jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rahul Sheshanarayana (22317717) | Fengqi You (1360893)
Source: Figshare (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Rethinking Retrosynthesis: Curriculum Learning ReshapesTransformer-Based Small-Molecule Reaction Prediction Retrosynthesis prediction remains a central challenge in computational chemistry, particularly when models must generalize to rare or structurally complex reactions. We present a curriculum learning (CL) framework that reshapes model training by systematically controlling reaction difficulty during learning, directly addressing the challenge of chemical generalization. In contrast to conventional generative approaches that treat all training reactions uniformly, our method introduces reactions in a chemically informed progression, gradually exposing the model to increasingly complex transformations based on synthetic accessibility, ring complexity, and molecular size. This difficulty-aware pacing allows the model to better capture reaction conditionality, preserve chemical plausibility, and avoid failure modes commonly observed in rare or underrepresented transformations. Applied across three transformer-based architecturesChemBERTa + DistilGPT2, ReactionT5v2, and BARTthe framework yields substantial performance gains. Notably, the largest improvements are observed in the BART model, which lacks any chemical domain pretraining: CL improves its top-1 accuracy from 27.0% to 75.9% (+48.9%). The remainder of our evaluations use ChemBERTa + DistilGPT2 as a representative pretrained model. In low-data regimes with only 50% of the training data, CL increases top-1 accuracy from 16.9% to 46.6% (+29.7%). Under scaffold-based splits, CL improves top-1 accuracy by up to 29%, and in structurally dissimilar settings (Tanimoto similarity <0.4), CL boosts top-1 accuracy from 18.2% to 69.4% (+51.2%), demonstrating strong robustness to distributional shifts. These improvements are achieved without auxiliary labels, templates, or reaction class supervision. Looking forward, this CL framework may aid retrosynthetic route planning for pharmaceutical intermediates, catalysts, polymers, and functional materials. Robustness (evolution) ; Curriculum ; Retrosynthetic analysis ; Generative grammar ; Training set ; Domain (mathematical analysis) ; Similarity (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Janakarajan, Nikita; id_orcid0000-0001-7886-8385 | Espejo Morales , Irina | Alberts , Marvin | Giovannini , Andrea | Manica , Matteo | Foncubierta-Rodríguez , Antonio
Source: Repository for Publications and Research Data (ETH Zurich) (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Unified lookup tables: training foundation models on encoded data Transformers have proven successful in a range of sequence modelling tasks. However, these models have significant limitations: they are inherently data-greedy, and suffer from the risk of training data leakage. These limitations prevent their broad application in various domains. While the advent of foundation models (FMs) addresses the data-greedy nature of Transformers, the risk of exposing training data remains; it has been demonstrated that excerpts of the training data can be obtained by prompt engineering on an FM. To simultaneously address these limitations, we propose unified lookup tables (ULTs), a data preprocessing step that enables building and fine-tuning FMs on encoded data. ULTs enable the reuse of a trained model on new datasets without exposing any unencoded training data. The method leverages data compression methods as efficient modality tokenizers, and a common representation vocabulary to facilitate fine-tuning on encoded data. We theoretically support our claims through numerical estimations of the likelihood of reverse engineering the data encoding and practically through empirical evaluation on domains that can benefit from ULTs. Specifically, we evaluate the impact of using ULTs as a preprocessing step before training both decoder-only and encoder–decoder language models on text, images, and molecules. We demonstrate that the encoding step does not negatively affect model training and leads to an average relative increase of ∼16% on a collection of text metrics, while producing close to competitive results on image classification and chemical reaction prediction tasks. Preprocessor ; Vocabulary ; Encoding (memory) ; Data pre-processing ; External Data Representation ; Transformer ; Data collection ; Data type Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Yu Shee (20747595) | Anton Morgunov (17870663) | Haote Li (16876244) | Victor S. Batista (1342758)
Source: Figshare (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

DirectMultiStep:Direct Route Generation for MultistepRetrosynthesis Traditional computer-aided synthesis planning (CASP) methods rely on iterative single-step predictions, leading to exponential search space growth that limits efficiency and scalability. We introduce a series of transformer-based models that leverage a mixture of experts approach to directly generate multistep synthetic routes as a single string, conditionally predicting each transformation based on all preceding ones. Our DMS Explorer XL model, which requires only target compounds as input, outperforms state-of-the-art methods on the PaRoutes dataset with 1.9x and 3.1x improvements in Top-1 accuracy on the n<sub>1</sub> and n<sub>5</sub> test sets, respectively. Providing additional information, such as the desired number of steps and starting materials, enables both a reduction in model size and an increase in accuracy, highlighting the benefits of incorporating more constraints into the prediction process. The top-performing DMS-Flex (Duo) model scores 25–50% higher on Top-1 and Top-10 accuracies for both n<sub>1</sub> and n<sub>5</sub> sets. Additionally, our models successfully predict routes for the FDA-approved drugs not included in the training data, demonstrating strong generalization capabilities. While the limited diversity of the training set may affect performance on less common reaction types, our multistep-first approach presents a promising direction toward fully automated retrosynthetic planning. Leverage (statistics) ; Generalization ; Retrosynthetic analysis ; Set (abstract data type) ; Training set ; Reduction (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: S. K. Niranjan | M L Raviprakash | B. V. Balaji Prabhu | V N Vinaykumar
Source: International Journal of Environmental Sciences (article, 2025-07-02)
Field: Chemistry — Inorganic Chemistry

Coconut Disease Prediction Using Neural Attention Mechanism With Adamax Optimizer Based Convolutional Neural Network Accurate coconut disease prediction systems become most important in developing effective disease mitigation strategies, encouraging cost-effective crop protection for small-scale farming. The farmers utilize only the conventional methods like laboratory view and experts for the identification of diseases in coconut. However, these techniques are inadequate for an effective and timely detection of diseases in the coconut. Hence, this research proposes the Neural Attention Mechanism with AdaMax Optimizer based Convolutional Neural Network (NAM-AMO-CNN) approach for prediction of coconut disease. This article is comprises of different levels: Initially, the dataset is collected from the real-world of various images of coconut diseases. Next, pre-processing is conducted through data augmentation and noise removal through Quaternion Non-local Means Denoising Algorithm (QNLM) approach. Then, segmentation is performed through DeepLabV3+ and Feature extraction is employed by Residual DenseNet. Finally, prediction is performed through NAM-AMO-CNN approach, which allows the classifier to concentrate on critical disease-infected regions, leading to improved accuracy. Experimental results demonstrates that the proposed NAM-AMO-CNN approach attains the superior accuracy of 98.43% as compared to the existing methods like Artificial Intelligence Enabled Coconut Tree Disease Detection and Classification (AIE-CTDDC) and Enhanced Visual Geometry Group (EVGG16). Convolutional neural network ; Mechanism (biology) ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) Coconut Research and Applications ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Simonyuk, V. A.
Source: Electronic Library of Belarusian State Technological University (Belarusian State Technological University) (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Modern tendences in organic synthesis Modern organic chemistry has a huge amount of experimental and theoretical material, and the level of development of organic synthesis can serve as a measure of scientific and technological progress. The goal of organic synthesis is to obtain substances with valuable physical, chemical and biological properties or to test theoretical predictions. Modern organic synthesis is multifaceted and allows obtaining virtually any organic molecules. Organic synthesis ; Organic chemicals ; Chemical synthesis ; Fine chemical ; Measure (data warehouse) History and advancements in chemistry ; Chemical Reactions and Mechanisms ; Chemical Reactions and Isotopes
Authors: Xin Wang | D-Y Liu | Yunfei Cheng
Source: Molecules (review, 2025-10-02)
Field: Chemistry — Organic Chemistry

Advances in the Total Synthesis of Angular Triquinane-Type Natural Products This review provides a comprehensive summary of advances in synthetic strategies for structurally complex angular triquinane natural products, with a particular emphasis on terpenoids and alkaloids over the past two decades. The formidable challenge inherent in these frameworks lies in the stereoselective construction of the congested, angularly fused triquinane core bearing one or more quaternary stereocenters. We systematically categorize and critically evaluate the key methodologies employed to construct this central motif, highlighting four major strategic approaches: (i) The Pauson-Khand reaction; (ii) tandem or cascade cyclization processes; (iii) skeletal reorganization strategies; and (iv) other innovative methods, including ring-expansion, radical cyclizations, and photochemical pathways. Total synthesis ; Natural (archaeology) ; Core (optical fiber) ; Tandem ; Cascade ; Stereoselectivity Synthetic Organic Chemistry Methods ; Chemical synthesis and alkaloids ; Marine Sponges and Natural Products
Authors: Liang Cheng | Zhenzhi Tan | Zongbin Jia | Qifeng Lin | Qi Yang | Sanzhong Luo
Source: Nature Synthesis (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Heuristic data-driven approach for synergistic cobalt(IV)–enamine catalysis Workflow ; Intuition ; Scalability ; Heuristic ; Cluster analysis ; Scope (computer science) ; Identification (biology) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Yu Huang | Min Luo | Xinyu Huang | Miaowen Wen | Chan‐Byoung Chae
Source: IEEE Wireless Communications Letters (article, 2025-03-27)
Field: Materials Science — Materials Chemistry

Demystifying Molecular Data-Driven Detection With Explainable Artificial Intelligence Molecular communication (MC) is a bio-inspired paradigm for information exchange that leverages the properties of messenger molecules for data transmission. Recognized as a promising physical-layer technique for the Internet of Bio-Nano Things within biological entities, MC facilitates intricate collaboration and networking among micro-scale devices. Data-driven detectors are favored in MC receivers due to their complex and dynamic channel characteristics. The messages of the MC systems are vital, while the recovery process via the data-driven detectors mostly exhibits an opaque nature. To address this transparency issue, this letter uses an artificial intelligence tools, SHapley Additive exPlanations (SHAP), to explain the basic principles of data-driven detectors in MC from a systematic perspective. Through this approach, important feature points of the received signals are extracted, which further enhances the detection performance of MC with a reduced need for signal samples, thereby substantiating the role of interpretability in improving the functional capabilities of MC systems. Computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Vinoth Kumar Jayaraman | Hemavathi Sugumar
Source: Advanced Sustainable Systems (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Revolutionizing Battery Innovation through Artificial Intelligence: The Next Frontier ABSTRACT The global thirst for sustainable energy storage systems has accelerated the limitations of conventional battery research and development methods. The complex nature of battery chemistries of cathode, anode, electrolytes and their performance metrics demands quicker, cleverer, and more extrapolative methodologies. In this aspect, Artificial Intelligence (AI) has arisen as a metamorphic tool, enabling the fast‐tracked invention, and design of battery materials and systems. This perspective article explores the growing incorporation of AI into battery research and development, and its applications in materials design, performance prognostication, troubleshooting, and process control in fabrication. Moreover, this work inspects contemporary innovations, current challenges, and the potential for AI to revolutionize the entire battery eco‐system, from atom‐level simulations to industrial‐scale production. Battery (electricity) ; Process (computing) ; Work (physics) ; Perspective (graphical) ; Frontier ; Sustainable energy ; Control (management) Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Jonathan M. Mauß | Klara S. Kley | Rohini Khobragade | N. V. Tran | Jacopo De Bellis | Ferdi Schüth | Matthias Scheffler | Lucas Foppa
Source: ChemRxiv (preprint, 2025-04-02)
Field: Materials Science — Materials Chemistry

Modelling the Time-Dependent Reactivity of Catalysts by Experiments and Artificial Intelligence The description of heterogeneous catalysis is challenged by the intricacy of numerous multi-scale processes that govern the performance of catalyst materials. The chemical environment of the catalytic process and the kinetics of structural changes create configurations of typically unknown local geometries and chemistry. These may result in significant changes in activity or selectivity within minutes, hours, or longer, during the so-called induction period. Here, we use experimental data together with a focused artificial-intelligence (AI) approach based on subgroup discovery and symbolic regression to model the evolution of the catalyst reactivity with time on stream. We consider palladium-based alloys synthesized mechanochemically and applied in the selective hydrogenation of concentrated acetylene streams resulting from a hypothetical electric plasma-assisted methane-to-ethylene process. Our AI approach starts with the identification of descriptions of materials and reaction conditions relevant to acetylene conversion. Then, a model for time-on-stream-dependent selectivity focused on situations associated to noticeable acetylene conversion is obtained by the sure-independence-screening-and-sparsifying-operator (SISSO) approach. Our AI approach identifies relationships between the measured catalyst reactivity and only few, key parameters, from 21 measured and calculated bulk, surface, and mesoscopic materials' properties and reaction parameters offered as candidate descriptive parameters. These identified parameters highlight the crucial influence of surface and subsurface carbon and hydrogen on the selectivity towards ethylene formation. Guided by the AI models, new, highly selective bimetallic and trimetallic systems are designed and tested experimentally. Reactivity (psychology) ; Catalysis ; Computer science ; Artificial intelligence ; Psychology ; Chemistry ; Organic chemistry ; Medicine Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Krishna Kanth Kodanganti | Divya Kodanganti
Source:  (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Bridging Artificial Intelligence and Quantum Computing: A Data-Driven Framework for Molecular Simulations in Drug Discovery The application of quantum computing to drug discovery can enable an enhanced level of accuracy in estimating molecular properties and conducting chemical simulations. However, there is a considerable distance between real drug-like molecular data and their accessibility for quantum computational analysis. Here, we present an automated, scalable framework that can bridge this gap by extracting drug-like molecules from publicly available biomedical databases, optimizing their molecular geometries, and producing quantum-ready Hamiltonians using widely accepted techniques in quantum chemistry. This method uses the ChEMBL v28 database to retrieve molecules with zero Rule-of-5 violations, maintaining pharmaceutical applicability. These molecules are then prepared using RDKit to convert SMILES notations into chemically valid 3D geometries, enabling force-field-based optimization and downstream quantum analysis. Once these geometries are ready, we use the quantum chemistry module of PennyLane to prepare an electronic Hamiltonian for Variational Quantum Eigensolver (VQE) simulations. This work describes an automated workflow that converts classical molecular information into quantum-ready formats, developed for advanced simulations on noisy quantum simulators and nearterm quantum hardware. Although classical methods remain essential due to their speed and scalability, our study demonstrates that quantum methods can offer competitive accuracy in estimating molecular energies-an important factor in evaluating drug behavior. In conclusion, this paper represents a data-driven link between current chemical databases and next-generation quantum techniques, while also offering an initial roadmap for quantum-accelerated molecular design in the drug discovery arena. chEMBL ; Quantum ; Quantum computer ; Workflow ; Quantum chemical ; Drug discovery ; Scalability ; Bridging (networking) Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Ming-Hsien Li | Yuhong Yang | Yen‐Hsun Su | Chun-Yen Yang | Chia‐Chi Chang | Ming Ling Lee | Yu-Ting Chow | Yung‐Hui Li | Yaw‐Wen Kuo | Jeih-weih Hung | Hsin‐Hung Chou | Wei‐Han Hsiao | Chien‐Hung Wu | Hsiang Chen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Plasma-Engineered Vanadium pentoxide for High-sensitive Electrolyte–Insulator–Semiconductor Biosensors: Artificial Intelligence Assisted Curve Recognition Pentoxide ; Vanadium ; Hysteresis ; Surface modification ; Plasma ; Doping ; Analytical Chemistry (journal) ; Transmission electron microscopy Advanced Nanomaterials in Catalysis ; Gas Sensing Nanomaterials and Sensors
Authors: Viswanadhasai Rao
Source: International Journal of Science Architecture Technology and Environment (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Chemistry: Evolution, Foundations, and Modern Applications Artificial Intelligence in Chemistry: Evolution, Foundations, and Modern Applications V. Prabhakar Rao1*, T. Gunasekhar2, L. Md. Bhakshu3, B. L. Prasanna Latha4, A. Ramesh Babu5, B. Narasimha Reddy6, P. Venkatesu7 Abstract Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, capable of learning, reasoning, and decision-making. The roots of AI trace back to […] Applications of artificial intelligence ; Artificial Intelligence System ; TRACE (psycholinguistics) ; Key (lock) ; Human intelligence ; Artificial life Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Ali Malek | Max Dreger | Nima Shaigan | Chaojie Song | Kourosh Malek | Jasna Janković | Michael Eikerling
Source: JuSER Publikationsportal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-driven modeling of polymer electrolyte fuel cells: Towards predictive analytics with explainable artificial intelligence Predictive analytics ; Artificial neural network ; Predictive modelling ; Analytics ; Data analysis Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Explainable Artificial Intelligence (XAI)
Authors: Laura Priscila Araújo Amaro Maciel | O. Machado de Sousa | F. O. Carvalho | L. V. C. Assali | M.V. Lalić | C. Moysés Araújo | Olle Eriksson | Helena M. Petrilli | A. B. Klautau
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Rapid Screening Strategy for Aluminum-Ion Battery Cathode Materials Using Data-Driven Filtering and Ab Initio Calculations Ab initio ; Battery (electricity) ; Cathode ; Aluminium ; Materials science ; Ion ; Ab initio quantum chemistry methods ; Chemistry ; Physical chemistry ; Composite material ; Thermodynamics ; Physics ; Molecule ; Organic chemistry Electron and X-Ray Spectroscopy Techniques
Authors: Kun Lu | Qunbo Ying | Yong Dai | Qiang Yan | Shutong Wang | Yalin Wang | Gaoxiang Huang | Tao Wang | Fengyan Chen | Xuxiu Tao | Pingping Wang | Qian Fan | Chenyin Lin | Xiang Liu
Source: MedMat. (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Artificial intelligence empowering innovative research and development in medical materials: prospects from model algorithm breakthroughs to precision transformation Traditional medical material development relies on trial-and-error experimentation and lengthy clinical trials, resulting in prolonged cycles, high costs, and limited success rates. This model not only severely hampers research and development efficiency but also struggles to rapidly address the urgent demand for new materials in the medical field. Artificial intelligence (AI) technology, by integrating multimodal data with advanced algorithms, is breaking through this bottleneck. This paper systematically reviews the application progress of AI across the entire medical material development chain, focusing on 3 core scenarios: “AI-driven molecular material design,” “biocompatibility prediction,” and “personalized material customization.” Through comparative analysis of differences in technical approaches and methodological frameworks among global research groups, it deeply elucidates the key challenges currently facing the field and offers forward-looking perspectives. “biocompatibility prediction,” and “personalized material customization.” By comparing and analyzing differences in technical approaches and methodological frameworks among global research groups, it deeply elucidates key challenges in the field and prospectively outlines future directions for the convergence of AI and medical materials. This aims to provide a systematic framework for innovative development in medical materials. Field (mathematics) ; Key (lock) ; Precision medicine ; Medical research ; Core (optical fiber) ; Convergence (economics) ; Transformation (genetics) Machine Learning in Materials Science ; 3D Printing in Biomedical Research ; Manufacturing Process and Optimization