jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Harjit Singh
Source: Prabha Materials Science Letters (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

VAE-Guided Transformer Based Deep Learning Framework for Inverse Prediction of Aluminium (Al) Alloy Post-Processing Conditions These days, Machine Learning (ML) is widely used in material science research due to various advancements in algorithmic architecture. ML algorithms can be used for forward and inverse design prediction of the materials. Based on material compositions and processing, inverse design material informatics can produce any specific material profile. However, this machine learning approach is limited due to lack of data and presence of imbalanced datasets. The problem of imbalanced data severely impacts the outcome due to biased machine learning models that favor majority classes and ignore the rare classes in the datasets. To address these issues, the work in this paper proposed a comprehensive deep learning framework for inverse prediction for aluminium (Al) alloys using post-processing conditions. The proposed framework utilized the generative manifold-guided variational autoencoder (VAE) along with the deep learning-based TabTransformer for classification tasks. This transformer-based attention-aware VAE-guided framework worked well enough to capture complex interactions among compositional and mechanical features of Al alloy samples. The performance evaluation results demonstrated that significant improvement was achieved through the proposed framework of VAE-guided TabTransformer. The effectiveness of the proposed work was highlighted with a mass increment in macro F1-scores, which increased from 0.49 to 0.89 when compared to the baseline framework. The improvement was not only due to the majority classes, but the outcomes of minority classes also contributed significantly. Further, the performance was supported by K-fold and statistical validation results. The proposed framework successfully addressed the complex data-imbalanced inverse design problem and proved to be a powerful, generalizable, and intelligent tool for material discovery and optimization. Inverse ; Autoencoder ; Deep learning ; Materials informatics ; Baseline (sea) ; Generative grammar ; Macro ; Artificial neural network Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Yi Zeng | Shichen Zhang | Wen-Yu Guo | Xiao-Yan Zheng
Source: Chemical Communications (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Accelerating organic luminescent material discovery: from quantum chemical calculations to machine learning Organic luminescent materials have attracted significant attention for their pivotal roles in optoelectronic devices, chemical sensing, and biomedical diagnostics. However, the rational design of organic luminescent materials with specific functions remains a challenging task because the photophysical properties of these materials are intricately governed by complex electronic transitions of excited states and subtle structural variations. This complexity is further intensified by the inherent contradictions between key physical parameters, such as the trade-off between quantum efficiency and lifetime, or between singlet-triplet energy gaps and oscillator strength, as well as the vastness of the chemical space. Here, we systematically review recent advances in the rational design of organic luminescent materials, including functional fluorescent dyes, room-temperature phosphorescence systems, and thermally activated delayed fluorescence systems using quantum mechanics calculations and machine learning (ML). Importantly, key molecular descriptors are established <i>via</i> theoretical calculations to bridge microscopic electronic structures with macroscopic photophysical properties, effectively decoupling conflicting performance factors, and ML establishes a robust high-throughput screening framework for the discovery of high-performance candidates, which facilitates the precise "on-demand customization" of advanced organic luminescent materials in the future. Luminescence ; Quantum chemical ; Rational design ; Quantum ; Quantum dot Luminescence and Fluorescent Materials ; Machine Learning in Materials Science ; Synthesis and Properties of Aromatic Compounds
Authors: Wenjin Gao | Chenqiang Hua | Tianchao Niu | Miao Zhou
Source: Surface Science Reports (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Machine learning empowered surface growth of 2D materials: Synthesis, characterization and mechanism Characterization (materials science) ; Field (mathematics) ; Miniaturization ; Electronics ; Mechanism (biology) ; Frontier 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Kadri Muuga | Lisanne Knijff | Chao Zhang
Source: ArXiv.org (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions The molecular electrostatic potential (MEP) is a key quantity for describing and predicting intermolecular and ion-molecule interactions. Here, we assess the ability of machine-learning (ML) models to infer the MEP, based on the equivariant graph-convolutional neural network architecture PiNet2 and trained on dipole and quadrupole moments. For the established QM9 dataset, we find that including the quadrupole contribution in the ML models substantially improves their ability to recover the MEP compared to dipole-only models. This trend is confirmed on the SPICE dataset, which spans a much broader region of organic chemical space. Together, this study underscores the central role of the quadrupole moment as a fitting target for ML models aiming at rapid access to the MEP. Quadrupole ; Dipole ; Artificial neural network ; Moment (physics) ; Intermolecular force ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Kutalmis Gokkus | Selim Unal | Zeynep Gokkus | Ayşegül Özbal | Mahmut Gür | M. Serdar Cavus | Alper S. Akalin | Vural Bütün
Source: International Journal of Hydrogen Energy (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Hyper crosslinked polymers based hydrogen generation: A combined mechanistic, statistical and machine learning approach Catalysis ; Hydrogen production ; Hydrogen ; Artificial neural network ; Random forest ; Multilayer perceptron ; Mechanism (biology) Covalent Organic Framework Applications ; Hydrogen Storage and Materials ; Carbon dioxide utilization in catalysis
Authors: Ansu Mathew | Ahmer A. B. Baloch | Alamin Mohammed Yakasai | Hemant Mittal | Vivian Alberts | Jayakumar Vandavasi Karunamurthy
Source: arXiv (Cornell University) (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Crystal System Prediction for Perovskites Using Augmented X-ray Diffraction Data Prediction of crystal system from X-ray diffraction (XRD) spectra is a critical task in materials science, particularly for perovskite materials which are known for their diverse applications in photovoltaics, optoelectronics, and catalysis. In this study, we present a machine learning (ML)-driven framework that leverages advanced models, including Time Series Forest (TSF), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a simple feedforward neural network (NN), to classify crystal systems, point groups, and space groups from XRD data of perovskite materials. To address class imbalance and enhance model robustness, we integrated feature augmentation strategies such as Synthetic Minority Over-sampling Technique (SMOTE), class weighting, jittering, and spectrum shifting, along with efficient data preprocessing pipelines. The TSF model with SMOTE augmentation achieved strong performance for crystal system prediction, with a Matthews correlation coefficient (MCC) of 0.9, an F1 score of 0.92, and an accuracy of 97.76%. For point and space group prediction, balanced accuracies above 95% were obtained. The model demonstrated high performance for symmetry-distinct classes, including cubic crystal systems, point groups 3m and m-3m, and space groups Pnma and Pnnn. This work highlights the potential of ML for XRD-based structural characterization and accelerated discovery of perovskite materials Perovskite (structure) ; Boosting (machine learning) ; Artificial neural network ; Random forest ; Data pre-processing ; Crystal (programming language) ; Diffraction ; F1 score ; Preprocessor Machine Learning in Materials Science ; Perovskite Materials and Applications ; X-ray Diffraction in Crystallography
Authors: Fei Shuang | Penghua Ying | Kai Liu | Zixiong Wei | Fengxian Liu | Zheyong Fan | Minqiang Jiang | Poulumi Dey
Source: arXiv (Cornell University) (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potentials for Million-Atom Simulations of Multicomponent Alloys Machine learning interatomic potentials (MLIPs) with broad chemical flexibility are important for atomistic simulations of compositionally complex materials such as high-entropy alloys. Here, we study two state-of-the-art MLIP frameworks, the neuroevolution potential (NEP) and the graph atomic cluster expansion (GRACE), for 16 elemental metals and multicomponent alloys. GRACE potential with Finnis-Sinclair type shows substantially higher training efficiency and consistently, though only slightly, better accuracy for mechanical properties, thermal stability, and chemical extrapolation. In contrast, NEP achieves an approximately 60-fold higher inference speed, making it attractive for million-atom molecular dynamics simulations. We further examine uncertainty quantification strategies and find that ensemble-based uncertainty correlates robustly with model error, whereas D-optimality is less reliable for the systems considered here. Large-scale nonequilibrium molecular dynamics simulations of shock propagation further show that NEP, combined with ensemble-based uncertainty quantification, enables efficient and reliable simulations under extreme dynamic conditions. Molecular dynamics ; Interatomic potential ; Inference ; Non-equilibrium thermodynamics ; Flexibility (engineering) ; Thermal ; Uncertainty quantification ; Graph Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Le Huu Nghia | Phạm Thị Bích Thảo | Truong Do Anh Kha | Vo Khuong Dien | Nguyen Thanh Tien
Source: Open MIND (preprint, 2026-02-12)
Field: Materials Science — Materials Chemistry

