jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Naoya Kuroda | Kenji Ishihara | Tomoya Shiota | Wataru Mizukami
Source: arXiv (Cornell University) (preprint, 2026-03-03)
Field: Materials Science — Materials Chemistry

Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications Machine learning interatomic potentials (MLIPs) evaluate potential energy surfaces orders of magnitude faster while maintaining accuracy comparable to first-principles calculations, and universal MLIPs that cover most of the periodic table are becoming increasingly commonplace. However, existing large-scale datasets have limited or no coverage of heavy elements such as minor actinides crucial in the nuclear field, and universal MLIPs are typically limited to 89 elements. Here, we constructed a heavy element dataset HE26 containing minor actinides, based on experimental and computational literature data. By integrating this with existing molecular and crystal datasets, we developed an open-source universal MLIP covering 97 elements, the broadest elemental coverage to date. The resulting model showed strong performance on the inorganic MPtrj and organic OFF23 test sets and promising accuracy on HE26. The dataset and model open a pathway toward the development of energy resources and the design of novel materials, such as actinide-based high-entropy ceramics, in the nuclear field. Periodic table ; Interatomic potential ; Table (database) ; Energy (signal processing) ; Minor (academic) ; Cover (algebra) ; Experimental data Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Nuclear Materials and Properties
Authors: S. Chandra | H. Hemani | Vivek Chavan
Source: Physics Letters A (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Assessing the influence of orientation and surrounding texture on plastic response of porous crystals through a crystal plasticity and data driven modeling approach Crystallite ; Plasticity ; Anisotropy ; Porosity ; Crystal plasticity ; Void (composites) ; Coalescence (physics) ; Texture (cosmology) Machine Learning in Materials Science ; Metal Forming Simulation Techniques ; Microstructure and mechanical properties
Authors: John L. Provis
Source: Frontiers in Materials (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Structural materials in Frontiers in Materials, 10 years on The Structural Materials section of Frontiers in Materials has published almost 1000 articles during the past 10 years, and this article commemorates and assesses the development of thematic trends among this body of works. The three “grand challenges” identified upon launching the section in 2015 remain relevant: Bottom-up design of materials; Sustainable materials supply; Durable materials for extreme environments. Additional topics that have featured extensively in the journal include Natural materials; AI and machine learning; Materials characterisation and performance; Composites and manufacturing. It is anticipated that each of these topics will continue to contribute to the future of Structural Materials research, taking advantage of advances in computational capabilities and the ever-stronger impetus for sustainable selection and use of materials to drive future innovation in this discipline. Section (typography) ; Special section ; Natural materials ; Materials processing ; Structural material ; Natural (archaeology) ; Sustainable development Machine Learning in Materials Science ; Material Selection and Properties ; Smart Materials for Construction
Authors: Yunhao Fan | Gia-Wei Chern
Source: arXiv (Cornell University) (preprint, 2026-01-07)
Field: Materials Science — Materials Chemistry

Equivariant Neural Networks for Force-Field Models of Lattice Systems Machine-learning (ML) force fields enable large-scale simulations with near-first-principles accuracy at substantially reduced computational cost. Recent work has extended ML force-field approaches to adiabatic dynamical simulations of condensed-matter lattice models with coupled electronic and structural or magnetic degrees of freedom. However, most existing formulations rely on hand-crafted, symmetry-aware descriptors, whose construction is often system-specific and can hinder generality and transferability across different lattice Hamiltonians. Here we introduce a symmetry-preserving framework based on equivariant neural networks (ENNs) that provides a general, data-driven mapping from local configurations of dynamical variables to the associated on-site forces in a lattice Hamiltonian. In contrast to ENN architectures developed for molecular systems -- where continuous Euclidean symmetries dominate -- our approach aims to embed the discrete point-group and internal symmetries intrinsic to lattice models directly into the neural-network representation of the force field. As a proof of principle, we construct an ENN-based force-field model for the adiabatic dynamics of the Holstein Hamiltonian on a square lattice, a canonical system for electron-lattice physics. The resulting ML-enabled large-scale dynamical simulations faithfully capture mesoscale evolution of the symmetry-breaking phase, illustrating the utility of lattice-equivariant architectures for linking microscopic electronic processes to emergent dynamical behavior in condensed-matter lattice systems. Dynamical systems theory ; Lattice (music) ; Adiabatic process ; Homogeneous space ; Quadratic equation ; Artificial neural network ; Equivariant map ; Lattice model (finance) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Rick Oerder | Jan Hamaekers
Source: ArXiv.org (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training We introduce Flexible Cutoff Learning (FCL), a method for training machine learning interatomic potentials (MLIPs) whose cutoff radii can be adjusted after training. Unlike conventional MLIPs that fix the cutoff radius during training, FCL models are trained by randomly sampling cutoff radii independently for each atom. The resulting model can then be deployed with different per-atom cutoff radii depending on the application, enabling application-specific optimization of the accuracy-cost tradeoff. Using a differentiable cost model, these per-atom cutoffs can be optimized for specific target systems after training. We demonstrate FCL with a modified MACE architecture trained on the MAD dataset. For a subset featuring molecular crystals, optimized per-atom cutoffs reduce computational cost by more than 60% while increasing force errors by less than 1%. These results show that FCL enables training of a single general-purpose MLIP that can be adapted to diverse applications through post-training cutoff optimization, eliminating the need for retraining. Cutoff ; Cutoff frequency ; RADIUS ; Training (meteorology) ; Bayesian optimization ; Classifier (UML) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Lorenzo Celiberti | Alexander Ehrentraut | Luca Leoni | Cesare Franchini
Source: arXiv (Cornell University) (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Machine Learning the order-disorder Jahn-Teller transition in LaMnO$_3$ We investigate the Jahn-Teller structural phase transition in LaMnO$_3$ at $T_{JT} \simeq 750$ K using molecular dynamics simulations based on machine-learning force fields trained on ab initio data. Analysis of the site-site correlation function of the distortions reveals that the transition is driven by the ordering of the $Q_2$ Jahn-Teller distortion of the MnO$_6$ octahedra, which acts as the order parameter and establishes the order-disorder nature of the transition. Dynamical local distortions are found to persist above $T_{JT}$. Our results reproduce the experimental temperature dependence of both structural and phonon properties and highlight the presence of anharmonic effects at finite temperature. More broadly, the combined use of machine-learning molecular dynamics and velocity autocorrelation function analysis provides a robust framework for uncovering the microscopic mechanisms of structural phase transitions in correlated materials. In particular, this approach enables a clear distinction between order-disorder transitions and alternative mechanisms, such as displacive behavior, through the temperature evolution of vibrational properties. Autocorrelation ; Phase transition ; Anharmonicity ; Molecular dynamics ; Ab initio ; Function (biology) ; Distortion (music) ; Phase (matter) Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Inorganic Chemistry and Materials
Authors: YuanBin Liu | Y. Zhou | Richard Ademuwagun | Luc Walterbos | J. George | Stephen R. Elliott | Volker L. Deringer
Source: Journal of the American Chemical Society (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Medium-Range Structural Order in Amorphous Arsenic Medium-range order (MRO) is a key structural feature of amorphous materials, but its origin and nature remain elusive. Here, we reveal the MRO in amorphous arsenic (<i>a</i>-As) using advanced atomistic simulations, based on machine-learned potentials derived using automated workflows. Our simulations accurately reproduce the experimental structure factor of <i>a</i>-As, especially the first sharp diffraction peak (FSDP), which is a signature of MRO. We compare and contrast the structure of <i>a</i>-As with that of its lighter homologue, red amorphous phosphorus (<i>a</i>-P): we find that <i>a</i>-As has a more uniform dihedral-angle distribution, and so we confirm that its structure can be thought of as a 3-fold coordinated continuous random network in first approximation, in contrast to the more molecular-cluster-like structure of <i>a</i>-P. The pressure-dependent structural behaviors of <i>a</i>-As and <i>a</i>-P differ as well, and the origin of the FSDP is closely correlated with the size and spatial distribution of voids in the amorphous networks. Our work provides fundamental insights into MRO in an amorphous elemental system, and more widely it illustrates the usefulness of automation for machine-learning-driven atomistic simulations. Amorphous solid ; Arsenic ; Structure factor ; Diffraction ; Contrast (vision) ; Amorphous metal ; Work (physics) ; Impurity Machine Learning in Materials Science ; Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys
Authors: Thierry Barrière | Vincent Placet | Sami Holopainen | N. Niang
Source: Procedia Structural Integrity (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Predicting the Deformation Behavior of Biocomposites using Combined Mathematical and AI-Based Modeling The use of fossil plastic materials is being significantly replaced by renewable raw materials, such as natural short fiber reinforced (NSFR) polymer composites (biocomposites). Due to the increasing popularity of biocomposites nowadays, ranging from their applications in the food packaging to automotive and aerospace industry, volume of their experimental research is huge. However, experimental research is costly and time consuming. In this research, a micromechanically based constitutive model is proposed to investigate and simulate the elastic-viscoplastic and micro- to macro-scopic deformation behavior of the biocomposites consisting of short biofibers (hemp) and a semi-crystalline polymer matrix (PLA). However, also the constitutive mathematical modeling as such is computationally time-consuming when applied to predict long-term deformation behavior in large design spaces. Therefore, a combination of the proposed mathematical model and an AI-model is proposed. Idea of the concept is that the mathematical model is solely used to predict high-quality data for machine learning (ML) which is computationally very efficient. Deformation (meteorology) ; Aerospace ; Constitutive equation ; Mathematical model ; Polymer ; Raw material Natural Fiber Reinforced Composites ; Composite Material Mechanics ; Mechanical Engineering and Vibrations Research
