that incorporating even a modest set of new element DFT data during refinement reduces the energy MAE below ~20 meV/atom. The fine tuned models reduce the MAE by approximately 7–10 times compared to models trained from scratch, and by 10–20 times relative to zero shot foundation models. This performance gain remains consistent across varying dataset sizes (equilibrium vs. non equilibrium structures) and model scales. Our work illustrates the efficacy of transfer learning from globally homogeneous systems to locally heterogeneous multi element alloy environments, delivering a robust MLIP tool for the accelerated design of multicomponent alloys. Transferability ; Alloy ; Work (physics) ; Homogeneous ; Density functional theory ; Mean squared error ; Foundation (evidence) ; Approximation error Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Yongshuai Xiu | Xiaohu Deng | Yixiao Sun | Yuedong Yuan | Gang Shen | Zunzhong Du | Xiaojun Yang | Dongying Ju
Source: Metalurgija (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Data-drivenPrediction and Application of Steel Material Parameters Accurate prediction of steel material parameters during heat treatment is essential for reliable finite element analysis (FEA) and process optimisation. Conventional experimental measurements and empirical models are often costly, time-consuming, and difficult to generalise to complex chemistries, microstructures, and temperature ranges. In this work, a data-driven prediction model is established using a comprehensive dataset that integrates simulation and experimental data, covering 18 elemental compositions, three typical microstructures, and a wide temperature range. Six key parameters are predicted simultaneously: thermal conductivity, specific heat capacity, yield stress, coefficient of thermal expansion, Young’s modulus, and density. Five machine learning models are evaluated, among which XGBoost shows the best performance for thermal parameters, while Gradient Boosting provides the highest accuracy for mechanical properties. After hyperparameter optimisation with grid search and cross-validation, all models achieve R² values above 0.99 and relative prediction errors within 5 %. An integrated Steel Materials Data Management System (S-MDMS) is further developed to combine data storage, visualisation, and online property prediction. The proposed model provides an efficient route for rapid acquisition and application of steel parameters in FEA-based heat treatment design and process optimisation. Hyperparameter ; Finite element method ; Hyperparameter optimization ; Thermal ; Boosting (machine learning) ; Process (computing) ; Material properties ; Approximation error ; Gradient boosting Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgical Processes and Thermodynamics
Authors: Mikołaj Martyka | Joanna Jankowska | Hans Lischka | Pavlo O. Dral
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients The recent development of machine learning (ML) methods for quantum chemistry has tremendously boosted the efficiency of molecular calculations. In this work, we use ML to enable nonadiabatic molecular dynamics (NAMD) simulations without access to the analytical energy gradients from the underlying target electronic structure method. By fine-tuning our foundational model for excited states, OMNI-P2x, on energies alone and leveraging automatic differentiability to obtain forces, we eliminate the gradient computation bottleneck that restricts calculations, such as NAMD, to methods with available analytical derivatives. First, we validate the method on the benchmark system, fulvene, demonstrating that gradient-free ML potentials accurately reproduce NAMD populations and dynamics across multiple levels of theory: AIQM1/MRCI, CASSCF, and MRSF-TDDFT. This enables, for the first time, performing dynamics at the QD-NEVPT2 level, where analytical gradients remain unavailable. We further benchmark the protocol on cyclohexadiene photoinduced ring-opening, where gradient-free training on XMS-CASPT2 energies reproduces reference dynamics with high accuracy, and compare them to QD-NEVPT2 results. Finally, we apply the approach to <i>trans</i>-azobenzene, a prototypical molecular photoswitch, by performing fully dimensional simulations of its photoisomerization dynamics at the CASSCF and QD-NEVPT2 levels, establishing the highest-level excited-state simulations of this photoreaction to date. Molecular dynamics ; Bottleneck ; Benchmark (surveying) ; Computation ; Dynamics (music) ; Excited state ; Quantum ; Potential energy ; Quantum dynamics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics