jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Miyazaki | Atsuhiro Tomita | Akihide Hayashi | So Takemoto | Mizuki Takemoto | Hodaka Mori
Source: arXiv (Cornell University) (preprint, 2026-03-17)
Field: Materials Science — Materials Chemistry

PFP/MM: A Hybrid Approach Combining a Universal Neural Network Potential with Classical Force Fields for Large-Scale Reactive Simulations Universal machine-learning interatomic potentials (uMLIPs) enable reactive molecular simulations with near-DFT accuracy, yet applying them efficiently to large, realistic condensed-phase systems remains computationally demanding. Here we present PFP/MM, a hybrid approach that combines a uMLIP, PreFerred Potential (PFP), with molecular mechanics (MM) to enable both large-scale and long-time simulations that are challenging for uMLIP-only calculations. Using an alanine dipeptide in explicit water, we achieve multi-ns/day enhanced sampling and obtain a Ramachandran plot consistent with established basins. For an intramolecular nucleophilic addition reaction in a polar solvent environment, we reproduce the expected solvent-induced stabilization in the free-energy profile. We further apply the approach to a cytochrome P450 Compound I hydroxylation reaction and obtain a free-energy landscape consistent with the accepted reaction mechanism. These results demonstrate that uMLIP-based reactive simulations can be applied to diverse condensed-phase processes in large, realistic environments. Ramachandran plot ; Molecular dynamics ; Intramolecular force ; Plot (graphics) ; Force field (fiction) ; Discretization ; Potential energy ; Artificial neural network Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Bikash Timalsina | Huy Gia Nguyen | Keivan Esfarjani
Source: Journal of Applied Physics (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Effect of stoichiometry on thermodynamic and thermal transport properties of entropy-stabilized oxide MgCoNiCuZnO5 Lattice distortion in a crystal can have a strong effect on its thermal transport properties. Herein, using a neuroevolution machine learning potential and molecular dynamics simulations, we demonstrate that the lattice thermal conductivity of MgCoNiCuZnO5 (J14) decreases from κ≃2.60±0.20 W m−1 K−1 at 300 K to κ≃2.07±0.05 W m−1 K−1 at 900 K, consistent with experimental findings. Our simulations indicate that the reduction in lattice thermal conductivity as a function of temperature in J14 is due to a decreased mean free path of propagons. Next, we investigated the bounds on κ when reducing or increasing distortions. This is done by varying stoichiometry to make atomic size distribution of transition metal elements more or less uniform: the composition of zinc and nickel in J14 was varied by 30% to make atomic sizes more and less uniform, respectively. While adding more Zn increases distortions, adding Ni reduces it. As a result, we find that the related changes in the thermal conductivity with 30% more zinc (Mg0.2Co0.2Ni0.14Cu0.2Zn0.26O) and 30% more nickel (Mg0.2Co0.2Ni0.26Cu0.2Zn0.14O) are, respectively, 2.54±0.20 and 2.77±0.20 W m−1 K−1 at room temperature. This shows that there is indeed a correlation between the amount of distortions and the thermal conductivity in these compounds. We see larger mean free paths (MFPs) in the less distorted (30% Ni) compound, while MFPs in the more distorted compound are shorter. The MFPs are associated with phonon-like vibrational modes (also called propagons). These results imply that the propagons are mostly responsible for the changes in thermal conductivity. This is supported by the slight decrease in the thermal conductivity with temperature, implying the important contribution of propagons in thermal transport in these distorted high-entropy materials. The neuroevolution machine learning interatomic potential implemented in Graphics Processing Unit Molecular Dynamics is a reliable tool to investigate thermal transport in disordered systems beyond the perturbative approach. Thermal conductivity ; Stoichiometry ; Zinc ; Nickel ; Mean free path ; Oxide ; Thermal ; Lattice (music) Thermal Expansion and Ionic Conductivity ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Yunqing Liu | Yi Zhou (2723) | Fan, Wenqi
Source: arXiv (Cornell University) (preprint, 2026-02-28)
Field: Materials Science — Materials Chemistry

Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions Molecule representation learning is crucial for understanding and predicting molecular properties. However, conventional atom-centric models, which treat chemical bonds merely as pairwise interactions, often overlook complex bond-level phenomena like resonance and stereoselectivity. This oversight limits their predictive accuracy for nuanced chemical behaviors. To address this limitation, we introduce \textbf{DeMol}, a dual-graph framework whose architecture is motivated by a rigorous information-theoretic analysis demonstrating the information gain from a bond-centric perspective. DeMol explicitly models molecules through parallel atom-centric and bond-centric channels. These are synergistically fused by multi-scale Double-Helix Blocks designed to learn intricate atom-atom, atom-bond, and bond-bond interactions. The framework's geometric consistency is further enhanced by a regularization term based on covalent radii to enforce chemically plausible structures. Comprehensive evaluations on diverse benchmarks, including PCQM4Mv2, OC20 IS2RE, QM9, and MoleculeNet, show that DeMol establishes a new state-of-the-art, outperforming existing methods. These results confirm the superiority of explicitly modelling bond information and interactions, paving the way for more robust and accurate molecular machine learning. Consistency (knowledge bases) ; Pairwise comparison ; Representation (politics) ; Property (philosophy) ; Covalent bond ; Regularization (linguistics) ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ryan J. Szukalo | Andreas Neophytou | Axel Gomez | Nicolás Giovambattista | Francesco Sciortino | Pablo G. Debenedetti
Source: Proceedings of the National Academy of Sciences (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Energy landscape statistics and thermodynamics of a machine-learned model of water Water's anomalous thermodynamic behavior arises from the presence of intricate hydrogen-bond networks that are highly sensitive to many-body interactions, challenging molecular modeling for decades. The ongoing machine learning revolution has opened the possibility of performing quantum-accurate liquid-structure calculations at affordable computational cost. Beyond reproducing water's thermodynamic properties with high fidelity, such simulations provide a stringent benchmark for theoretical models and a route to deeper physical understanding. We use the recently developed machine-learned Deep Potential Many-Body Polarizable water model to show that the free energy of supercooled water can be accurately modeled with the potential energy landscape formalism. The resulting equation of state predicts the presence of a liquid-liquid critical point in excellent agreement with recent estimates. Together with previous studies based on empirical classical water potentials, it confirms that the potential energy landscape of water is Gaussian, providing a unifying framework for extracting thermodynamic behavior across model complexity, from empirical force fields to quantum-trained neural network models. Benchmark (surveying) ; Energy landscape ; Water model ; Work (physics) ; Liquid water ; Supercooling ; Energy (signal processing) ; Artificial neural network ; Empirical modelling Machine Learning in Materials Science ; Quantum many-body systems ; Quantum, superfluid, helium dynamics
Authors: Dipok Deb
Source: Journal of International Crisis and Risk Communication Research (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Superconducting Critical Temperature: A Linear and Regularized Linear Modeling Approach This study adopts a data-driven approach to estimate the critical temperature of superconducting materials using linear machine learning models. A comprehensive dataset derived from material physico-chemical properties was analyzed after systematic preprocessing and standardization. Three linear modeling strategies—Linear Regression, Ridge Regression, and Linear Regression with Subset Selection—were developed and evaluated using standard regression performance metrics. The findings demonstrate that both basic and regularized linear models can effectively capture the relationship between material features and superconducting behavior, offering robust and interpretable predictions. While feature selection enhances model transparency, it comes with a modest reduction in predictive capability. Overall, this work emphasizes the value of simple, well-regularized linear models as efficient and interpretable tools for understanding and prediction of superconducting properties. Linear model ; Linear regression ; Feature selection ; Preprocessor ; Feature (linguistics) ; Proper linear model ; Model selection ; Regression analysis ; Linear prediction Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Superconductivity in MgB2 and Alloys
Authors: Andrea Fantasia | Daniele Lanzoni | Niccolò Di Eugenio | Angelo Monteleone | Roberto Bergamaschini | Francesco Montalenti
Source: Advanced Theory and Simulations (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

A parametrically‐Conditioned Deep Learning Surrogate for Coherent Spinodal Decomposition ABSTRACT A machine‐learning framework is designed to tackle the spinodal decomposition of coherently strained alloy microstructures, with cubic anisotropy in elastic constants. A database of phase‐field simulations, leveraging a Green's function approximation of the elastic field, is constructed for randomly chosen composition fields and widely variable misfit strain, producing a continuum variation in the phase morphology from smooth to strongly anisotropic domains. A convolutional recurrent neural network is then trained to accurately predict the full time‐evolution sequence under the explicit conditioning of the known misfit parameter, at a reduced computational cost. Extensive error analysis at the pixel level and for global descriptors is used to assess the model accuracy and evaluate its generalization capability on longer timescales and larger computational domains. The model returns a one‐to‐one match of the ground‐truth simulations over the temporal range of training sequences. Moreover, it can reliably predict average behaviors for sequences several times longer the training ones, albeit losing one‐to‐one accordance. As a proof, the NN trained model is used for reconstructing the full phase diagram of the system, achieving a degree of accuracy. The proposed framework is general and can be applied beyond the specific, prototypical system here considered, enabling high‐throughput parametric studies. Spinodal decomposition ; Parametric statistics ; Artificial neural network ; Deep learning ; Generalization ; Convolutional neural network ; Range (aeronautics) ; Phase (matter) ; Function (biology) Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Model Reduction and Neural Networks
Authors: Kai Gu | Yingping Liang | S. Andrew Peng | Aotian Guo | H. Zhong | Ying Fu
Source: arXiv (Cornell University) (preprint, 2026-01-04)
Field: Materials Science — Materials Chemistry

A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties. Although deep learning offers a potential methodology to achieve generative inverse design, it is still hindered by the scarcity of high-quality datasets that align nanocrystal synthesis routes with their properties. Here, we present the construction of a large-scale, aligned Nanocrystal Synthesis-Property (NSP) database and demonstrate its capability for generative inverse design. To extract structured synthesis routes and their corresponding product properties from literature, we develop NanoExtractor, a large language model (LLM) enhanced by well-designed augmentation strategies. NanoExtractor is validated against human experts, achieving a weighted average score of 88% on the test set, significantly outperforming chemistry-specialized (3%) and general-purpose LLMs (38%). The resulting NSP database contains nearly 160,000 aligned entries and serves as training data for our NanoDesigner, an LLM for inverse synthesis design. The generative capability of NanoDesigner is validated through the successful design of viable synthesis routes for both well-established PbSe nanocrystals and rarely reported MgF2 nanocrystals. Notably, the model recommends a counter-intuitive, non-stoichiometric precursor ratio (1:1) for MgF2 nanocrystals, which is experimentally confirmed as critical for suppressing byproducts. Our work bridges the gap between unstructured literature and data-driven synthesis, and also establishes a powerful human-AI collaborative paradigm for accelerating nanocrystal discovery. Nanocrystal ; Generative grammar ; Inverse ; Generative model ; Product (mathematics) Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Inorganic Chemistry and Materials
Authors: Kai Gu | Yingping Liang | S. Andrew Peng | Aotian Guo | H. Zhong | Ying Fu
Source: ArXiv.org (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties. Although deep learning offers a potential methodology to achieve generative inverse design, it is still hindered by the scarcity of high-quality datasets that align nanocrystal synthesis routes with their properties. Here, we present the construction of a large-scale, aligned Nanocrystal Synthesis-Property (NSP) database and demonstrate its capability for generative inverse design. To extract structured synthesis routes and their corresponding product properties from literature, we develop NanoExtractor, a large language model (LLM) enhanced by well-designed augmentation strategies. NanoExtractor is validated against human experts, achieving a weighted average score of 88% on the test set, significantly outperforming chemistry-specialized (3%) and general-purpose LLMs (38%). The resulting NSP database contains nearly 160,000 aligned entries and serves as training data for our NanoDesigner, an LLM for inverse synthesis design. The generative capability of NanoDesigner is validated through the successful design of viable synthesis routes for both well-established PbSe nanocrystals and rarely reported MgF2 nanocrystals. Notably, the model recommends a counter-intuitive, non-stoichiometric precursor ratio (1:1) for MgF2 nanocrystals, which is experimentally confirmed as critical for suppressing byproducts. Our work bridges the gap between unstructured literature and data-driven synthesis, and also establishes a powerful human-AI collaborative paradigm for accelerating nanocrystal discovery. Nanocrystal ; Generative grammar ; Inverse ; Generative model ; Product (mathematics) Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Inorganic Chemistry and Materials
Authors: Kareem Abdelmaqsoud | Daniel Sinclair | Venkata Satya Surya Amaranth Karra | S. Mohadeseh Taheri-Mousavi | Michael Widom | Bryan A. Webler | John R. Kitchin
Source: arXiv (Cornell University) (preprint, 2026-01-16)
Field: Materials Science — Materials Chemistry

Computational Design of Ductile Additively Manufactured Tungsten-Based Refractory Alloys Tungsten exhibits exceptional temperature and radiation resistance, making it well-suited for applications in extreme environments such as nuclear fusion reactors. Additive manufacturing offers geometrical design freedom and rapid prototyping capabilities for these applications, provided the intrinsic brittleness and low printability of tungsten can be overcome. Designing tungsten alloys with improved ductility, and thus printability in additive manufacturing, can be accelerated using a computationally derived performance predictor to screen out brittle compositions. Calculations of the Pugh ratio using density functional theory may serve this purpose, given its correlation with ductility. This process can be made more efficient through the use of machine learning interatomic potentials to accelerate density functional theory calculations. Here, we demonstrate that machine learning interatomic potentials can effectively identify optimal alloy compositions in the W-Ta-Nb system along the melting point-Pugh ratio Pareto front. The trend in Pugh ratio as a function of tungsten fraction is explained in terms of the electronic density of states at the Fermi level. Experimental validation reveals a strong correlation between the computed Pugh ratio and the observed crack fractions in additively manufactured alloys. Notably, the two alloys predicted to have the highest Pugh ratio values, W20Ta70Nb10 and W30Ta60Nb10, exhibit no intergranular microcracking in experiments. Tungsten ; Brittleness ; Density functional theory ; Fusion ; Intermetallic ; Alloy ; Refractory metals ; Interatomic potential Fusion materials and technologies ; Advanced materials and composites ; Additive Manufacturing Materials and Processes
Authors: Mengxia Mo | Haiyang Yu | Xing Zhou | Wangdong Yang | Can Leng | Wenwu Zhou | Wei‐Qing Huang | Chengkun Wu | Jie Liu
Source: Advanced Functional Materials (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

CMD‐FEP: Machine‐Learned Free‐Energy Prediction for Efficient Screening of Material Interfacial Binder ABSTRACT Identifying interfacial binders for energetic materials remains constrained by the inherent trade‐off between computational speed and predictive fidelity. Experimental screening is labor‐intensive, while empirical force‐field molecular dynamics (MD) offers efficiency at the cost of accuracy. Conversely, high‐fidelity approaches such as free‐energy perturbation (FEP) are computationally prohibitive for large chemical spaces. Here, we develop conventional molecular dynamics‐free energy perturbation(cMD‐FEP), an automated Uni‐Mol‐based machine learning framework that seamlessly bridges conventional MD and FEP to enable high‐fidelity, high‐throughput screening of interfacial binders. cMD‐FEP employs staged, independent