Efficient molecular dynamics simulation of 2D penta-silicene materials using machine learning potentials Machine Learning Interatomic Potentials (MLIPs) are a modern computational method that allows achieving near-quantum mechanical accuracy (DFT) while still describing large-scale systems in molecular dynamics (MD) simulations. In this work, we use MLIP from DeepMD package and the classical Tersoff potential for SiC (Tersoff.SiC potential) to fully and accurately describe atomic interactions and apply them to molecular dynamics simulations of penta silicene sheet. The results show that the melting points (T$_g$) temperatures of the system in the canonical NVT and isobaric NPT sets are 632 K and 606 K, while the Tersoff.SiC potential have the high melting points, respectively. In addition, the radial distribution function exhibits characteristic peaks at interatomic distances of 2.275 Å\text{} and 2.375 Å, while the Tersoff.SiC potential only describe distance of 2.375 Å. Furthermore, penta silicene was also simulated using on-the-fly machine learning for 10 ps to evaluate the structural stability of the system. This study investigates the thermodynamic properties of two-dimensional penta silicene sheets with pentagonal structures using a high-precision, cost-effective method, contributing further evidence to support experimental synthesis and opening up potential future applications of this material. Silicene ; Molecular dynamics ; Interatomic potential ; Stability (learning theory) ; Radial distribution function ; Function (biology) ; Canonical ensemble Graphene research and applications ; Thermal properties of materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Jane Schlesinger | Simon Hjaltason | Nathan J. Szymanski | Christopher J. Bartel
Source: ArXiv.org (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Thermodynamic assessment of machine learning models for solid-state synthesis prediction Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized. These models aim to circumvent direct first-principles modeling of solid-state phase transformations, instead learning from large databases of successfully synthesized materials. Here, we assess the alignment of several recently introduced synthesis prediction models with material and reaction thermodynamics, quantified by the energy with respect to the convex hull and a metric accounting for thermodynamic selectivity of enumerated synthesis reactions. A dataset of successful synthesis recipes was used to determine the likely bounds on both quantities beyond which materials can be deemed unlikely to be synthesized. With these bounds as context, thermodynamic quantities were computed using the CHGNet foundation potential for thousands of new hypothetical materials generated using the Chemeleon generative model. Four recently published machine learning models for synthesizability prediction were applied to this same dataset, and the resultant predictions were considered against computed thermodynamics. We find these models generally overpredict the likelihood of synthesis, but some model scores do trend with thermodynamic heuristics, assigning lower scores to materials that are less stable or do not have an available synthesis recipe that is calculated to be thermodynamically selective. In total, this work identifies existing gaps in machine learning models for materials synthesis and introduces a new approach to assess their quality in the absence of extensive negative examples (failed syntheses). Metric (unit) ; Predictive modelling ; Convex hull ; Generative grammar ; Work (physics) ; Quality (philosophy) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Jane Schlesinger | Simon Hjaltason | Nathan J. Szymanski | Christopher J. Bartel
Source: Open MIND (preprint, 2026-02-03)
Field: Materials Science — Materials Chemistry

