jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aymar Tchagoue | Véronique Eglin | Jean-Marc Petit | Sébastien Pruvost | Jannick Duchet-Rumeau | Jean-François Gérard
Source: Journal of Chemical Information and Modeling (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Multi-Score Reinforcement Learning for High-Tg Polyimide Design This study explores strategies to guide the generation of polyimides with high glass transition temperatures (<i>T</i><sub>g</sub> > 750 K) through reinforcement learning. We present a systematic computational framework for analyzing and combining multiple scoring functions into a single score in reinforcement learning (RL) for molecular design. Rather than relying solely on a single scoring function based on a predictive model, we examine a range of complementary scores, including a novel naı̈ve high-<i>T</i><sub>g</sub> score and various Tanimoto similarity-based scores. We analyze these scores both individually and in combination with the predictive model-based score in order to assess their influence on the structural diversity and quality of the generated polymers. In addition, we investigate several methods for combining scores, such as arithmetic, geometric, and harmonic means, as well as a novel exponential-logarithmic function, referred to as ExpAgg. We evaluate how these aggregation strategies affect the outcomes of molecular generation across different reinforcement learning configurations. Our findings show that the choice of score combination method significantly impacts both the quality and diversity of generated polymers. The proposed ExpAgg achieves superior performance in multiple settings, revealing nontrivial interactions between score compatibility and model convergence. While the predictive model exhibits underestimation in the out-of-distribution region (>800 K), our multiscore framework successfully generates chemically reasonable high-<i>T</i><sub>g</sub> candidates. Based on these insights, we provide practical guidelines for selecting aggregation functions when fusing two scores. This case study on high-<i>T</i><sub>g</sub> polyimide generation demonstrates how score aggregation strategies influence molecular RL outcomes; broader generalizability to other molecular design tasks remains to be investigated. This work emphasizes the importance of moving beyond simple weighted averages in order to enhance targeted molecular design. Reinforcement learning ; Generalizability theory ; Reinforcement ; Polyimide ; Score ; Quality (philosophy) ; Function (biology) Machine Learning in Materials Science ; Synthesis and properties of polymers ; Advanced Polymer Synthesis and Characterization
Authors: Xiuquan Zhong
Source: Theoretical and Natural Science (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

AI-Driven Prediction of Enzyme Structure and Function and Its Applications Enzymes are chemical catalysts that are essential in all the biochemical changes that occur in the biological world. Their catalytic activities are closely connected with the three-dimensional (3D) organization, which terms active sites, substrate-binding pockets, and other mechanically relevant conformational states. Protein sequences have been sequenced affordably at an unprecedented rate using high-throughput methods; but experimentally measured structures and high-quality functional annotation are relative rarities, resulting in an ongoing gap in the so-called sequence to structure to functional gap. In recent years (in the last five years), the deep-learning branch of artificial intelligence (AI) brought disruptive improvements that created a significant undercut in this gap. The structure predictors of deep learning structure predictors such as AlphaFold2 have demonstrated high-confidence structural models of a large number of proteins, including enzymes, leading to lower time and resource costs associated with structure determination. Simultaneously, prediction of enzyme functions has moved away both homology-driven transfer prediction and manually hand-crafted features, to representation learning with sequences and structures, such as protein language models, graph neural networks, and contrastive learning models, but also integrative (multimodal) prediction with the combination of complementary signals. The review presents the latest developments in AI-based enzyme structure prediction and AI-based enzyme function prediction and describes its scientific and practical importance introduced by such developments. We emphasize such representative uses as enzyme engineering, synthetic biology, and drug discovery and development. We also specify outstanding constraints like the prediction of multimeric assemblies, conformational heterogeneity and dynamics, and realistic enzyme-ligand interfaces, and the outlook of future directions through additional training information, combining physical constraints, and close integration of AI prediction with experimental justification. Function (biology) ; Protein structure ; Representation (politics) ; Protein function ; Protein structure prediction ; Sequence (biology) ; Annotation ; Graph Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Dhruv Menon | Vivek Kumar Singh | Xu Chen | Mohammad Reza Alizadeh Kiapi | Ivan Zyuzin | Hamish W. MacLeod | Nakul Rampal | William Shepard | Omar M. Yaghi | David Fairén Jiménez
Source: arXiv (Cornell University) (article, 2026-03-20)
Field: Chemistry — Inorganic Chemistry

A chemical language model for reticular materials design Reticular chemistry has enabled the synthesis of tens of thousands of metal-organic frameworks (MOFs), yet the discovery of new materials still relies largely on intuition-driven linker design and iterative experimentation. As a result, researchers explore only a small fraction of the vast chemical space accessible to reticular materials, limiting the systematic discovery of frameworks with targeted properties. Here, we introduce Nexerra-R1, a building-block chemical language model that enables inverse design in reticular chemistry through the targeted generation of organic linkers. Rather than generating complete frameworks directly, Nexerra-R1 operates at the level of molecular building blocks, preserving the modular logic that underpins reticular synthesis. The model supports both unconstrained generation of low-connectivity linkers and scaffold-constrained design of symmetric multidentate motifs compatible with predefined nodes and topologies. We further combine linker generation with flow-guided distributional targeting to steer the generative process toward application-relevant objectives while maintaining chemical validity and assembly feasibility. The generated linkers are subsequently assembled into three-dimensional frameworks and are structurally optimized to produce candidate materials compatible with experimental synthesis. Using Nexerra-R1, we validate this strategy by rediscovering known MOFs and by proposing the experimental synthesis of a previously unreported framework, CU-525, generated entirely in silico. Together, these results establish a general inverse-design paradigm for reticular materials in which controllable chemical language modelling enables the direct translation from computational design to synthesizable frameworks. Reticular connective tissue ; Chemical space ; Limiting ; Modular design ; Process (computing) ; Translation (biology) Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes ; Magnetism in coordination complexes
Authors: Dhruv Menon | Vivek Singh | Xu Chen | Mohammad Reza Alizadeh Kiapi | Ivan Zyuzin | Hamish W. MacLeod | Nakul Rampal | William Shepard | Omar M. Yaghi | David Fairén Jiménez
Source: arXiv (Cornell University) (preprint, 2026-03-20)
Field: Chemistry — Inorganic Chemistry

A chemical language model for reticular materials design Reticular chemistry has enabled the synthesis of tens of thousands of metal-organic frameworks (MOFs), yet the discovery of new materials still relies largely on intuition-driven linker design and iterative experimentation. As a result, researchers explore only a small fraction of the vast chemical space accessible to reticular materials, limiting the systematic discovery of frameworks with targeted properties. Here, we introduce Nexerra-R1, a building-block chemical language model that enables inverse design in reticular chemistry through the targeted generation of organic linkers. Rather than generating complete frameworks directly, Nexerra-R1 operates at the level of molecular building blocks, preserving the modular logic that underpins reticular synthesis. The model supports both unconstrained generation of low-connectivity linkers and scaffold-constrained design of symmetric multidentate motifs compatible with predefined nodes and topologies. We further combine linker generation with flow-guided distributional targeting to steer the generative process toward application-relevant objectives while maintaining chemical validity and assembly feasibility. The generated linkers are subsequently assembled into three-dimensional frameworks and are structurally optimized to produce candidate materials compatible with experimental synthesis. Using Nexerra-R1, we validate this strategy by rediscovering known MOFs and by proposing the experimental synthesis of a previously unreported framework, CU-525, generated entirely in silico. Together, these results establish a general inverse-design paradigm for reticular materials in which controllable chemical language modelling enables the direct translation from computational design to synthesizable frameworks. Reticular connective tissue ; Chemical space ; Limiting ; Modular design ; Process (computing) ; Translation (biology) Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes ; Magnetism in coordination complexes
Authors: Samanta Biswas
Source: International Journal of Innovative Science and Research Technology (IJISRT) (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Chemistry for Future Sustainability: An In-Depth Scientific Review Artificial Intelligence (AI) is rapidly transforming chemical sciences by enabling data-driven molecular discovery, reaction prediction, and process optimization. Simultaneously, global sustainability challenges such as climate change, pollution, and energy scarcity require innovative chemical solutions with minimal environmental impact. The integration of AI with chemistry offers a powerful framework for achieving sustainable development through green molecular design, energy-efficient catalysis, optimized industrial processes, and enhanced environmental monitoring. This review critically examines recent advances in AI-driven chemistry and evaluates their role in promoting future sustainability, while highlighting challenges related to data quality, interpretability, energy consumption, and ethical governance [1–5]. Sustainability ; Scarcity ; Process (computing) ; Corporate governance ; Energy (signal processing) ; Sustainable development Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Asymmetric Hydrogenation and Catalysis
Authors: Mohammad Abu-Shams
Source: Discover Applied Sciences (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Artificial intelligence integration in materials science The pervasive integration of Artificial Intelligence (AI) in materials science has transformed the design, functionality, properties and structures of innovative, as well as traditional molecules and crystalline. The new AI-powered laboratories promise to address persistent problems facing humans such cost reduction and productivity improvement in research and development. AI accelerates the discovery of resilient and durable chemical structures capable of providing humans with quality lasting products. More importantly, AI allows researchers to devise new bioactive materials making drugs more potent and affordable. The present research offers readers a succinct comprehensive overview of the progress in Ai application within materials science. Findings demonstrate that AI has improved chemoinformatics, imaging and drug discovery. Moreover, the implementation of AI has resulted in advancing additive manufacturing through transforming 3D printing design and optimization processes. The study documents an increasing trend in developing specific AI tools for advancing materials science such as ChatMOF, MatKG, BioinspiredLLM, MatterGen, and SpectATNet. Notwithstanding the progress in AI’s implementation within materials science, there are important limits to the process represented by data quality, availability and accessibility. Future directions of research in the discipline include developing autonomous research assistant models and laboratories, improving the flexibility and responsiveness of AI systems to mimic human decision-making, and advancing interdisciplinary collaborations to enhance AI-enabled materials discovery. Flexibility (engineering) ; Process (computing) ; Quality (philosophy) ; Applications of artificial intelligence ; Productivity ; Automation Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Wenrui Zhang | Xinggang Wang | Bin Feng | Wenyu Liu
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

MolSight: Optical Chemical Structure Recognition with SMILES Pretraining, Multi-Granularity Learning and Reinforcement Learning Optical Chemical Structure Recognition (OCSR) plays a pivotal role in modern chemical informatics, enabling the automated conversion of chemical structure images from scientific literature, patents, and educational materials into machine-readable molecular representations. This capability is essential for large-scale chemical data mining, drug discovery pipelines, and Large Language Model (LLM) applications in related domains. However, existing OCSR systems face significant challenges in accurately recognizing stereochemical information due to the subtle visual cues that distinguish stereoisomers, such as wedge and dash bonds, ring conformations, and spatial arrangements. To address these challenges, we propose MolSight, a comprehensive learning framework for OCSR that employs a three-stage training paradigm. In the first stage, we conduct pre-training on large-scale but noisy datasets to endow the model with fundamental perception capabilities for chemical structure images. In the second stage, we perform multi-granularity fine-tuning using datasets with richer supervisory signals, systematically exploring how auxiliary tasks—specifically chemical bond classification and atom localization—contribute to molecular formula recognition. Finally, we employ reinforcement learning for post-training optimization and introduce a novel stereochemical structure dataset. Remarkably, we find that even with MolSight's relatively compact parameter size, the Group Relative Policy Optimization (GRPO) algorithm can further enhance the model's performance on stereomolecular. Through extensive experiments across diverse datasets, our results demonstrate that MolSight achieves state-of-the-art performance in (stereo)chemical optical structure recognition. Reinforcement learning ; Perception ; Pattern recognition (psychology) ; Training set ; Reinforcement ; Chemical process Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Pooya Jafari | Guofeng Ye | Junling Wang | Tao Chu | Boyou Hou | Di Wu | Siqi Huo | Hao Wang | Ziqi Sun | Pingan Song
Source: Journal of Material Science and Technology (article, 2026-03-01)
Field: Materials Science — Polymers and Plastics

Designing flame retardants for epoxy resins with generative AI for preventing battery thermal runaway propagation • As-trained descriptive AI model can describe fire properties of fire retardants. • The AI model can predict how new FRs impact of tensile strength and T g of EP. • As-trained GAI model can generate new FR molecules with better performances. • Fire-retardant EP-coated wood can prevent inter-battery thermal propagation. Artificial Intelligence (AI) has demonstrated great potential for discovering new flame retardants (FRs) for polymers. While existing AI can describe flame retardancy of FRs, it has been unable either to generate new FR molecules with desired performances or predict their impacts on mechanical strength and glass transition temperature ( T g ) of polymer matrices. To fill this knowledge gap, we, herein, propose a generative artificial intelligence (GAI)-based de novo molecular design approach (GAI4FR) to generate new FR molecules for commercially important epoxy (EP) resins. Also, a descriptive AI model is trained using existing works to predict impacts of AI-generated FR molecules on flame retardancy, tensile strength ( σ t ), and T g of EP, enabling the identification of three FR molecules with better overall performances. The as-identified FR molecules are then synthesized, and their predicted performances are well- validated. We further demonstrate the application of as-prepared flame-retardant EP-coated wood sheets as heat shields for preventing thermal runaway of lithium-ion battery (LIB) packs. The coated wood shows equally desirable thermal protection for LIB packs to commercial counterparts. This work offers a groundbreaking GAI4FR framework for creating next-generation high-performance flame retardants for various flammable polymers and opens the door to discovering many other functional materials. Epoxy ; Thermal runaway ; Thermal ; Battery (electricity) ; Thermal stability Flame retardant materials and properties ; Fiber-reinforced polymer composites ; Epoxy Resin Curing Processes
Authors: Ziyu Fan | Zhijian Huang | Yinxin Li | Xiaowen Hu | Siyuan Shen | Yunliang Wang | Zeyu Zhong | Shuhong Liu | Shuning Yang | Shangqian Wu | Min Wu | Lei Deng
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Hierarchical Structure-Property Alignment for Data-Efficient Molecular Generation and Editing Property-constrained molecular generation and editing are crucial in AI-driven drug discovery but remain hindered by two factors: (i) capturing the complex relationships between molecular structures and multiple properties remains challenging, and (ii) the narrow coverage and incomplete annotations of molecular properties weaken the effectiveness of property-based models. To tackle these limitations, we propose HSPAG, a data-efficient framework featuring hierarchical structure–property alignment. By treating SMILES and molecular properties as complementary modalities, the model learns their relationships at atom, substructure, and whole-molecule levels. Moreover, we select representative samples through scaffold clustering and hard samples via an auxiliary variational auto-encoder (VAE), substantially reducing the required pre-training data. In addition, we incorporate a property relevance-aware masking mechanism and diversified perturbation strategies to enhance generation quality under sparse annotations. Experiments demonstrate that HSPAG captures fine-grained structure–property relationships and supports controllable generation under multiple property constraints. Two real-world case studies further validate the editing capabilities of HSPAG. Property (philosophy) ; Cluster analysis ; Masking (illustration) ; Mechanism (biology) ; Robustness (evolution) ; Hierarchical clustering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Zhangyong Si | Mary BE Chan-Park
Source: Progress in Polymer Science (article, 2026-01-03)
Field: Chemistry — Organic Chemistry

