jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rupam Bhaduri | S Manasa | Mani Karthik | S. Sakthivel
Source: International Journal of Modern Physics B (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Stable Spinels for Energy Applications Using Dense Neural Network Approach The previous drawback in spinel materials research is that screening for direct band gaps and thermal stability using conventional ab initio methods is computationally intensive and slow, limiting the discovery of promising candidates. In this research, a machine learning-based framework combining a Dense Neural Network (DNN) with a semiconductor classification model and the Extreme Gradient Boosting Algorithm (XGBA) is employed to accelerate ab initio predictions of spinel materials (AB 2 X 4 ). From a dataset of 4,160 unexplored spinels, the model successfully identifies 185 candidates exhibiting direct band gaps and thermal stability. The framework achieves a prediction accuracy of 97.8% while significantly reducing computational costs and shortening the typical spinel screening research cycle by approximately 3.4 years. These results demonstrate the efficiency and high-throughput capability of the proposed strategy, offering a practical approach for the rapid discovery of novel spinel materials with desirable electro-optical and thermal properties. Spinel ; Artificial neural network ; Ab initio ; Boosting (machine learning) ; Limiting ; Thermal ; Energy (signal processing) ; Stability (learning theory) Magnetic Properties and Synthesis of Ferrites ; Pigment Synthesis and Properties ; Heusler alloys: electronic and magnetic properties
Authors: Huan Xie | Y Yun-Chung Wu | Zhiqi Chen | Zhongyun Wu | Yu-Qing Zheng | Zhirong Liu | Jinrong Xu
Source: Journal of Chemical Education (article, 2026-02-20)
Field: Chemistry — Physical and Theoretical Chemistry

Intelligent Recognition of the Polarimeter’s Field of View and Automatic Optical Rotation Measurement of Sucrose Hydrolysis Using Machine Learning Artificial intelligence (AI) and automation techniques have promoted the rapid development of scientific fields such as chemistry, biomedicine, and materials science, where multiple variables and tremendous data collection are required in experiments. By incorporating machine learning (ML), an independently devised digital control system, and integrating custom-developed software into the sucrose hydrolysis experiment, intelligent identification of the polarimeter’s field of view and automatic data acquisition of the sucrose hydrolysis reaction are achieved. This innovation revolutionizes traditional experimental practices by replacing manual recognition and operation with automated processes, effectively addressing the inherent time-consuming and labor-intensive nature of conventional methods and thereby significantly improving experimental efficiency and accuracy. This novel, portable, and economical ML-based optical rotation measurement device will promote innovation in chemical experiment teaching models in the era of AI. Automation ; Field (mathematics) ; Rotation (mathematics) ; Software ; Identification (biology) ; Experimental data Various Chemistry Research Topics ; Molecular spectroscopy and chirality ; Catalysis and Oxidation Reactions
Authors: Charles Parton-Barr | Richard J. Mandle
Source: Open MIND (preprint, 2026-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Prediction of Dielectric Anisotropy in Nematic Liquid Crystals We curate a large-scale dataset of low frequency dielectric anisotropy values for low molecular weight liquid crystals. Using this dataset, we demonstrate that supervised machine-learning models can predict dielectric anisotropy with substantially improved accuracy (RMSE 2.6) compared to estimates obtained from the Maier-Meier relations using molecular properties from both the widely used semiempirical AM1 method (RMSE 9.7) and the modern r2scan-3c composite method (RMSE 11.2). Realising the potential of machine learning techniques for liquid crystalline materials requires carefully curated data to be accessible, and on this basis we propose a simple and standard template for reporting data. Anisotropy ; Dielectric ; Liquid crystal ; Simple (philosophy) ; Experimental data ; Basis (linear algebra) Liquid Crystal Research Advancements ; Material Dynamics and Properties ; Dielectric materials and actuators
Authors: Aaron Garrison | Heather J. Kulik
Source: Journal of Chemical Theory and Computation (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

System-Specific Reparameterization of Density Functionals with Machine Learning: Application to Spin-Splitting Energies of Transition Metal Complexes Accurate estimates of the spin-splitting energy (SSE) are essential for proper modeling of transition metal complex (TMC) catalysts and functional materials but are notoriously challenging to achieve. Over a large set of over 450 TMCs, we demonstrate that adding Hartree-Fock exchange (HFX) to semilocal density functionals (e.g., PBE or SCAN) in a system-specific fashion provides the flexibility to significantly reduce errors relative to reference wave function theory (i.e., DLPNO-CCSD(T)) values. Nevertheless, to do so requires that one knows the correct amount of HFX to add to the functional, which is system-dependent. We show that global reparameterizations, optimal ionization potential tuning, and machine learning (ML) models trained on atomic properties and molecular connectivity are all insufficient to reliably improve results relative to standard hybrid functionals. Instead, we utilize a Behler-Parrinello neural network trained on the electron density generated by a hybrid functional to predict the optimal amount of HFX to include, achieving errors on the unseen test set of <5% HFX, corresponding to 3-4 kcal/mol energetic errors. These results are competitive with the best single functionals on these data sets, as well as previous approaches that utilize system-specific functional recommendation, providing a path to a ML-derived self-tuning functional based on the electron density. This approach provides a practical and interpretable way to increase the accuracy of density functionals, which will enable more reliable and efficient screening of chemical space. Hybrid functional ; Density functional theory ; Flexibility (engineering) ; Set (abstract data type) ; Artificial neural network ; Function (biology) ; Path (computing) ; Test set ; Energy (signal processing) ; Orbital-free density functional theory Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Bin Guo | S.Y. Chen | Yuan Luo | Xuenuan Li | Bingbing Luo | Shilong Lin | Lu Lei | Yingxi Qin | Kaiyou Zhang | Li Jiang | Lei Liao | Aimiao Qin | Chenguo Hu
Source: Advanced Functional Materials (article, 2026-01-10)
Field: Materials Science — Biomaterials

Self‐Powered Flexible Dual‐Mode Humidity Sensor Enabled by Integrated TENG and Visualized Cellulose Nanocrystals ABSTRACT Cholesteric liquid crystals are 1D photonic crystals with a helical structure that can selectively reflect light. In this study, we successfully fabricated color cholesteric liquid crystal films using cellulose nanocrystals (CNC) and citric acid (CA) through co‐assembly. Due to the reversible swelling and dehydration of its chiral structure, the CA/CNC composite film exhibits reversible structural color responses between green and dark red at relative humidity (RH) of 43%–97%. When used as the friction material for TENG, the CA/CNC composite film exhibits excellent triboelectric performance, with a response time of approximately 22 ms, open‐circuit voltage, short‐circuit current, charge density and power density of 187.17 V, 7.75 µA, 86.06 nC, and 1.21 W/m 2 , respectively. CA/CNC‐TENG functions not only as a power supply but also incorporates humidity sensing capabilities. When establishing sensing relationships between electrical signals and visual signal RGB values with humidity respectively, its minimum detection limits are 2.4% and 4.9% RH, respectively. Utilizing a convolutional neural network deep learning model to analyze electrical signal and visual signal RGB value data, the average recognition accuracy rates were 95.90% and 97.91% respectively. The integrated CA/CNC‐TENG color‐visualized humidity sensor may significantly promote the application of TENG in electronic detection devices and environmental monitoring fields. Composite number ; Humidity ; SIGNAL (programming language) ; Structural coloration ; RGB color model ; Relative humidity ; Cellulose ; Triboelectric effect Advanced Cellulose Research Studies ; Liquid Crystal Research Advancements ; Photonic Crystals and Applications
Authors: Dario Baum | Arno Förster | Lucas Visscher
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Transfer learning of GW Bethe–Salpeter equation excitation energies A persistent challenge in machine learning for electronic-structure calculations is the sharp imbalance between abundant low-fidelity data like (time-dependent) density functional theory [(TD)DFT] results and the scarcity of high-fidelity data like many-body perturbation theory labels. We show that transfer learning provides an effective route to bridge this gap: graph neural networks pretrained on DFT and TDDFT properties can be finetuned with limited qsGW and qsGW-Bethe-Salpeter Equation (BSE) data to yield accurate predictions of quasiparticle and excitation energies. Assessing both full-model and readout-only finetuning across chemically diverse test sets, we find that pretraining improves accuracy, reduces reliance on costly qsGW data, and mitigates large predictive outliers even for molecules larger or chemically distinct from those seen during finetuning. Our results demonstrate that multi-fidelity transfer learning can substantially extend the reach of many-body-level predictions across chemical space. Time-dependent density functional theory ; Excitation ; Quasiparticle ; Transfer of learning ; Density functional theory ; Perturbation theory (quantum mechanics) ; Outlier ; Bridge (graph theory) ; Yield (engineering) Machine Learning in Materials Science ; Quantum many-body systems ; Physics of Superconductivity and Magnetism
Authors: ideal research review
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Atomistic and Machine Learning Studies of Solute Segregation in Metastable Grain Boundaries Grain boundary (GB) segregation plays a decisive role in determining the mechanical, thermal, electrical,and corrosion properties of polycrystalline materials. While equilibrium grain boundaries have beenextensively studied, metastable grain boundaries—formed during rapid solidification, severe plasticdeformation, or non-equilibrium processing—remain less understood. These metastable interfaces exhibitcomplex atomic structures and local energy landscapes that strongly influence solute segregation behavior.This study integrates atomistic simulations and machine learning (ML) techniques to investigate solutesegregation in metastable grain boundaries. Atomistic modeling based on molecular dynamics (MD)and Monte Carlo (MC) simulations is used to compute segregation energies, local structural motifs, anddiffusion characteristics. Subsequently, supervised ML models are trained on atomistic datasets to predictsegregation tendencies across diverse boundary types and chemical compositions. The results revealthat metastable boundaries possess heterogeneous segregation sites with wide energy distributions, oftenleading to enhanced segregation compared to equilibrium configurations. ML models demonstrate highpredictive accuracy and significantly reduce computational costs by identifying dominant structuraldescriptors influencing segregation energy. The study highlights the synergistic potential of atomisticmodeling and data-driven approaches for accelerating alloy design and tailoring grain boundary chemistry. Grain boundary ; Metastability ; Molecular dynamics ; Crystallite ; Monte Carlo method ; Alloy Microstructure and mechanical properties ; Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties
Authors: Ellida Saidi | Wei Huang | Yongwei Wang | Youliang Zhang
Source: Journal of Materials Engineering and Performance (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

Balancing Material Strength and Fracture Toughness in Metallic and Inorganic Materials: A Comprehensive Review Fracture toughness ; Microstructure ; Toughness ; Fracture (geology) ; Strengthening mechanisms of materials ; Plasticity ; Grain size ; Dislocation ; Corrosion Microstructure and mechanical properties ; Mechanical Failure Analysis and Simulation ; Material Selection and Properties
Authors: Tatyana Krivobokova | Razvan-Andrei Morariu | Gianluca Finocchio | Boris Maryasin
Source: Communications Chemistry (article, 2026-01-03)
Field: Materials Science — Materials Chemistry

Modelling and estimation of chemical reaction yields from high-throughput experiments Machine learning (ML) and artificial intelligence (AI) techniques are transforming the way chemical reactions are studied today. Datasets from high-throughput experimentation (HTE) are generated to better understand the reaction conditions crucial for outcomes such as yields and selectivities. However, it is often overlooked that datasets from such designed experiments possess a specific structure, which can be captured by a statistical model. Ignoring these data structures when applying ML/AI algorithms can result in misleading conclusions. In contrast, leveraging knowledge about the data-generating process yields reliable, interpretable, and comprehensive insights into reaction mechanisms. A particularly complex dataset is available for the Buchwald-Hartwig amination. Using this dataset, a statistical model for such HTE-generated chemical data is introduced, and a parameter estimation algorithm is developed. Based on the estimated model, new insights into the Buchwald-Hartwig amination are discussed. Our approach is applicable to a wide range of HTE-generated data for chemical reactions and beyond. Process (computing) ; Chemical process ; Range (aeronautics) ; Experimental data ; Chemical reaction ; Estimation theory ; Statistical model ; Estimation Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Aditya Kolakoti | Satish Geeri
Source: Next Materials (article, 2026-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic wood composite for electromagnetic shielding: Experimental study and machine learning predictions The use of electronic instruments is increasing globally, and at the same time, the demand for shielding the electromagnetic (EM) radiation from these instruments is also growing. In this study, magnetic wood-based polymer composites are proposed for EM wave absorption. For this purpose, Red oak wood was chosen as the filler, and composites (Specimens I, II, and III) were fabricated by hand lay-up technique and subjected to chemical treatment for 0, 24, and 48 h. The specimens are evaluated for EM properties of permittivity, permeability, tangent losses, and Reflection Loss (RL), and mechanical properties of tensile and flexural. In addition, regression and machine learning techniques are implemented to observe the influence of chemical treatment on EM and mechanical properties. The results reveal that, with increased duration of chemical interaction, the composite specimens' properties were enhanced. It is also observed that Specimen III (48 h of treatment) exhibits improvements of 35.85% in tensile stress, 53.84% in flexural stress, and RL by 43.75% compared to Specimen I. Similarly, regression and machine learning (ML) techniques show strong correlations between the chemical treatment and mechanical and EM properties, with high R² values. These results outline the wood composite as one of the feasible materials for EM shielding applications. • Red Oak wood as an EM wave absorption polymer composite material. • High chemical interaction enhances the EM properties. • ML techniques like SVR, XGBoost, and ANN, were utilized for corelation of properties. Ultimate tensile strength ; Composite number ; Flexural strength ; Electromagnetic shielding ; Universal testing machine ; Electromagnetic radiation ; Reflection (computer programming) Electromagnetic wave absorption materials ; Electromagnetic Fields and Biological Effects ; Magnetic and Electromagnetic Effects
Authors: Gutha Yuvaraja | Vijaya Yarramuthi | Venkata Subbaiah Munagapati | Sada Venkateswarlu | Diyun Chen | Sambasivam Sangaraju | Koteswararao Rudravarapu | Pratap Kotte | E.A. Lohith | N.V.V. Jyothi
Source: Coordination Chemistry Reviews (article, 2026-03-19)
Field: Materials Science — Surfaces, Coatings and Films

Bioinspired chitosan magnetic nanocomposites for advanced oil–water separation: bridging experiments with machine learning and molecular dynamics Bridging (networking) ; Chitosan ; Nanocomposite ; Molecular dynamics ; Magnetic nanoparticles ; Molecular machine Surface Modification and Superhydrophobicity ; Enhanced Oil Recovery Techniques ; Polymer composites and self-healing
Authors: Wataru Takahara | Mai Ogano | Yuichi Yamaguchi | Yosuke Harashima | Tomoaki Takayama | Shogo Takasuka | Akihiko Kudo | Mikiya Fujii
Source: ChemRxiv (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Data-Driven Optimization of the Solid-Solution Ratio of Stannitetype CuxAg2-xZnGeySn1-yS4 photocatalysts Using Machine-Learning with Chemically Similar Out-of-Plane Data Yield (engineering) ; Nanoparticle ; Work (physics) ; Degradation (telecommunications) Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Pigment Synthesis and Properties
Authors: Jiqun Zhang | Juhong Yu | Nianxiang Qiu | Yong Liu | Ye Song | Liang Zhang | Shiyu Du
Source: AI & Materials (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Advanced artificial intelligence algorithms and hardware acceleration techniques applied to material structure design The ability of AI-based algorithms to reflect the physical properties of training data and accurately predict the properties of undeveloped materials has made artificial intelligence (AI) algorithms an important tool in the domain of materials science. Material structure design, as a multi-step and multi-scientific task, involves many aspects from the determination of design objectives to material selection, preparation methods, sample testing and performance evaluation, etc. Traditional experiment-driven, theory-driven and algorithm-driven approaches have accumulated a large amount of textual material text data. With the development of technology, the data-driven approach of “Big Data+AI” has accelerated the development of material structure design, and in particular, deep learning (DL) as a branch of machine learning has become the fastest growing topic in materials science because of its powerful capabilities to analyze unstructured data and automatically identify features. In this paper, we focus on the common deep learning methods in materials research, and then review the material property prediction, material structure optimization, material discovery and information extraction from materials literature in the context of deep learning-based material structure design. At the same time, we introduce the hardware acceleration technologies based on deep learning. Finally, the summary and future directions of deep learning in future materials research are discussed. Deep learning ; Context (archaeology) ; Domain (mathematical analysis) ; Focus (optics) ; Property (philosophy) ; Data structure ; Acceleration Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Material Selection and Properties
Authors: LIAN Hongzhen | LU Chunyue
Source: SHILAP Revista de lepidopterología (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Data-driven multi-objective property model prediction in Al-Cu alloys Cast aluminum alloys are widely used in aerospace，automotive and other industries due to their excellent mechanical properties. However，traditional alloy design faces challenges such as vast composition space，high costs of trial-and-error experiments and difficulty in predicting the nonlinear relationship between composition and properties. This paper proposes a machine learning model that combines backpropagation neural networks，principal component analysis，and genetic algorithms for multi-objective property prediction of cast aluminum alloys. The model establishes the relationship between alloy composition and properties through the nonlinear mapping of backpropagation neural networks，reduces dimensionality via principal component analysis，and optimizes network parameters using genetic algorithms-thereby improving prediction accuracy and training efficiency. The results show that the optimized model has mean squared error of 36.28，correlation coefficient of 0.91，and mean absolute error of 2.44. In the experimental verification of ultimate strength，yield strength，and elongation after fracture，the error between experimental values and predicted values is controlled within the range of ±5%. This high prediction accuracy demonstrates the efficiency and reliability of the proposed model. Backpropagation ; Artificial neural network ; Nonlinear system ; Mean squared error ; Component (thermodynamics) ; Genetic algorithm ; Principal component analysis ; Property (philosophy) Machine Learning in Materials Science ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: Guanming Chen | Maximilian Fleck | Thijs Stuyver
Source: ChemRxiv (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Leveraging Hidden-Space Representations Effectively in Bayesian Optimization for Experiment Design through Dimension-Aware Hyperpriors Hidden-space molecular representations derived from pretrained graph neural networks, Transformer models, and large language models offer an appealing alternative to conventional encodings for Bayesian optimization (BO) in chemical experiment design. However, their performance benefits in BO remain poorly understood and difficult to assess in practice, and, when deployed within current chemical BO frameworks, they often do not yield clear advantages over traditional representations. Here, we show that this behavior arises from a previously underappreciated interaction between representation dimensionality and Gaussian process kernel hyperpriors, rather than from limitations of the representations themselves. We demonstrate that different chemical representations induce substantial variations in search-space dimensionality, which, when paired with fixed or mismatched lengthscale hyperpriors, lead to flat marginal likelihood landscapes and severely degrade surrogate learning and acquisition optimization. To address this issue, we introduce a dimension-aware adaptive hyperprior, tailored to reaction optimization, whose characteristic lengthscale scales with the square root of the search space dimensionality. Benchmarking across multiple reaction optimization datasets and molecular representations shows that this adaptive hyperprior consistently restores and amplifies the performance advantages of hidden-space featurizations over traditional representations, such as Mordred or one-hot encoding. Our results identify hyperprior calibration as a critical requirement for fair evaluation and effective deployment of modern molecular representations in chemical BO. Bayesian optimization ; Gaussian process ; Curse of dimensionality ; Chemical space ; Bayesian probability ; Design of experiments ; Probabilistic logic ; Boosting (machine learning) ; Representation (politics) ; Process (computing) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Multi-Objective Optimization Algorithms