Thermodynamic assessment of machine learning models for solid-state synthesis prediction Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized. These models aim to circumvent direct first-principles modeling of solid-state phase transformations, instead learning from large databases of successfully synthesized materials. Here, we assess the alignment of several recently introduced synthesis prediction models with material and reaction thermodynamics, quantified by the energy with respect to the convex hull and a metric accounting for thermodynamic selectivity of enumerated synthesis reactions. A dataset of successful synthesis recipes was used to determine the likely bounds on both quantities beyond which materials can be deemed unlikely to be synthesized. With these bounds as context, thermodynamic quantities were computed using the CHGNet foundation potential for thousands of new hypothetical materials generated using the Chemeleon generative model. Four recently published machine learning models for synthesizability prediction were applied to this same dataset, and the resultant predictions were considered against computed thermodynamics. We find these models generally overpredict the likelihood of synthesis, but some model scores do trend with thermodynamic heuristics, assigning lower scores to materials that are less stable or do not have an available synthesis recipe that is calculated to be thermodynamically selective. In total, this work identifies existing gaps in machine learning models for materials synthesis and introduces a new approach to assess their quality in the absence of extensive negative examples (failed syntheses). Metric (unit) ; Predictive modelling ; Convex hull ; Generative grammar ; Work (physics) ; Quality (philosophy) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: R. Seaton Ullberg | John Langhout | Megan M. Butala | Simon R. Phillpot
Source: ACS Applied Energy Materials (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Predicting Short-Range Order in Disordered Rocksalt Li-Oxide Cathode Materials with Density Functional Theory and Crystal Graph Neural Networks Li-excess disordered rocksalts have recently emerged as a promising class of materials to replace conventional layered nickel manganese cobalt oxide cathodes as a means to enhance the reversible capacity of Li-ion batteries. However, Li ions on the cation sublattice have been shown to form localized clusters of chemical short-range order, which can affect capacity and Li mobility, high values of which are crucial to the macroscale performance of the cathode. To address this challenge, we employ both ab initio calculations and crystal graph machine learning methods to reveal structural factors contributing to short-range order formation and explore a large compositional space to identify materials that exhibit a thermodynamically stable disordered phase. Ultimately, this high-throughput screening process results in a subset of compositions that are strong candidates for future experimental characterization. Density functional theory ; Artificial neural network ; Order (exchange) ; Graph theory ; Crystal (programming language) ; Cathode Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Shalini Ninoria | Diptonil Banerjee
Source: Macromolecular Symposia (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

A Comprehensive Study on Machine Learning Techniques for Prediction of Material Properties ABSTRACT Machine learning (ML) is becoming a valuable tool for materials science, driving both basic and applied research. It promises to be especially helpful to model complicated surface dynamics and material property prediction—essential for semiconductor manufacturing, thin‐film deposition, and nanotechnology. Although existing ML applications in materials science tend to be straightforward fitting procedures or small‐scale data, this research investigates its ability in 1+1 and 2+1 dimensional growth models and displays precise predictions for material stability and crystal structures. The paper includes ML basics (algorithms, descriptors, databases) and uses ML for surface dynamics, emphasizing ML's contribution to material property control. Property (philosophy) ; Material properties ; Stability (learning theory) ; Surface (topology) ; Material Design ; Crystal (programming language) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Clara Saetta | Alessandro Bonardi | Riccardo Todisco | Ilaria Barlocco | Alberto Villa | Alessandro Morittu | GiovanniMaria Piccini | Giovanni Di Liberto
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Nature and pH-dependent Surface Chemistry of TiN in Aqueous Environment from Ab-Initio and Machine Learning Accelerated Simulations Tin ; Aqueous solution ; Catalysis ; Dissociation (chemistry) ; Density functional theory ; Surface charge ; Titanium nitride ; Point of zero charge Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Titanium Alloys Microstructure and Properties
Authors: Džonatans Miks Melgalvis | Toms Rekis
Source: ChemRxiv (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

GraPhAI: Neural Networks for Solving Centrosymmetric Crystal Structures Crystal structure determination from low resolution diffraction data is challenging due to lack of a general-purpose method for solving the crystallographic phase problem. We have developed graph neural network GraPhAI and propose a new and efficient diffraction data representation in a graph form for deep learning. The trained GraPhAI models are intended for ab initio phasing of down to 2 Å resolution data of typical unit cell volume centrosymmetric crystal structures. The success rate is above 80% for structures containing an atom with Z ≥ 19 (e.g. metal-organic frameworks, coordination compounds, or inorganic structures). For purely organic crystal structures, the success so far is limited. Phaser ; Crystal structure ; Diffraction ; Artificial neural network ; Ab initio ; Representation (politics) ; Graph ; Crystal (programming language) ; Atom (system on chip) Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions
Authors: Toshifumi Mori | Kei-ichi Okazaki | Kang Kim | Nobuyuki Matubayasi
Source: arXiv (Cornell University) (preprint, 2026-03-26)
Field: Materials Science — Materials Chemistry

Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates In complex molecular systems, the reaction coordinate (RC) that characterizes transition pathways is essential to understand underlying molecular mechanisms. This review surveys a framework for identifying the RC by applying deep learning to the committor, which provides the most reliable measure of the progress along a transition path. The inputs to the neural network are collective variables (CVs) expressed as functions of atomic coordinates of the system, and the corresponding RC is predicted as the output by training the network on the committor as the learning target. Because deep learning models typically operate in a black-box manner, it is difficult to determine which input variables govern the predictions. The incorporation of eXplainable Artificial Intelligence (XAI) techniques enables quantitative assessment of the contributions of individual input variables to the predictions. This approach allows the identification of CVs that play dominant roles and demonstrates that the committor distribution on the surface using important CVs is separated by well-defined boundaries. The framework provides an explainable deep learning strategy for assigning a molecular mechanism from the RC and is applicable to a wide range of complex molecular systems. Deep learning ; Artificial neural network ; Identification (biology) ; Range (aeronautics) ; Mechanism (biology) ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xinyu Zheng | Haixia Li | Zhanli Han | Zehua Ren | Shiyang Chai | Lei Zhang | Xu Ji
Source: Chemical Engineering Science (article, 2026-01-02)
Field: Chemistry — Physical and Theoretical Chemistry

High-throughput screening of thermodynamically stable molecular crystal forms using crystal structure prediction method based on deep graph neural network Crystal structure prediction ; Crystal structure ; Crystal (programming language) ; Lattice energy ; Artificial neural network ; Test set ; Lattice (music) ; Stacking Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods