jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Misael Bessa Sales | Francisco Izaias da Silva Aires | Paulo Gonçalves de Sousa | Calebe da Rocha Silva | Emanuel Lino Rodrigues | Maria Cristiane Martins de Souza | José C. S. dos Santos
Source: The Canadian Journal of Chemical Engineering (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Artificial intelligence in enzyme catalysis: Emerging trends and applications in biocatalyst engineering Abstract This study systematically explores the applications of artificial intelligence (AI) in enzyme research, emphasizing its role in enzyme design, catalytic function prediction, stability, discovery of novel enzymes, and process optimization. Using bibliometric review techniques, we quantified research subfields, mapped global scientific trends, and identified the machine learning (ML) approaches most frequently employed in enzymology. Our analysis reveals that algorithms such as random forests, support vector machines, decision trees, and K‐nearest neighbours are predominantly applied to predict protein solubility, enzyme stability after mutations, catalytic mechanisms, and drug‐enzyme interactions. These findings demonstrate how AI enables more accurate predictions of enzymatic behaviour, accelerates drug discovery pipelines, and supports the identification of new metabolic pathways for industrial biotechnology. Beyond mapping current applications, this work highlights significant challenges, including the scarcity of high‐quality datasets and the limited generalization of models across enzyme classes. Nonetheless, the results underscore the transformative potential of AI when integrated into experimental workflows, particularly with advances in laboratory automation and the emerging synergy between AI and quantum computing. By consolidating state‐of‐the‐art knowledge and identifying future directions, this study contributes to guiding research strategies in data‐driven enzyme engineering and promotes the development of sustainable biotechnological solutions. Transformative learning ; Identification (biology) ; Process (computing) ; Automation ; Function (biology) ; Scarcity ; Protein engineering Machine Learning in Materials Science ; Enzyme Catalysis and Immobilization ; Protein Structure and Dynamics
Source: Molecular Systems Design & Engineering (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Front cover Enhancing generative molecular design via uncertainty Nucleofection ; Gestational period ; TSG101 ; Liquation ; Dysgeusia ; Diafiltration ; Emperipolesis ; Triacetin ; Durvalumab Supramolecular Chemistry and Complexes ; Gene Regulatory Network Analysis ; Molecular Communication and Nanonetworks
Authors: Ting Qin | Aparna Chandrasekaran | Jack Hoffman | Jason Shiers | Tarun Jain | Colin Sambrook Smith
Source: SLAS TECHNOLOGY (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Empowering chemists in drug design: Delivering AI solutions through an ELN framework at the enterprise level Artificial intelligence (AI) holds immense potential to revolutionize drug discovery, yet its widespread adoption within scientific enterprises faces significant hurdles. Key challenges include ensuring user-friendliness, managing complex workflows, and integrating diverse datasets. To address these issues, we propose a novel framework that leverages the familiar Electronic Lab Notebook (ELN) paradigm. By formalizing AI workflows as ELN protocols and treating AI execution as ELN experiments, the proposed system provides a scalable, traceable, and user-oriented deployment strategy that aligns with existing laboratory practices. This ELN-based framework adheres to FAIR principles, enhancing data findability, accessibility, interoperability, and reusability. By mirroring the intuitive ELN interface, our solution empowers bench chemists to easily access and utilize cutting-edge AI tools, enabling them to move beyond purely synthetic roles and fully engage as medicinal chemists. This allows chemists to design compounds with real-time consideration of synthetic feasibility and to actively contribute to the drug design process with their practical expertise, thereby accelerating drug discovery efforts and maximizing the return on AI investments. Workflow ; Software deployment ; Mirroring ; Process (computing) ; Key (lock) ; Drug discovery Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Sonia Paliwal | Himanshu Joshi | Kanak Pandey
Source:  (article, 2026-02-04)
Field: Chemistry — Analytical Chemistry

Phytochemical Analysis of Tagetes Erecta by AI to Quality Control and Bioactivity Prediction The African or Aztec marigold, Tagetes erecta L., is a useful medicinal plant and is characterized by significant compounds such as xanthophylls and polyphenols that are associated with eye benefits and anti-inflammatory outcomes. Conventional drug development mechanisms are a problem because of the high workload and devastating processes. In this paper, the authors examine AI, ML, and DL technologies to improve the pharmaceutics pipeline of T. erecta. The major developments are Non-Destructive Quality Control (NDQC) with Hyperspectral Imaging to perform quantification in real-time, fast drug discovery through DL algorithms that predict bioactivity, and optimization of extraction and formulation with the help of ML algorithms. Successful implementation faces hurdles regarding data quality and ethical governance, necessitating standardized datasets for compounds' chemical and biological properties. Phytochemical ; Tagetes ; Hyperspectral imaging ; Quality (philosophy) ; Pharmaceutics ; Verbascoside ; Polyphenol Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Pharmaceutical Quality and Counterfeiting
Authors: Jing Lan | Hexiao Ding | Hongzhao Chen | Yufeng Jiang | Nga-Chun Ng | Gwing Kei Yip | Guiping Cheng | Yunlin Mao | Jing Cai | D. Lin | Jung Sun Yoo
Source: arXiv (Cornell University) (preprint, 2026-01-21)
Field: Materials Science — Materials Chemistry

DeepMoLM: Leveraging Visual and Geometric Structural Information for Molecule-Text Modeling AI models for drug discovery and chemical literature mining must interpret molecular images and generate outputs consistent with 3D geometry and stereochemistry. Most molecular language models rely on strings or graphs, while vision-language models often miss stereochemical details and struggle to map continuous 3D structures into discrete tokens. We propose DeepMoLM: Deep Molecular Language M odeling, a dual-view framework that grounds high-resolution molecular images in geometric invariants derived from molecular conformations. DeepMoLM preserves high-frequency evidence from 1024 $\times$ 1024 inputs, encodes conformer neighborhoods as discrete Extended 3-Dimensional Fingerprints, and fuses visual and geometric streams with cross-attention, enabling physically grounded generation without atom coordinates. DeepMoLM improves PubChem captioning with a 12.3% relative METEOR gain over the strongest generalist baseline while staying competitive with specialist methods. It produces valid numeric outputs for all property queries and attains MAE 13.64 g/mol on Molecular Weight and 37.89 on Complexity in the specialist setting. On ChEBI-20 description generation from images, it exceeds generalist baselines and matches state-of-the-art vision-language models. Code is available at https://github.com/1anj/DeepMoLM. PubChem ; Language model ; Property (philosophy) ; Folding (DSP implementation) ; Geometric modeling ; Baseline (sea) ; 3D modeling ; Generalist and specialist species ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Multimodal Machine Learning Applications
Authors: Di Wei | Xinyi Pang | Jing Wang | Zhishang Li | Hongxiang Zong | Xing Ding | Turab Lookman | Jun Sun
Source: Acta Materialia (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Data-driven design of Nb-W refractory alloys using Transformer-based stress-strain modeling Deformation (meteorology) ; Workflow ; Refractory metals ; Ultimate tensile strength ; Refractory (planetary science) ; Finite element method ; Material Design Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Additive Manufacturing Materials and Processes
Authors: Shanxian Lin | Yuichi Nagata | Haichuan Yang
Source: arXiv (Cornell University) (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

Associative Constructive Evolution: Enhancing Metaheuristics through Hebbian-Learned Generative Guidance Metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Evolutionary Algorithms (EA) excel at exploring solution spaces but lack mechanisms to accumulate and reuse procedural knowledge from successful search trajectories. This paper proposes Associative Constructive Evolution (ACE), a framework that enhances metaheuristics through learned generative guidance. ACE introduces a Generative Construction Automaton (GCA) -- a probabilistic model over operation sequences -- coupled with the base metaheuristic in a synergistic loop: the metaheuristic explores and provides trajectory samples, while the GCA consolidates successful patterns and guides future exploration. Three mechanisms realize this cooperation: Hebbian weight consolidation that strengthens associations between co-successful operations, guided sampling that biases search toward learned high-quality regions, and symbolic abstraction that extracts frequent patterns into reusable macro-operations. Experiments integrating ACE with EA and PSO on molecular design and maze navigation demonstrate consistent improvements. ACE-PSO achieves a 27.5% increase in success rate while reducing convergence time by 49.6%. In molecular design, ACE-EA improves fitness by 10.1% with 126 chemically interpretable macro-operations automatically discovered. Metaheuristic ; Abstraction ; Generative grammar ; Constructive ; Probabilistic logic ; Particle swarm optimization ; Associative property ; Reuse Machine Learning in Materials Science ; Metaheuristic Optimization Algorithms Research ; Advanced Multi-Objective Optimization Algorithms
Authors: S Kabireal Stephen | Sugumar Mohanasundaram | S.J. Vijay | DS Ebenezer Jacob Dhas | Rajakumar S Rai
Source: Journal of Thermoplastic Composite Materials (article, 2026-02-08)
Field: Materials Science — Polymers and Plastics

High-temperature macro-molecular polymer composites for automotive applications: A critical review of sustainable matrices and performance The growing demand for lightweight, thermally stable materials in the automotive industry has accelerated research on high-temperature polymer matrix composites (HT-PMCs) as alternatives to metals in harsh operating environments. This critical review evaluates the current state of advanced HT-PMCs, emphasizing macro-molecular design, processing strategies, automotive applications, and performance limitations. Thermoset systems such as polyimides, bismaleimides, and polybenzoxazines, together with thermoplastic matrices including PEEK, PPS, and recyclable blends, are examined for their mechanical properties, reinforcement compatibility, and thermal stability in the 200–400°C range. Recent advancements in sustainable matrices—particularly bio-based polyimides and recyclable thermoplastics—are highlighted, with comparative analyses of mechanical, thermal, and environmental performance to identify clear trends and research gaps. Case studies in powertrain components, electrification systems, brake assemblies, under-hood structures, and lightweight panels demonstrate weight reductions exceeding 30% alongside improved thermal management. Critical challenges such as moisture absorption, thermal-oxidative aging, and elevated manufacturing costs are discussed, with attention to trade-offs between thermal stability and recyclability. Emerging solutions include hybrid architectures, nanofiller integrations, bio-derived matrices, and self-healing systems, supported by AI-accelerated material discovery. By integrating sustainability with performance, this review provides a comprehensive roadmap for researchers and engineers advancing the next generation of high-temperature polymer composites for automotive applications. Automotive industry ; Thermosetting polymer ; Powertrain ; Thermal stability ; Durability ; Brake ; Sustainability ; Polymer Synthesis and properties of polymers ; Thermal properties of materials ; Epoxy Resin Curing Processes
Authors: Sampat Singh Tanwar | R.O Das | Deepika Bairagee | Seema Sharma
Source: Current Microwave Chemistry (article, 2026-01-09)
Field: Chemistry — Organic Chemistry

Microwave-Assisted Synthesis of Heterocyclic Scaffolds: A Greener Route to Drug Discovery Introduction/Objectiv: The objective of this review is to explore the role of Micro-wave-Assisted Synthesis (MAS) for the efficient and eco-friendly synthesis of heterocyclic compounds, which are critical scaffolds in drug discovery. The review highlights how MAS aligns with green chemistry principles by reducing reaction time, enhancing yields, and mini-mizing solvent use. It particularly focuses on the synthesis of bioactive heterocycles, including pyrimidines, quinolines, imidazoles, and indoles. Methods: A comprehensive literature review was conducted to analyze studies involving MAS in the synthesis of heterocyclic compounds. The methodology involved comparing microwave-based synthetic protocols with conventional methods based on parameters such as reaction time, yield, solvent usage, selectivity, and scalability. Recent technological advances, including con-tinuous-flow microwave reactors and AI-assisted optimization techniques, were also evaluated for their role in overcoming existing limitations. Results: The studies reviewed consistently demonstrated that MAS significantly reduces reac-tion times (from hours to minutes), increases product yields, and improves selectivity compared to traditional thermal methods. For example, several heterocyclic compounds showed yield im-provements of over 20–30% when synthesized under microwave conditions. Additionally, MAS contributed to reduced solvent consumption, supporting environmentally sustainable practices. However, challenges related to industrial scalability and high equipment costs were noted. Conclusion: Microwave-assisted synthesis represents a powerful, sustainable, and time-effi-cient technique for the synthesis of pharmacologically important heterocycles. Despite limita-tions in scale-up and equipment accessibility, ongoing innovations—such as continuous-flow systems and AI-guided optimization—are addressing these barriers. MAS is expected to become increasingly vital in the future of drug discovery, offering a green alternative to conventional synthesis. Drug discovery ; Yield (engineering) ; Green chemistry ; Scalability ; Process (computing) ; Natural product ; Reaction conditions Microwave-Assisted Synthesis and Applications ; Multicomponent Synthesis of Heterocycles ; Synthesis and biological activity
Authors: Trupti Mohanty | Maitrey Mehta | Hasan M. Sayeed | Bat-El Oded | Itay Pitussi | Arie Borenstein | Vivek Srikumar | Taylor D. Sparks
Source: Integrating materials and manufacturing innovation (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

CrysText: A Generative AI Approach for Text-Conditioned Crystal Structure Generation Using LLM Generative grammar ; Representation (politics) ; Feature (linguistics) ; Pattern recognition (psychology) ; Crystal structure Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Yusu Li | Xinyue Zhao | Jin-ao Duan | Ping Xiao
Source: Coordination Chemistry Reviews (article, 2026-03-11)
Field: Materials Science — Biomaterials

Design mechanisms and biomedical applications of peptide-metal chelates in antimicrobial therapy, tumor theranostics, and integrated diagnosis-treatment systems Rational design ; Cancer treatment ; Design elements and principles ; Cancer therapy ; Drug delivery ; Translational medicine Nanoparticle-Based Drug Delivery ; Metal complexes synthesis and properties ; RNA Interference and Gene Delivery
Authors: Xuan Li | Zhanke Zhou | Zongze Li | Jiangchao Yao | Yu Rong | Lu Zhang | Bo Han
Source: Open MIND (preprint, 2026-03-06)
Field: Materials Science — Materials Chemistry

Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning Large language models (LLMs) benefit substantially from supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) in reasoning tasks. However, these recipes perform poorly in instruction-based molecular optimization, where each data point typically provides only a single optimized reference molecule and no step-by-step optimization trajectory. We reveal that answer-only SFT on the reference molecules collapses reasoning, and RLVR provides sparse feedback under similarity constraints due to the model's lack of effective exploration, which slows learning and limits optimization. To encourage the exploration of new molecules while balancing the exploitation of the reference molecules, we introduce Reference-guided Policy Optimization (RePO), an optimization approach that learns from reference molecules without requiring trajectory data. At each update, RePO samples candidate molecules with their intermediate reasoning trajectories from the model and trains the model using verifiable rewards that measure property satisfaction under similarity constraints in an RL manner. Meanwhile, it applies reference guidance by keeping the policy's intermediate reasoning trajectory as context and training only the answer in a supervised manner. Together, the RL term promotes exploration, while the guidance term mitigates reward sparsity and stabilizes training by grounding outputs to references when many valid molecular edits exist. Across molecular optimization benchmarks, RePO consistently outperforms SFT and RLVR baselines (e.g., GRPO), achieving improvements on the optimization metric (Success Rate $\times$ Similarity), improving balance across competing objectives, and generalizing better to unseen instruction styles. Our code is publicly available at https://github.com/tmlr-group/RePO. Context (archaeology) ; Verifiable secret sharing ; Metric (unit) ; Fidelity ; Term (time) ; Reinforcement learning ; Trajectory ; Similarity (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Hongrui Lin | HeJun WangJia Song | Yafeng Zou | Yiming Huang | Fengting Lv | Shu Wang | G. S. Li
Source: ACS Applied Materials & Interfaces (article, 2026-01-29)
Field: Materials Science — Polymers and Plastics

