jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrea Ruiz-Escudero | Zuriñe Serna-Burgos | Sonia Arrasate | Humberto González‐Díaz
Source: Scientific Reports (article, 2025-08-14)
Field: Materials Science — Materials Chemistry

PTML models of self assembled ligand free nanoparticle catalysts for cross coupling reactions Cross-coupling reactions have transformed the synthesis of complex and valuable compounds used in pharmaceuticals, materials science, and chemical synthesis. Transition metal nanoparticle (NP) catalysts represent a promising strategy within this field, but their behavior and efficiency continue under investigation. The use of computational models enables rapid design, optimization, and understanding of the behavior of these molecules, thereby reducing the costs and time. In this study, the perturbation theory and machine learning (PTML) approach was used to construct a predictive model for estimating yield after multiple reuses (up to 10) of self-assembled Au- or glass-supported transition metal NP catalysts under ligand-free conditions and diverse cross-coupling reactions. The studied reactions include Suzuki-Miyaura, Kumada, Negishi, Buchwald-Hartwig, C(sp<sup>2</sup>)- and C(sp<sup>3</sup>)-H functionalization, and double carbonylation. A comprehensive dataset was built, and multiple linear regression (MLR) and artificial neural network (ANN) models were built and compared. The best MLR model achieved MAE = 7.4% and RMSE = 12.2% on the test set, demonstrating robust performance for yield prediction. Among the ANN models, MLP (9:9-20-9-1:1) and RBF (9:9-70-1:1) regression models showed similar results, with test MAE of 5.9% and 5.8% respectively, and both showed test RMSE of 9.8%. MLP (9:9-20-18-1:1) classification model showed high precision (97.0%) and recall (93.8%), effectively distinguishing high- and low-yielding reactions. These results highlight the potential of PTML-based models to guide catalyst and reaction condition selection, optimize catalytic systems, and minimize synthesis costs and environmental impact. Ligand (biochemistry) ; Nanoparticle ; Coupling (piping) ; Catalysis ; Chemistry ; Computer science ; Nanotechnology ; Materials science ; Biochemistry ; Receptor Polyoxometalates: Synthesis and Applications ; Mesoporous Materials and Catalysis ; Catalysis and Oxidation Reactions
Authors: Nazym Alimbekova | Hari Mohan Rai | Tursinbay Turymbetov | Ainur Zhumadillayeva
Source: SHILAP Revista de lepidopterología (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

HAttFFNN: Hybridized attention mechanism-based feedforward neural network deep learning model for the plastic material classification of three stage materials on spectroscopic data Deep learning ; Artificial neural network ; Pattern recognition (psychology) ; Stage (stratigraphy) ; Feed forward ; Feedforward neural network ; Deep neural networks Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Gaussian Processes and Bayesian Inference
Authors: S. Chakrabarty | Kamal Choudhary | Daniel Wines | Youjia Li | Vishu Gupta | Muhammed Nur Talha Kilic | Alok Choudhary | Ankit Agrawal
Source: The Journal of Physical Chemistry C (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Predicting Lattice Parameters from Atomic-Scale Images of Two Dimensional Materials Using Deep Learning Determining lattice parameters in two-dimensional (2D) materials is essential for materials characterization and discovery. In this work, we propose a deep-learning-driven pipeline that addresses the regression task of estimating the lattice constants a and b and the angle γ directly from 2D images using computer vision. We evaluate our approach on three different 2D material data sets: JARVIS-2D (JV2D) and Computational 2D Materials Database (C2DB), and a newly created data set derived from the Alexandria database. Multiple architectures are compared, including DenseNet121, Vision Transformers (ViT-L/14) paired with Multi Layer Perceptron (MLP) heads, and GoogleNet. DenseNet121 achieves accurate performance, with mean absolute errors as low as 0.18 Å for the Alexandria-based data set and 0.17 Å for C2DB and 0.59 Å for JV2D, as well as up to 96% accuracy in classifying Bravais lattice types for the Alexandria-based data set. Bravais lattice ; Lattice (music) ; Deep learning ; Pattern recognition (psychology) ; Perceptron ; Lattice constant ; Artificial neural network Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications
Authors: Matthew Witman | Brian Davis | Vitalie Stavila | Terry A. Johnson
Source: ChemRxiv (preprint, 2025-03-25)
Field: Materials Science — Materials Chemistry

Machine learning-guided materials and system co-design for high-pressure hydrogen compression Cost effective and reliable hydrogen compression remains a challenging barrier in the wide-spread adoption of hydrogen as an energy carrier. The prevailing technology of mechanical compression suffers from several drawbacks, some of which can be addressed by non-mechanical compression strategies (e.g., electrochemical or metal hydride-based thermal compression). Thermally driven metal hydride compression strategies typically rely on multi-stage metal hydride-based compressors; however, discovering or optimizing low-stability metal hydrides that can pressurize hydrogen upwards of 1000 bar is difficult, both with respect to computational predictions and experimental validation. Here we (1) demonstrate that simple machine learning-derived design rules can inform rational design of alloying strategies yielding low-stability hydrides, (2) validate their experimental pressure-composition-temperature (PCT) isotherms up to 875 bar, and (3) utilize a dynamic systems-level model of a metal hydride compressor design to evaluate their performance under realistic operating conditions. Importantly, this analysis yields predicted operational efficiencies of both 2-stage (90-875 bar) and 3-stage (20-875 bar) metal hydride compressors to enable further evaluation of this technology and its techno-economic outlook. Compression (physics) ; High pressure ; Materials science ; Computer science ; Artificial intelligence ; Mechanical engineering ; Engineering ; Composite material ; Engineering physics Machine Learning in Materials Science ; Mechanical Failure Analysis and Simulation ; Advanced ceramic materials synthesis
Authors: Pundarik Mahata | Prasenjit Ghosh | R. Mukhopadhyay | Ashok Kumar Pandey
Source:  (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Impact of Molecular Footprint on Machine Learning Model for Optimising the Polymer Blend Formulation Abstract Compound formulation selection is a very crucial, complex and tedious work in polymer product development. The researchers utilize their knowledge and past data, look at the requirement and then prepare a formulation. Then the same formulation has to be tested and different parameters will be measured against the requirement. Based on the deviation, the formulation will be fine-tuned further. These steps will be repeated three to four times to achieve the acceptable level of desired properties. In general, the DOE approach is very popular to arrive at the 1st set of experiments. However, the complexity of DOE increases with the increase of parameters. Even with a selected few parameters, the two-level DOE with a 1/8th fractional factorial can lead to more than 10 sets of experiments. Each of these mixing along with subsequent experiments is very resource-consuming. Thus, it is always a target to reduce the number of tests or adopt a different method. In answer to that, ML has been gaining interest in the property prediction of polymer material. However, for predicting the properties of polymer blends, ML is very new. Here we have done 160 experiments with the same process parameter and collected data to build ML mode. Also in this study, we have used polymer informatics tools and added different molecular footprints (MF) in the descriptor list. We have compared different types of descriptors and shown the importance score of MF. The descriptor list was reduced accordingly. Different ML models were tested and Gaussian Process Regression (GPR) has been finalized. This new ML model from the reduced descriptor list along with other techniques like Active Learning can be used to predict the property of new formulation, search for an optimized formulation from the given ranges, and thus reduce the effort of sample selection. Design of experiments ; Process (computing) ; Factorial experiment ; Set (abstract data type) ; Kriging ; Gaussian process ; Selection (genetic algorithm) ; Mixing (physics) ; Product (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Jian Chen | Fei Zhou | Shumin Han | X. W. Chen | Mingqing Liao | Hongli Wang | Yong Cao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning accelerate the design of low cost and high strength Cu-Zn alloys based on the small sample dataset Tin ; Alloy ; Consistency (knowledge bases) ; Electrical resistivity and conductivity ; Sizing ; Aluminium ; Stacking ; Sample (material) Metallurgy and Material Science ; Metallurgical and Alloy Processes ; Electrodeposition and Electroless Coatings
Authors: Dmytro Tymoshchuk | Oleh Yasniy
Source: Scientific journal of the Ternopil national technical university (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Information technology for predicting the hysteresis behavior of shape memory alloys based on a stacking ensemble machine learning model Shape Memory Alloys are characterized by a nonlinear hysteretic behavior on the stress–strain (σ–ε) diagram, where the loop area determines the amount of energy dissipated per cycle. In this work, an ensemble Stacking machine learning model was developed to predict the hysteresis behavior of SMAs under cyclic loading at different frequencies (0.5, 1, 3, and 5 Hz). The model was constructed using experimental data from 100–250 loading cycles. Random Forest, Gradient Boosting, Extra Trees, k-Nearest Neighbors (kNN), Support Vector Regression (SVR), and Multilayer Perceptron (MLP) were employed as base algorithms. The ElasticNet model was selected as the meta-learner and tuned using GridSearchCV with GroupKFold validation. This approach ensured the combination of ensemble stability with adaptive selection of the most informative predictions from the base models. The obtained results showed a high accuracy in reproducing the stress–strain relationship: R2 &gt; 0.995, MSE &lt; 0.0007, MAE &lt; 0.02, and MAPE &lt; 1.3 % on the test data. Validation on independent cycles 251 and 300 confirmed the model’s generalization ability, achieving R2 &gt; 0.974, MSE &lt; 0.007, MAE &lt; 0.06, and MAPE &lt; 4.8 %. The interpretability of the model was provided by the SHAP method, which quantitatively determines the contribution of each input feature to the prediction. It was found that Stress is the dominant factor influencing the prediction, while UpDown defines the loading–unloading phase, and Cycle reflects the accumulation of cyclic effects. The developed ensemble Stacking model is an integral component of an information technology framework for predicting the hysteresis behavior of shape memory alloys using machine learning methods. The proposed approach provides not only high prediction accuracy but also a physically grounded interpretability of the results. Interpretability ; Hysteresis ; Multilayer perceptron ; Support vector machine ; Ensemble learning ; Perceptron ; Ensemble forecasting ; Generalization ; Feature selection ; Stacking Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; High Temperature Alloys and Creep
Authors: Freederia AI Researcher
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-14)
Field: Chemistry — Inorganic Chemistry

Catalytic Cracking of Long-Chain Alkanes via Optimized Zeolite Y Tuning through Molecular Dynamics Simulations and Machine Learning-Driven Compositional Optimization **Abstract:** The catalytic cracking of long-chain alkanes into valuable short-chain olefins is a cornerstone of petrochemical production. Traditional Zeolite Y catalysts, while widely employed, suffer from limitations in product selectivity and coke formation. This research proposes a novel synergistic approach combining molecular dynamics (MD) simulations with machine learning (ML) to optimize the composition and structure of Zeolite Y, specifically targeting improved olefin yields and reduced coke generation in the cracking of n-dodecane. The methodology involves *in silico* MD simulations to probe the impact of different alkali metal cation dopants and Si/Al ratio variations on the catalytic activity and stability of Zeolite Y, followed by the training of a Gaussian Process Regression (GPR) model to predict catalyst performance based on compositional parameters. The complete optimization workflow, including a hyper-scoring system for performance evaluation, demonstrates a pathway towards a 10x improvement in olefin selectivity and catalyst lifespan compared to conventional Zeolite Y catalysts. Zeolite ; Olefin fiber ; Fluid catalytic cracking ; Coke ; Catalysis ; Cracking ; Selectivity Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Aman Srivastava | Rabesh Kumar Singh | Ankit Sahai | Nandini Srivastava
Source: Research Journal of Chemistry and Environment (article, 2025-11-30)
Field: Materials Science — Materials Chemistry

Exploring Artificial Intelligence in Organic Synthesis The field of chemistry has seen significant growth with the integration of Artificial Intelligence (AI). This has provided chemists with advanced techniques and tools that make the interpretation of chemical problems much easier and faster. In the art of computerized chemistry, scientific knowledge is seamlessly translated into digital form without any need for human interference. Dealing with chemistry conundrums inspires us to explore innovative AI techniques that can be effectively utilized to tackle complex chemical issues requiring specialized knowledge. An innovative program utilizes chemical expertise to address challenges in the field of chemistry, specifically focusing on structure, design, properties and synthesis. Utilizing expert systems, artificial neural networks and machine learning, our computer systems are equipped to handle vast amounts of chemical information. This review aims to explore the application of AI in reaction prediction and chemical synthesis. Field (mathematics) ; Interpretation (philosophy) ; Applications of artificial intelligence ; Expert system ; Artificial neural network Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Jialu Wu (11714967) | Yiheng Zhu (5818622) | Xiaorui Wang (237883) | Yitong Li (2690047) | Mingze Yin (21600557) | Tianyue Wang (720273) | Yuqiang Han (12209191) | Yu Kang (40770) | Yafeng Deng (11730611) | Jian Wu (41301) | Chang-Yu Hsieh (4738329) | Tingjun Hou (61462)
Source: Figshare (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

HiCLR: Knowledge-Induced Hierarchical ContrastiveLearning with Retrosynthesis Prediction Yields a Reaction FoundationModel Reaction representation learning is of paramount importance for adopting deep-learning-based chemistry modeling to solve real-world tasks such as synthesis planning. Most prevailing models are prestrained by self-supervised objectives that rely solely on the chemical structure information. Since structurally similar reactions could possess entirely distinct properties (e.g., reaction yields) and the synthesis-related tasks are highly heterogeneous, there are inherent limitations in constructing a foundational reaction model within the existing approaches. To tackle this limitation, we propose HiCLR, a knowledge-induced hierarchical contrastive learning framework for chemical reactions, by introducing relational inductive bias to forge chemically meaningful and generally applicable reaction fingerprints. Critically, the pretraining scheme combining both retrosynthesis prediction and contrastive loss enables HiCLR to tackle generation-based and understanding-based tasks simultaneously. Comprehensive experiments demonstrate that HiCLR successfully organizes the reaction space into hierarchical global semantic clusters, aligned well with prior knowledge. Consequently, HiCLR is the first foundation model that can be broadly applied to various synthesis-related tasks, and it achieves state-of-the-art performance in reaction classification, reaction condition recommendation, reaction yield prediction, synthesis planning, and even molecular property prediction. HiCLR demonstrates clear benefits in incorporating domain knowledge to guide the learning of neural networks, expediting AI-driven advancements in chemistry. Representation (politics) ; Retrosynthetic analysis ; Property (philosophy) ; Generalization ; Foundation (evidence) ; Expediting ; Domain (mathematical analysis) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Advanced Graph Neural Networks
Authors: M. Giridharadhayalan | T. Ramkumar | M. Selvakumar | P. Narayanasamy
Source: Transactions of the Indian Institute of Metals (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Fatigue Life Prediction of Ti–5Al–2.5Sn Alloy Reinforced Tungsten Composites Using Machine Learning Materials science ; Tungsten ; Composite material ; Alloy ; Titanium alloy ; Metallurgy Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Advanced materials and composites
Authors: Jialu Wu | Yiheng Zhu | Xiaorui Wang | Yitong Li | Mingze Yin | Tianyue Wang | Yuqiang Han | Yu Kang | Yafeng Deng | Jian Wu | Chang‐Yu Hsieh | Tingjun Hou
Source: JACS Au (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

HiCLR: Knowledge-Induced Hierarchical Contrastive Learning with Retrosynthesis Prediction Yields a Reaction Foundation Model Reaction representation learning is of paramount importance for adopting deep-learning-based chemistry modeling to solve real-world tasks such as synthesis planning. Most prevailing models are prestrained by self-supervised objectives that rely solely on the chemical structure information. Since structurally similar reactions could possess entirely distinct properties (e.g., reaction yields) and the synthesis-related tasks are highly heterogeneous, there are inherent limitations in constructing a foundational reaction model within the existing approaches. To tackle this limitation, we propose HiCLR, a knowledge-induced hierarchical contrastive learning framework for chemical reactions, by introducing relational inductive bias to forge chemically meaningful and generally applicable reaction fingerprints. Critically, the pretraining scheme combining both retrosynthesis prediction and contrastive loss enables HiCLR to tackle generation-based and understanding-based tasks simultaneously. Comprehensive experiments demonstrate that HiCLR successfully organizes the reaction space into hierarchical global semantic clusters, aligned well with prior knowledge. Consequently, HiCLR is the first foundation model that can be broadly applied to various synthesis-related tasks, and it achieves state-of-the-art performance in reaction classification, reaction condition recommendation, reaction yield prediction, synthesis planning, and even molecular property prediction. HiCLR demonstrates clear benefits in incorporating domain knowledge to guide the learning of neural networks, expediting AI-driven advancements in chemistry. Foundation (evidence) ; Retrosynthetic analysis ; Artificial intelligence ; Computer science ; Contrastive analysis ; Natural language processing ; Engineering ; Linguistics ; Geography ; Archaeology ; Chemistry ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Jia-kuan Ren | Siwei Wu | Weina Zhang | Lijuan Zhu | Zhihong Li | Chao Zhang | Xuewei Xing | Chun Feng
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of strength and toughness for high-strength titanium alloys used for oil and gas drill by feature engineering directed machine learning algorithms Titanium alloy ; Toughness ; Feature selection ; Boosting (machine learning) ; Drill ; Gradient boosting ; Stability (learning theory) ; Feature (linguistics) Titanium Alloys Microstructure and Properties ; Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability
Authors: Sagheer Ahmad | Muhammad Ali Khan | Ghada Eid | Muhammad Shehryar Khan | Aqsa | Muhammad Mahboob Ahmad | Naeem Akhtar | Farhan Zafar | Muhammad Imran Khan
Source: Electrochimica Acta (article, 2025-11-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-assisted synthesis of N-S functionalized NiO/CoO nanocomposite for enhanced supercapacitor performance Supercapacitor ; Nanocomposite ; Capacitance ; Nanoporous ; Electrochemistry ; Specific surface area ; Energy storage ; Thiourea Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Nanoporous metals and alloys
Authors: Daniil N. Chistikov | Vladimir E. Bochenkov | D. A. Firsov | Vadim V. Korolev | Anastasia V. Bochenkova
Source: Lomonosov chemistry journal (article, 2025-05-10)
Field: Materials Science — Materials Chemistry

APPLICATION OF NEURAL NETWORKS FOR INVESTIGATING POTENTIAL ENERGY SURFACES OF ELECTRONICALLY EXCITED STATES AND INTERNAL CONVERSION MECHANISMS OF ORGANIC MOLECULES Based on non-empirical data obtained using a high-level multi-configuration quantum chemical method, neural networks with the architectures of a multilayer perceptron and an E(3)-equivariant graph network were constructed and trained to predict the energies of the ground and the first two electronically excited states of the methylenimmonium cation, CH2NH2+. It is shown that the E(3)-equivariant graph neural network architecture demonstrates higher accuracy. Using the trained network, a segment of the cation’s potential energy surfaces near the region of the conical intersection between the first excited and the ground states was investigated; this region plays an important role in the mechanism of internal conversion and photoisomerization reactions. It is demonstrated that the neural network accurately reproduces the topography of the potential energy surfaces of the two electronic states in the region of their conical intersection. Excited state ; Conical intersection ; Artificial neural network ; Potential energy ; Conical surface ; Internal conversion ; Quantum ; Photoisomerization Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Abdalla Ahmed | Seong Su Kim
Source: Carbon (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Machine learning-optimized synthesis of freestanding conductive graphitized sheets from polybenzoxazine via self-peeling laser-induced processing Graphene ; Porosity ; Electrical conductor ; Carbon nanotube ; Thermal conductivity ; Nanocomposite ; Bayesian optimization ; Sheet resistance ; Carbon fibers Thermal properties of materials ; Graphene research and applications ; Carbon Nanotubes in Composites