Authors: Roberto Puch-Solis | Farhan Tanvir Santo | Jonathan Miller | Busayo Ajala | Vanitha Kunalan | Saravana Kumar Jayaram | Niamh NicDaéid
Source: Data in Brief (article, 2026-02-23)
Field: Chemistry — Spectroscopy

Ground truth data set of Gas Chromatography Mass Spectrometry (GCMS) analysed synthesised methylamphetamine Controlled substances are typically subjected to chemical analysis for the purpose of identifying them and, in certain instances, determining their purity using a comprehensive characterisation. This requires the analysis of chemical impurities that may be present in a drug sample due to the synthesis process. This process, known as impurity or drug profiling, can be applied to drugs that are chemically synthesised and/or subsequently chemically modified. The profiling of impurities can offer insights into the synthetic methods employed and, at times, the initial chemicals utilised. Our article focuses on data obtained from the repetitive synthesis (<i>n</i> = 152) of methylamphetamine using five distinct synthetic routes or pathways: Rosenmund Reduction, Birch Reduction, Moscow route, Hypophosphorous route and Emde route. Each sample has been analysed using Gas Chromatography Mass Spectrometry (GCMS) producing a chromatogram, which is presented in various formats. A template for publishing GCMS data is given in [1] which provides a systematic and robust method to organise GCMS data that is amenable for automated handling by data scientists and accessible to practitioners. The template has been previously applied to MDMA [2]. This paper is the third in the series and is addressing the impurity of methylamphetamine samples using the five pathways listed above. The data can be used for methylamphetamine characterisation according to the synthesis method used, the automated detection of the synthetic method using machine learning, and for training purposes. Mass spectrometry ; Impurity ; Gas chromatography–mass spectrometry ; Gas chromatography ; Analyte ; Data set ; Sample preparation ; Training set Analytical Chemistry and Chromatography ; Forensic Toxicology and Drug Analysis ; Analytical chemistry methods development
Authors: Abhijith Thoopul Anantharanga | John Plummer | Saryu Fensin | Brandon Runnels
Source: Acta Materialia (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Predicting grain boundary void nucleation sites in microstructure during shock loading with convolutional neural networks Microstructure ; Nucleation ; Void (composites) ; Grain boundary ; Shock (circulatory) ; Convolutional neural network Microstructure and mechanical properties ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Baljinder Singh | Riyapi Das | Ajay Kumar | Shipra Goyal | Pushpender Kumar Sharma
Source: Critical Reviews in Analytical Chemistry (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Integrating Artificial Intelligence with Analytical Techniques for Enhanced Microplastics Analysis The widespread presence of microplastics (MPs) in environmental matrices poses significant ecological and human health risks due to their persistence and bioaccumulation. Conventional detection methods, including Raman, FTIR, and near-infrared spectroscopy, remain limited by spectral overlap, low resolution, and time-consuming analysis. This review highlights recent advances in microscopic, spectroscopic, and electrochemical approaches for MPs detection, with a particular focus on the integration of artificial intelligence (AI) and machine learning (ML). Techniques such as support vector machines, decision trees, random forests, principal component analysis, and deep neural networks have demonstrated improved classification accuracy under challenging conditions, including noisy spectra and polymer degradation. The role of open-access spectral libraries (e.g., SLoPP and SLoPP-E), data augmentation, and AI assisted imaging in enhancing reproducibility and resolution is emphasized. Additionally, ML driven electrochemical platforms enable sensor optimization, real-time data interpretation, and predictive modeling, underscoring AI's transformative potential for scalable MPs monitoring and risk assessment. Microplastics ; Applications of artificial intelligence ; Artificial neural network Machine Learning in Materials Science ; Microplastics and Plastic Pollution ; Composite Material Mechanics
Authors: Zhuohan Li | KyuJung Jun | Bowen Deng | Gerbrand Ceder
Source: Cell Press Blue (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Hierarchical high-throughput screening of alkaline-stable lithium-ion conductors combining machine learning and first-principles calculations Solid-state batteries require lithium-ion conductors that combine high ionic conductivity with stability under harsh electrochemical and chemical conditions. Here, we investigate the chemical factors governing the stability of NASICON-type and garnet-type Li-ion conductors in highly alkaline environments. This is particularly relevant to solid-state Li-air cells operated under humidified air where alkaline conditions arise due to the formation of LiOH discharge products. We implement a hierarchical high-throughput screening workflow that consists of a pre-screening step using a universal machine-learning interatomic potential and a more accurate DFT-based screening. This approach enables rapid evaluation of over 320,000 compositions, from which 209 alkaline-stable candidates are identified. We identify specific cation substitutions that improve alkaline stability in NASICON and garnet compounds and reveal the underlying mechanism. More importantly, we highlight design trade-offs that require careful composition optimization to simultaneously enhance synthesizability, operational stability, and Li-ion/electronic conductivities for practical humid Li-air battery applications. Stability (learning theory) ; Fast ion conductor ; Electrical conductor ; Chemical stability ; Conductivity ; Ionic bonding ; Cathode ; Range (aeronautics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research
Authors: Ryan Jacobs | Lane E. Schultz | Dane Morgan
Source: ArXiv.org (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Kolmogorov-Arnold Networks Applied to Materials Property Prediction Kolmogorov-Arnold Networks (KANs) were proposed as an alternative to traditional neural network architectures based on multilayer perceptrons (MLP-NNs). The potential advantages of KANs over MLP-NNs, including significantly enhanced parameter efficiency and increased interpretability, make them a promising new regression model in supervised machine learning problems. We apply KANs to prediction of materials properties, focusing on a diverse set of 33 properties consisting of both experimental and calculated data. We compare the KAN results to random forest, a method that generally gives excellent performance on a wide range of properties predictions with very little optimization. The KANs were worse, on par, or better than random forest about 35%, 60%, and 5% of the time, respectively, and KANs are in practice more difficult to fit than random forest. By tuning the network architecture, we found property fits often resulted in 10-20% lower errors compared to the standard KAN, and typically gave results comparable to random forest. In the specific context of predicting reactor pressure vessel transition temperature shifts, we explored the parameter efficiency and the interpretable power of KANs by comparing predictions of simple KAN models (e.g., < 50 parameters) and closed-form expressions suggested by the KAN fits to previously published deep MLP-NNs and hand-tuned models created using domain expertise of embrittlement physics. We found that simple KAN models and the resulting closed-form expressions produce prediction errors on par with established hand-tuned models with a comparable number of parameters, and required essentially no domain expertise to produce. These findings reinforce the potential applicability of KANs for machine learning in materials science and suggest that KANs should be explored as a regression model for prediction of materials properties. Random forest ; Artificial neural network ; Context (archaeology) ; Property (philosophy) ; Range (aeronautics) ; Perceptron ; Set (abstract data type) ; Regression Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Gaussian Processes and Bayesian Inference
Authors: Ryan Snyder | Dongru Li | Thang Ho | B. Joon Kim | Hysum Qazi | Xiaoliang Pan | Yihan Shao | Jingzhi Pu
Source: The Journal of Chemical Physics (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Enhancing Gaussian process regression-accelerated QM/MM free energy simulations using atomic environment descriptors Accurate free energy simulations based on combined quantum mechanical and molecular mechanical (QM/MM) potentials are essential for understanding reaction mechanisms in complex environments. Achieving ab initio QM/MM accuracy at the cost of more affordable semiempirical QM/MM methods, thereby enabling efficient sampling, remains a major challenge. To address this, we previously introduced a Δ-machine-learning approach employing Gaussian process regression (GPR) with QM-solute-based molecular descriptors. Here, we extend this approach by using atomic environment descriptors constructed from atom-centered symmetry functions, which incorporate MM-solvent contributions into the GPR input features. Molecular similarity is inferred through a system-specific sum kernel. We trained our models using both an energy-only GPR scheme and a GPR with derivative observation (GPRwDO) scheme that incorporates force information with heteroscedastic noise. On-the-fly model deployment in Chemistry at HARvard Macromolecular Mechanics (CHARMM)-based molecular dynamics simulations is enabled through a GPflow/pyCHARMM interface. We evaluated these approaches on the solution-phase SN2 Menshutkin reaction, using AM1/MM and B3LYP/MM as the base and target levels. The optimized models reduce AM1/MM potential energy errors from ∼13.1 to 1.4 (energy-only GPR) and 2.2 (GPRwDO) kcal/mol, with the corresponding force errors reduced from ∼14.6 to 4.4 and 2.1 (kcal/mol)/Å. The energy-only GPR model predicts a free energy barrier of 14.3 and a reaction free energy of -30.2 kcal/mol, whereas the GPRwDO model predicts 12.7 and -28.7 kcal/mol, both in excellent agreement with high-level benchmarks. Analyses of free energy paths, potentials of mean force, internal forces, and radial distribution functions reveal broad improvements in energetics, force description, and solvation structure. The AM1-GPR(wDO)/MM approaches reach target-level accuracy with an ∼100-fold acceleration. Ground-penetrating radar ; Free energy perturbation ; Potential energy ; Molecular dynamics ; Gaussian ; Energy (signal processing) ; Gaussian process ; Solvation ; Kriging Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies