jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhiting Zhang | Jia Qiu | Jiajun Zheng | Zhunzhun Yu | Lebin Su | Qianghua Lin | Chonghuan Zhang | Kuangbiao Liao
Source: Journal of Chemical Information and Modeling (article, 2025-03-20)
Field: Chemistry — Organic Chemistry

Enhanced Regioselectivity Prediction of sp<sup>2</sup> C–H Halogenation via Negative Data Augmentation and Multimodel Integration Efficient molecular editing is pivotal in synthetic chemistry, especially for developing drugs, materials, and high-value chemicals. Electrophilic aromatic substitution (S<sub>E</sub>Ar) reactions, specifically sp<sup>2</sup> C-H halogenation, face significant challenges due to electronic and steric factors, necessitating extensive trial-and-error. This study introduces an innovative machine learning-based model to predict halogenation sites in S<sub>E</sub>Ar reactions, achieving an average accuracy of 93% in 5-fold cross-validation. Employing ensemble techniques, particularly AutoGluon-Tabular (AG), the model demonstrates broad applicability across various aromatic halides, enhancing its utility in drug design, materials science, and more. By reducing experimental uncertainty and optimizing synthetic pathways, this model saves considerable time and resources, thereby accelerating innovation in synthetic chemistry. Halogenation ; Regioselectivity ; Steric effects ; Chemistry ; Electrophile ; Combinatorial chemistry ; Electrophilic aromatic substitution ; Halide ; Biochemical engineering ; Computational chemistry ; Computer science ; Organic chemistry ; Engineering ; Catalysis Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Radical Photochemical Reactions
Authors: Yuechun Li | Chunyan Ji | Zhaowen Cui | Jianxing Feng | Liang Zhang | Sha Liu | Wentao Zhang | Yanwei Ji | Yizhong Shen | Jianlong Wang
Source: Advanced Science (article, 2025-08-23)
Field: Materials Science — Materials Chemistry

Electron Transfer‐Tailored D‐Band Center to Boost Nanozyme Catalysis for Interpretable Machine Learning‐Empowered Intelligent Biosensing The escalating global burden of infectious diseases demands biosensing technologies that transcend the complexity-sensitivity-accuracy trade-off in real-world applications. Herein, an interpretable machine learning-empowered multimodal biosensor synergizing electron transfer-enhanced nanozymes and aggregation-induced emission luminogens (AIEgens) for ultrasensitive pathogen detection is presented. By engineering aminophenol formaldehyde resin nanobowls anchored with monodisperse Pt nanoparticles, interfacial electron transfer (N→Pt→O) induces an upshift of Pt d-band center relative to the Fermi level, as validated by density functional theory. This electronic modulation optimizes H<sub>2</sub>O<sub>2</sub> adsorption energy, lowers the energy barrier of the rate-determining step, and reduces activation energy, resulting in a 3.4-fold enhancement in peroxidase-like activity over conventional Pt nanozymes. Then, AIEgens are strategically integrated to generate cross-validated anti-interference signals, achieving a record-low detection limit for Salmonella typhimurium, surpassing classical immunoassays in sensitivity and accuracy. A SHapley Additive exPlanations (SHAP)-guided eXtreme Gradient Boosting (XGBoost) algorithm dynamically fuses multimodal signals, enhancing sensitivity by five fold over single-mode detection and delivering 100% diagnostic accuracy for positive samples. SHAP further deciphers the synergetic mechanism, revealing concentration-dependent signal contributions and validating decision logic. This work pioneers a nanozyme-AI co-design framework, bridging d-band-driven catalytic precision and machine learning-powered signal intelligence to redefine biosensing paradigms for combating public health emergencies. Biosensor ; Bridging (networking) ; Electron transfer ; Nanotechnology ; Computer science ; Energy transfer ; Sensitivity (control systems) ; Transfer of learning ; Detection limit ; Nanoparticle ; Artificial intelligence ; Chemistry ; Materials science ; Machine learning ; Electronic engineering ; Engineering ; Photochemistry Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Yajuan Shi | Fangyou Yan | Jie Jin | Zheng‐Hong Luo | Yin‐Ning Zhou
Source: Current Opinion in Chemical Engineering (article, 2025-05-03)
Field: Chemistry — Organic Chemistry

Advances in calculation of kinetic parameters in free-radical polymerization by data-driven methods Kinetic energy ; Polymerization ; Computer science ; Materials science ; Physics ; Classical mechanics ; Polymer ; Composite material Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties ; Photopolymerization techniques and applications
Authors: Basem A. Eraqi | Dmitrii Khizbullin | Shashank S. Nagaraja | S. Mani Sarathy
Source: Communications Chemistry (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Molecular property prediction in the ultra‐low data regime Data scarcity remains a major obstacle to effective machine learning in molecular property prediction and design, affecting diverse domains such as pharmaceuticals, solvents, polymers, and energy carriers. Although multi-task learning (MTL) can leverage correlations among properties to improve predictive performance, imbalanced training datasets often degrade its efficacy through negative transfer. Here, we present adaptive checkpointing with specialization (ACS), a training scheme for multi-task graph neural networks that mitigates detrimental inter-task interference while preserving the benefits of MTL. We validate ACS on multiple molecular property benchmarks, where it consistently surpasses or matches the performance of recent supervised methods. To illustrate its practical utility, we deploy ACS in a real-world scenario of predicting sustainable aviation fuel properties, showing that it can learn accurate models with as few as 29 labeled samples. By enabling reliable property prediction in low-data regimes, ACS broadens the scope and accelerates the pace of artificial intelligence-driven materials discovery and design. Property (philosophy) ; Environmental science ; Materials science ; Computer science ; Philosophy Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Jiajing Chen | Lu Jiang | Shendong Tan | Jun Yang | Zihui Li | Chen Bai | Xiang Zhang | Rongni Li | Yaoshu Xie | Ming Liu | Yanbing He | Tingzheng Hou
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning-aided screening of inorganic lithium solid-state electrolytes with a wide electrochemical window We construct an electrochemical window (ECW) dataset of over 16 000 Li-containing compounds using a thermodynamic approach for solid-state electrolytes (SSEs). Electrochemistry ; Window (computing) ; Electrolyte ; Electrochemical window ; Lithium (medication) ; Solid-state ; Fast ion conductor ; Computer science ; Materials science ; Chemistry ; Electrode ; Ionic conductivity ; Medicine ; World Wide Web Machine Learning in Materials Science
Authors: Tianren Zhang | Dai-Bei Yang
Source: Chemistry of Materials (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Multimodal Machine Learning with Large Language Embedding Model for Polymer Property Prediction Contemporary large language models (LLMs), such as GPT-4 and Llama, have harnessed extensive computational power and diverse text corpora to achieve remarkable proficiency in interpreting and generating domain-specific content, including materials science. To leverage the domain knowledge embedded within these models, we propose a simple yet effective multimodal architecture, PolyLLMem. By integrating text embeddings from Llama 3 with molecular structure embeddings from Uni-Mol, PolyLLMem enables the accurate prediction of polymer properties. Low-Rank Adaptation (LoRA) layers were integrated into our model during the property prediction stage to adapt the Llama 3 and Uni-Mol embeddings to our limited polymer data set, thereby enhancing their chemical relevance for polymer SMILES representation. Such a balanced fusion of fine-tuned textual and structural information enables PolyLLMem to robustly predict a variety of polymer properties despite the scarcity of training data. Its performance is comparable to, and in some cases exceeds, that of graph-based or transformer-based models that typically require pretraining on millions of polymer samples. These findings demonstrate that LLM, such as Llama, can effectively capture chemical information encoded in polymer SMILES (PSMILES), and underscore the efficacy of multimodal fusion of LLM embeddings and molecular structure embeddings in overcoming data scarcity and accelerating the discovery of advanced polymeric materials. Embedding ; Property (philosophy) ; Computer science ; Polymer ; Artificial intelligence ; Machine learning ; Materials science ; Philosophy ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yan Zhang | Changwei Hu | Dianyong Tang | Zhishan Su
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning assisted screening of MXene-supported single-atom catalysts for oxygen reduction SACs supported on Ti 2 NO 2 MXene and its derivatives with defective surfaces (O or Ti vacancies) are screened for excellent ORR electrocatalytic activity using first-principles calculations and key descriptors are identified by machine learning. Catalysis ; Reduction (mathematics) ; Oxygen reduction reaction ; Atom (system on chip) ; Oxygen atom ; Oxygen ; Oxygen reduction ; Materials science ; Chemistry ; Chemical engineering ; Nanotechnology ; Computer science ; Embedded system ; Physical chemistry ; Engineering ; Electrochemistry ; Molecule ; Organic chemistry ; Mathematics MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: Yuanda Tao | Ziqi Tian | Guang Li | Jin Wen | Meifang Zhu
Source: The Journal of Physical Chemistry C (article, 2025-06-17)
Field: Chemistry — Organic Chemistry

Improving Polymer UV-Aging Resistance: Insights from Degradation Mechanisms and High-Throughput Screening Photodegradation of high-performance fibers critically limits their utility in aerospace and heat-resistant applications, particularly under acidic photoirradiation conditions. A representative example is poly(p-phenylene benzobisoxazole) (PBO), which degrades under photoirradiation, particularly in acidic conditions. To address this challenge, we present a machine learning framework for high-throughput screening of UV-resistant functional groups to enhance PBO’s photostability. First, density functional theory (DFT) calculations elucidate the degradation mechanism, revealing that phosphoric acid catalysis significantly lowers reaction barriers for oxazole ring opening. Molecular dynamics (MD) simulations with a reactive force field (ReaxFF) further demonstrate that mechanical performance declines when degraded repeating units exceed a critical threshold of 2.50%. Building on these mechanistic insights, a Transformer-based spectral prediction model is developed to screen UV-resistant candidates, identifying three optimal functional groups that preserve PBO’s intrinsic mechanical properties. This integrated computational approach─combining DFT, ReaxFF-MD, and machine learning─provides a scalable strategy for designing UV-resistant polymers, with broader applicability to advanced material systems. Degradation (telecommunications) ; Throughput ; Resistance (ecology) ; High resistance ; Polymer degradation ; Materials science ; Polymer ; Computer science ; Composite material ; Biology ; Telecommunications Photopolymerization techniques and applications ; Microplastics and Plastic Pollution ; Dyeing and Modifying Textile Fibers
Authors: Kriti Kapil | Hironobu Murata | Lucca Trachsel | Krzysztof Matyjaszewski
Source: Sustainable Chemistry and Pharmacy (article, 2025-03-14)
Field: Materials Science — Biomaterials

Tailoring polymer architectures to drive molecular sieving in protein-polymer hybrids Protein-polymer hybrids (PPHs) exemplify a transformative intersection of biotechnology and materials science, offering innovative solutions to complex biomedical and engineering challenges. These hybrids synergistically combine the functional specificity of proteins with the structural adaptability of synthetic polymers, resulting in materials with enhanced stability, bioactivity, and responsiveness to environmental stimuli. Advances in reversible deactivation radical polymerization techniques, including atom transfer radical polymerization and reversible addition-fragmentation chain transfer polymerization, have enabled precise control over polymer architecture, molecular weight, and functionality. This precision has facilitated the creation of sophisticated polymer architectures such as block copolymers and complex polymer architectures, tailored for applications including molecular sieving and selective separation. Molecular sieving in PPHs, governed by polymer morphology and protein interactions, holds potential for applications ranging from size-selective separations and enzymatic pathway activation to next-generation therapeutic delivery systems. However, critical challenges remain, including preserving protein activity during synthesis, achieving biocompatibility under physiological conditions, and ensuring long-term stability against proteolytic degradation and pH fluctuations. This review discusses recent advancements in PPHs design, emphasizing synthetic methodologies, advanced characterization techniques, and predictive modeling approaches for achieving varying polymer topologies. It highlights the pivotal role of computational tools, including molecular dynamics simulations and machine learning algorithms, in guiding the design and optimization of these hybrids. Future research should prioritize interdisciplinary approaches to expand protein-polymer interaction datasets, refine predictive models, and enable high-throughput synthesis. Addressing these challenges will accelerate the development of next-generation PPHs for transformative applications in drug delivery, biocatalysis, and molecular separation technologies. • Synergistic protein-polymer hybrids enhance stability, bioactivity, and responsiveness. • ATRP and RAFT enable precise control of polymer architecture and functionality. • PPHs offer molecular sieving for separations, enzymatic pathways, and therapeutics. • Computational tools guide PPHs design using simulations and machine learning models. • Interdisciplinary research drives next-gen PPHs for drug delivery and biocatalysis. Polymer ; Materials science ; Hybrid ; Chemical engineering ; Nanotechnology ; Polymer science ; Chemistry ; Composite material ; Engineering ; Biology biodegradable polymer synthesis and properties ; Polymer Surface Interaction Studies ; Advanced Polymer Synthesis and Characterization
Authors: Kevin Ji | Tanumoy Banerjee | Matthew Witman | Mark D. Allendorf | Vitalie Stavila | Prashant Singh
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Design of lightweight BCC multi-principal element alloys with enhanced hydrogen storage using a machine learning-driven genetic algorithm Body-centered cubic (BCC) based multi-principal element alloy (MPEA) hydrides have demonstrated significant potential for compact and efficient hydrogen storage. Genetic algorithm ; Hydrogen ; Hydrogen storage ; Alloy ; Finite element method Nuclear Materials and Properties ; Aluminum Alloy Microstructure Properties
Authors: Sukriti Singh | José Miguel Hernández-Lobato
Source: Nature Communications (article, 2025-04-15)
Field: Materials Science — Materials Chemistry

A meta-learning approach for selectivity prediction in asymmetric catalysis Transition metal-catalyzed asymmetric reactions are of high contemporary importance in organic synthesis. Recently, machine learning (ML) has shown promise in accelerating the development of newer catalytic protocols. However, the need for large amount of experimental data can present a bottleneck for implementing ML models. Here, we propose a meta-learning workflow that can harness the literature-derived data to extract shared reaction features and requires only a few examples to predict the outcome of new reactions. Prototypical networks are used as a meta-learning method to predict the enantioselectivity of asymmetric hydrogenation of olefins. This meta-learning model consistently provides significant performance improvement over other popular ML methods such as random forests and graph neural networks. The performance of our meta-model is analyzed with varying sizes of training examples to demonstrate its utility even with limited data. A good model performance on an out-of-sample test set further indicates the general applicability of our approach. We believe this work will provide a leap forward in identifying promising reactions in the early phases of reaction development when minimal data is available. Bottleneck ; Computer science ; Workflow ; Artificial intelligence ; Machine learning ; Artificial neural network ; Set (abstract data type) ; Database Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Topic Modeling
Authors: Austin Rodriguez | Justin S. Smith | José L. Mendoza-Cortés
Source: Journal of Chemical Theory and Computation (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Does Hessian Data Improve the Performance of Machine Learning Potentials? The integration of machine learning into reactive chemistry, materials discovery, and drug design is transforming the development of novel molecules and materials. Machine Learning Interatomic Potentials (MLIPs) predict potential energies and forces with quantum chemistry accuracy, surpassing traditional approaches. Incorporating force fitting in MLIP training enhances potential-energy surface predictions and improves model transferability and reliability. This paper introduces and evaluates the integration of Hessian matrix training in MLIPs, which encodes second-order information about the PES curvature. Our evaluation focuses on models trained only to equilibrium geometries and first-order saddle points (i.e., critical points on the potential surface), demonstrating their ability to extrapolate to nonequilibrium geometries. This integration improves extrapolation capabilities, allowing MLIPs to accurately predict energies, forces, and Hessian predictions for nonequilibrium geometries. Hessian-trained MLIPs enhance reaction pathway modeling, transition state identification, and vibrational spectra predictions, benefiting molecular dynamics (MD) simulations and Nudged Elastic Band (NEB) calculations. By analyzing models trained with varying combinations of energy, force, and Hessian data on a small molecule reactive data set, we demonstrate that models including Hessian information not only extrapolate more accurately to unseen molecular systems, improving accuracy in reaction modeling and vibrational analysis, but also reduce the total amount of data required for effective training. However, the primary trade-off is increased computational expense, as Hessian training requires more resources than conventional energy-force training. Our findings provide comprehensive insights into the advantages and limitations of Hessian integration in MLIP training, allowing practitioners in computational chemistry to make informed decisions about employing this method in accordance with their specific research objectives and computational constraints. Hessian matrix ; Computer science ; Machine learning ; Artificial intelligence ; Mathematics ; Applied mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Materials Characterization Techniques
Authors: Ariana Quek | Niuchang Ouyang | H. H. Lin | Olivier Delaire | Johann Guilleminot
Source: Mechanics of Materials (article, 2025-01-05)
Field: Materials Science — Materials Chemistry

Enhancing robustness in machine-learning-accelerated molecular dynamics: A multi-model nonparametric probabilistic approach Robustness (evolution) ; Nonparametric statistics ; Materials science ; Probabilistic logic ; Machine learning ; Artificial intelligence ; Molecular dynamics ; Biological system ; Computer science ; Econometrics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sushree Jagriti Sahoo | Mikael Maraschin | Daniel S. Levine | Zachary W. Ulissi | C. Lawrence Zitnick | Joel B. Varley | Joseph A. Gauthier | Nitish Govindarajan | Muhammed Shuaibi
Source: ArXiv.org (preprint, 2025-09-22)
Field: Materials Science — Materials Chemistry

The Open Catalyst 2025 (OC25) Dataset and Models for Solid-Liquid Interfaces Catalysis at solid-liquid interfaces plays a central role in the advancement of energy storage and sustainable chemical production technologies. By enabling accurate, long-time scale simulations, machine learning (ML) models have the potential to accelerate the discovery of (electro)catalysts. While prior Open Catalyst datasets (OC20 and OC22) have advanced the field by providing large-scale density functional theory (DFT) data of adsorbates on surfaces at solid-gas interfaces, they do not capture the critical role of solvent and electrolyte effects at solid-liquid interfaces. To bridge this gap, we introduce the Open Catalyst 2025 (OC25) dataset, consisting of 7,801,261 calculations across 1,511,270 unique explicit solvent environments. OC25 constitutes the largest and most diverse solid-liquid interface dataset that is currently available and provides configurational and elemental diversity: spanning 88 elements, commonly used solvents/ions, varying solvent layers, and off-equilibrium sampling. State-of-the-art models trained on the OC25 dataset exhibit energy, force, and solvation energy errors as low as 0.1 eV, 0.015 eV/Å, and 0.04 eV, respectively; significantly lower than than the recently released Universal Models for Atoms (UMA-OC20). Additionally, we discuss the impact of the quality of DFT-calculated forces on model training and performance. The dataset and accompanying baseline models are made openly available for the community. We anticipate the dataset to facilitate large length-scale and long-timescale simulations of catalytic transformations at solid-liquid interfaces, advancing molecular-level insights into functional interfaces and enabling the discovery of next-generation energy storage and conversion technologies. Solvation ; Interface (matter) ; Catalysis ; Field (mathematics) ; Density functional theory ; Scale (ratio) ; Energy (signal processing) ; Baseline (sea) ; Potential energy Catalytic Processes in Materials Science