The Role of Conducting Polymers in Advancing Organoid Technology: A Perspective on Construction Assessment, Monitoring Devices, and Novel Bioregulation Strategies Conducting polymers (CPs) exhibit unique optoelectronic and bioactive properties, enabling the precise monitoring and modulation of complex physiological processes within organoid systems. This review highlights recent advances in CP-enhanced organoid technology, focusing on their roles in evaluating organoid construction, constructing device platforms for physiological monitoring, and enabling novel bioregulation strategies. By enabling high-resolution, noninvasive visualization and precise physiological intervention, CPs provide an ideal platform for the real-time monitoring and regulation of organoid differentiation, functional analysis, and the development of disease treatment strategies. Furthermore, emerging approaches that couple rational molecular design of CPs with artificial intelligence (AI) techniques are accelerating the development of organoid bidirectional interactive systems. These interdisciplinary innovations underscore the transformative potential of CPs to bridge the gap between organoid biology and precision bioengineering, paving the way for the development of clinical diagnostics and treatment tools. Organoid ; Perspective (graphical) ; Biomimetics ; Bridge (graph theory) ; Transformative learning ; Conductive polymer Conducting polymers and applications ; Lipid Membrane Structure and Behavior ; Polydiacetylene-based materials and applications
Authors: Kedeng Wu | Yucheng Zhu | Yan Chen | Bizhu Zhang | Shuyu Liu | Xiaobin Deng | Yabei Wu | Liangliang Zhu | Hang Xiao
Source: arXiv (Cornell University) (preprint, 2026-03-14)
Field: Materials Science — Materials Chemistry

Generative Inverse Design of Cold Metals for Low-Power Electronics Cold metals are a class of metals with an intrinsic energy gap located close to the Fermi level, which enables cold-carrier injection for steep-slope transistors and is therefore promising for low-power electronic applications. High-throughput screening has revealed 252 three-dimensional (3D) cold metals in the Materials Project database, but database searches are inherently limited to known compounds. Here we present an inverse-design workflow that generates 3D cold metals using MatterGPT, a conditional autoregressive Transformer trained on SLICES, an invertible and symmetry-invariant crystal string representation. We curate a training set of 26,309 metallic structures labeled with energy above hull and a unified band-edge distance descriptor that merges p-type and n-type cold-metal characteristics to address severe label imbalance. Property-conditioned generation targeting thermodynamic stability and 50-500 meV band-edge distances produces 148,506 unique candidates; 92.1% are successfully reconstructed to 3D structures and down-selected by symmetry, uniqueness and novelty filters, followed by high-throughput DFT validation. We identify 257 cold metals verified as novel with respect to the Materials Project database, with gaps around the Fermi level spanning 50-500 meV. First-principles phonon, electronic-structure, and work-function calculations for representative candidates confirm dynamical stability and contact-relevant work functions. Our results demonstrate that SLICES-enabled generative transformers can expand the chemical space of cold metals beyond high-throughput screening, providing a route to low-power electronic materials discovery. Electronics ; Transformer ; Stability (learning theory) ; Generative grammar ; Inverse ; Workflow ; Fermi level ; Uniqueness Machine Learning in Materials Science ; Quantum many-body systems ; Topological Materials and Phenomena
Authors: Jinlong Chen | Xingliang He | Xingkao Zhang | Zhijie He | Haoxiang Zhang | Hongying Hou | ju rog | Xiao-Hua Yu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Biomaterials

Deep Potential-Driven Deciphering of Microstructure-Corrosion Relationships in MgNi Alloys Dissolution ; Multiscale modeling ; Corrosion ; Inverse ; Mean squared error ; Material properties Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Shape Memory Alloy Transformations