Recent advances in designing antimicrobial polymers with diverse uptake and killing mechanisms Antimicrobial ; Rational design ; Modular design ; Synthetic biology ; Antibiotic resistance ; Human health ; Polymer Antimicrobial agents and applications ; Biopolymer Synthesis and Applications ; Advanced Polymer Synthesis and Characterization
Authors: Wenyuan Yang | Xin Wang | Hao Liu | Haigang Wu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Biomaterials

Explainable reinforcement learning designs a LRP1-targeted BBB-penetrating peptide with spatial selectivity for glioblastoma therapy Transcytosis ; Endocytosis ; Reinforcement learning ; Peptide ; Glioblastoma ; Rational design ; Nanomedicine Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Biochemical and Structural Characterization
Authors: Prashnna Gyawali | Alejandro Bautista-Hernández | Aldo H. Romero
Source: Journal of Physics Materials (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Acceleration, agency, and foundations: ten influential arXiv directions in AI for materials science (2025) Abstract A small number of preprints each year present results that enable faster downstream work by decreasing adoption challenges. The year 2025 revealed several arXiv papers that focused on artificial intelligence (AI) applications for materials science, demonstrating that their content would be reused through the distribution of code, weights, datasets, benchmarks, and the development of new implementations and third-party connections. This perspective organizes ten research papers based on their functional roles, which include fundamental atomistic models, controlled generative design and synthesis, data infrastructure and reasoning benchmarks, and large language model (LLM)-driven agency to identify shared methods for speeding up processes. We argue that AI-for-materials workflows are converging toward a five-layer stack: (i) transferable atomistic foundation potentials, (ii) generative engines for crystals and amorphous structures, (iii) feedback/control loops for inverse design, (iv) datasets and benchmarks that constrain and compare claims, and (v) LLM components for decision-making and planning. We discuss what each layer enables, where failure modes remain, and what a robust AI-for-materials pipeline should look like beyond 2025. Papers were selected using a transparent proxy-based rubric (artifact release, integration readiness, and early reuse signals), rather than citation counts, which lag for recent preprints. Generative grammar ; Rubric ; Reuse ; Workflow ; Pipeline (software) ; Implementation ; Agency (philosophy) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Inorganic Chemistry and Materials
Authors: Rafael G. Uceda | Sandra Míguez‐Lago | Carlos M. Cruz | Boris Pérez‐Cañedo | Alfonso Gijón | Luis Álvarez De Cienfuegos | Antonio J. Mota | D. Miguel | Juan M. Cuerva
Source: Advanced Science (article, 2026-03-13)
Field: Chemistry — Organic Chemistry

Artificial Intelligence Predictions in Huge Chemical Spaces: Chiroptical Properties of [6]‐helicene Family Navigating the vast chemical space remains a major challenge in the rational design of materials with tailored properties. Here, we investigate how the properties of the [6]helicene family can be effectively modelled using a local, data-driven AI framework. By predicting each molecule from its closest structural neighbours, we accurately estimate diverse photophysical and (chir)optical properties. The coupling with genetic algorithms enables efficient inverse design and multi-objective optimization, yielding molecules unlikely to arise from intuition alone. The method uncovers [6]helicenes with enhanced electronic circular dichroism (ECD) features, tuned low-energy transitions, and exceptionally large g values, while revealing clear structure-property relationships that translate into practical design rules. Overall, this framework offers a general and efficient route for goal-directed molecular discovery across extensive chemical spaces. Intuition ; Chemical space ; Rational design ; Inverse ; Molecule ; Coupling (piping) Synthesis and Properties of Aromatic Compounds ; Photochromic and Fluorescence Chemistry ; Supramolecular Chemistry and Complexes