task‐specific fine‐tuning—first on large‐scale MD trajectories and subsequently on FEP data—yielding an end‐to‐end predictive pipeline that directly maps molecular structures to interfacial interaction and free energies. Trained on datasets of ∼6.3 × 10 4 simulation‐derived entries, cMD‐FEP achieves FEP‐level accuracy with orders‐of‐magnitude acceleration, screening ∼10 6 octogen (HMX)–binder pairs within 10 min and predicting reliable free energies for ∼3 × 10 4 candidates. Clustering of top‐ranking binders identifies several noncanonical chemotypes with superior adhesion strength. Experimental validation on nitrogen‐containing binders confirms the predicted free‐energy trends, demonstrating strong agreement with cMD‐FEP results. This framework exhibits remarkable generalizability and robustness, offering a potential route for data‐driven discovery of organic interfacial functional materials beyond energetic systems. Molecular dynamics ; Generalizability theory ; Pipeline (software) ; Cluster analysis ; Experimental data ; Energy (signal processing) ; Multiscale modeling Machine Learning in Materials Science ; Energetic Materials and Combustion ; Crystallography and molecular interactions
Authors: Jinzhe Ma | Xiaoyan Fu | Wenbo Xie | P. Hu
Source: Journal of Chemical Theory and Computation (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

From Pretrained to Precision: Fine-Tuning Universal Interatomic Potentials for Accurate Catalytic Reaction Simulations Universal machine learning interatomic potentials (uMLIPs) represent a significant advancement in interatomic potential modeling, offering remarkable predictive accuracy across a wide range of chemical systems. However, their applications in catalytic reaction simulation are limited by their lack of accuracy in describing reactions, especially in reaction barrier prediction. In this study, we evaluate two established uMLIPs and use fine-tuning strategies to enhance their performance for the prediction of catalytic reaction prediction. We systematically compared the predictive accuracy, data efficiency, and generalization capabilities of two approaches, fine-tuning and training from scratch, using the accuracy of the original pretrained uMLIPs as a baseline. Specifically, we evaluated the applicability of the approaches across a range of tasks, from relatively simple applications such as molecular dynamics (MD) simulations and adsorption energy calculations to more complex challenges such as transition state searches. We also analyzed model performance across varying training set sizes to identify the critical data threshold needed for accurate reaction predictions. Additionally, we assessed the extrapolative generalization of the models by examining improvements in predictive accuracy for unseen elements following fine-tuning across both simple and complex tasks. Our results show that fine-tuning uMLIPs significantly improves the accuracy of reaction energy predictions, reducing the mean absolute error (MAE) to 0.09 eV, compared to 0.38 eV for the original uMLIPs. Notably, the fine-tuned models require only 10%-30% of the data used for training from scratch, yielding a stable and reliable performance. Moreover, the generalization capabilities of the uMLIPs were preserved after fine-tuning. This approach shows significant promise for extending the uMLIPs applicability to diverse catalytic reaction systems. Generalization ; Training set ; Range (aeronautics) ; Molecular dynamics ; Set (abstract data type) ; Interatomic potential ; Energy (signal processing) ; Experimental data Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Katie Yue Liu | Xiyu Zhao | Brian F. Woodfield | Siyuan Zhao | Jipeng Luo | Quan Shi
Source: The Journal of Chemical Thermodynamics (article, 2026-01-24)
Field: Chemistry — Physical and Theoretical Chemistry

Development and prospect of low-temperature adiabatic calorimetry Adiabatic process ; Calorimetry ; Modular design ; Heat capacity ; Development (topology) ; Thermal thermodynamics and calorimetric analyses ; Chemical Thermodynamics and Molecular Structure ; Thermal properties of materials
Authors: Zhihui Tian | Ethan Suwandi | Tomas Oppelstrup | Fei Zhou | See PDF
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Scaling Kinetic Monte-Carlo Simulations of Grain Growth with Combined Convolutional and Graph Neural Networks Autoencoder ; Scalability ; Convolutional neural network ; Scaling ; Inference ; Graph ; Reduction (mathematics) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks