jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Glenn K. K. Clothier | Daniel Taton | Simon Harrisson
Source: ACS Polymers Au (article, 2025-09-17)
Field: Materials Science — Biomaterials

Exploiting Machine Learning and Automated Synthesis in Continuous Flow for Process Optimization of the Organocatalyzed Ring-Opening Polymerization of <scp>l</scp>-Lactide The ring-opening polymerization (ROP) of lactide is a well-established route for the synthesis of polylactide (PLA), a degradable and biobased polymer with applications in biomedical materials, packaging, and additive manufacturing. However, optimizing reaction conditions for efficient PLA synthesis remains challenging, with its production currently dwarfed by traditional petrochemical derived commodity polymers. In this work, we employ a continuous flow reactor to systematically explore a reaction space defined by catalyst concentration, residence time, and initiator concentration for the organocatalyzed ROP of l-lactide performed at room temperature, using 1,8-diaza-bicyclo​[5.4.0]​undec-7-ene (DBU) as catalyst, benzyl alcohol as initiator and dichloromethane as solvent. Through high-throughput experimentation coupled with online characterization, a robust data set was generated and processed with a Kernel-Based Regularized Least Squares (KRLS) model to capture system kinetics and the dependencies between initial conditions and polymer characteristics. Multiobjective Pareto optimization was subsequently used to identify conditions that maximize polymer production rate, and these conditions were experimentally validated to confirm the model accurately predicts optimal reaction conditions. Pareto-optimized parameters yield a high conversion and well-controlled PLA with low dispersities. This study highlights the advantages of continuous flow polymerization for precise control over reaction kinetics and demonstrates the potential of machine-learning-assisted optimization for efficient and scalable PLA-based materials' exploration. Polymerization ; Flow chemistry ; Petrochemical ; Continuous flow ; Yield (engineering) ; Step-growth polymerization ; Lactide ; Process (computing) biodegradable polymer synthesis and properties ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Polymer Synthesis and Characterization
Authors: Rigoberto Advincula | Ilia N. Ivanov | Rama K. Vasudevan | Rajeev Kumar | Panagiotis Christakopoulos | Marileta Tsakinika | Jihua Chen | Jan‐Michael Y. Carrillo | Qinyu Zhu | Bobby G. Sumpter
Source: Faraday Discussions (article, 2025-08-12)
Field: Materials Science — Biomaterials

Harnessing data and control with AI/ML-driven polymerization and copolymerization Creating and curating new data to augment heuristics is a forthcoming approach to materials science in the future. Highly improved properties are advantageous even with "commodity polymers" that do not need to undergo new synthesis, high-temperature processes, or extensive reformulation. With artificial intelligence and machine learning (AI/ML), optimizing synthesis and manufacturing methods will enable higher throughput and innovative directed experiments. Simulation and modeling to create digital twins with statistical and logic-derived design, such as the design of experiments (DOE), will be superior to trial-and-error approaches when working with polymer materials. This paper describes and demonstrates protocols for understanding hierarchical approaches in optimizing the polymerization and copolymerization process <i>via</i> AI/ML to target specific properties, using model monomers such as styrene and acrylate. The key is self-driving continuous flow chemistry reactors with sensors (instruments) and real-time ML with an online monitoring set-up that allows a feedback loop mechanism. We provide initial results using ML refinement of the classical Mayo-Lewis equation (MLE), time-series data, and an autonomous flow reactor system build-up as a future data-generating station. More importantly, it lays the ground for precision control of the copolymerization process. In the future, it should be possible to undertake collaborative human-AI-guided protocols for the autonomous fabrication of new polymers guided by literature and available data sources targeting new properties. Copolymer ; Polymerization ; Polymer science ; Polymer chemistry ; Computer science ; Materials science ; Chemistry ; Polymer ; Organic chemistry biodegradable polymer synthesis and properties ; Machine Learning in Materials Science
Authors: Rick S. van den Hurk | Bob W.J. Pirok | Tijmen S. Bos
Source: Chromatographia (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives The application of artificial intelligence (AI) and machine learning (ML) is rapidly expanding and has begun to make a significant impact on polymer development and characterization. This perspective article explores the current state of AI in this field and highlights areas where its potential remains underutilized. While the optimization of polymer synthesis to achieve desired properties and the classification of polymer types are well-established, opportunities for AI integration in detailed characterization, analytical method development, and data processing remain largely untapped. Greater automation of the analytical laboratory, whether through dedicated algorithms or AI-driven solutions, will enable analytical chemists to focus more on addressing research questions and interpreting results, rather than on method development and routine measurements. Characterization (materials science) ; Automation ; Artificial intelligence ; Field (mathematics) ; Applications of artificial intelligence ; Focus (optics) ; Perspective (graphical) ; Nanotechnology ; Computer science ; Management science ; Biochemical engineering ; Data science ; Chemistry ; Engineering ; Mechanical engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Eunjae Shim | Ambuj Tewari | Tim Cernak | Paul M. Zimmerman
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Recommending reaction conditions with label ranking Pinpointing effective reaction conditions can be challenging, even for reactions with significant precedent. Herein, models that rank reaction conditions are introduced as a conceptually new means for prioritizing experiments, distinct from the mainstream approach of yield regression. Specifically, label ranking, which operates using input features only from substrates, will be shown to better generalize to new substrates than prior models. Evaluation on practical reaction condition selection scenarios - choosing from either 4 or 18 conditions and datasets with or without missing reactions - demonstrates label ranking's utility. Ranking aggregation through Borda's method and relative simplicity are key features of label ranking to achieve consistent high performance. Ranking (information retrieval) ; Simplicity ; Computer science ; Information retrieval ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Zihao Wang | Teng Zhou
Source: Molecular Systems Design & Engineering (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Computer-aided metal–organic framework screening and design approaches toward efficient carbon capture processes Computer-aided screening and design of metal–organic frameworks for carbon capture shift to closed-loop, optimization-guided discovery with incorporation of process-level considerations. Computer science ; Metal-organic framework ; Process engineering ; Computer architecture ; Nanotechnology ; Chemistry ; Engineering ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Carbon Dioxide Capture Technologies
Authors: Thomas Pickles | Youcef Leghrib | Matt Weisshaar | Marina V. Goncharuk | Peter Timperman | Timothy J. Doherty | David D. Ford | Jonathan Moores | Alastair J. Florence | Cameron J. Brown
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Automated scale-up crystallisation DataFactory for model-based pharmaceutical process development: a Bayesian case study This study presents the development and use of an automated model-based design of experiments platform for crystallisation process scale-up, integrating IoT-enabled hardware, in-line analytics, and a data-driven experimental planning approach. Process (computing) ; Process development ; Scale (ratio) ; Computer science ; Bayesian probability ; Artificial intelligence ; Process engineering ; Engineering ; Programming language ; Physics Crystallization and Solubility Studies ; Process Optimization and Integration
Authors: Mohammad Amin Sadeghi | Runze Zhang | Jason Hattrick‐Simpers
Source: The Journal of Open Source Software (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

AutoEIS: Automated equivalent circuit modeling from electrochemical impedance spectroscopy data using statistical machine learning Sadeghi et al., (2025). AutoEIS: Automated equivalent circuit modeling from electrochemical impedance spectroscopy data using statistical machine learning. Journal of Open Source Software, 10(109), 6256, https://doi.org/10.21105/joss.06256 Dielectric spectroscopy ; Equivalent circuit ; Electrical impedance ; Computer science ; Electrochemistry ; Data mining ; Pattern recognition (psychology) ; Artificial intelligence ; Electrical engineering ; Chemistry ; Engineering ; Voltage Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrochemical Analysis and Applications
Authors: Kévin Alhada–Lahbabi | Damien Deleruyelle | Brice Gautier
Source: ACS Applied Electronic Materials (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Reinforcement Learning-Assisted Ferroelectric Domain Wall Design Using a Machine Learning Phase-Field Surrogate Precise manipulation of ferroelectric domain walls (DWs) has garnered increasing interest for applications in DW memory devices. Although recent advancements in scanning probe microscopy-based automated experiments have improved tip control efficiency, achieving real-time optimization of the tip trajectory to configure arbitrary domain structures remains challenging. In this study, we introduce a reinforcement learning (RL) framework for autonomous DW manipulation, leveraging a 3-D machine learning phase-field surrogate model to accelerate environment dynamics. The RL agent is trained to optimize the tip trajectory, enabling it to achieve target DW configurations within both single-goal and goal-augmented RL frameworks while effectively generalizing across a range of target structures. This framework offers a promising approach for facilitating the real-time design of 2-D ferroelectric DWs. Reinforcement learning ; Surrogate model ; Ferroelectricity ; Reinforcement ; Computer science ; Artificial intelligence ; Materials science ; Machine learning ; Optoelectronics ; Composite material Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Piezoelectric Actuators and Control
Authors: Tong Xie | Yuwei Wan | Yixuan Liu | Yuchen Zeng | Shaozhou Wang | Wenjie Zhang | Clara Grazian | Chunyu Kit | Wanli Ouyang | D.C. Zhou | Bram Hoex
Source: Research Square (preprint, 2025-07-07)
Field: Materials Science — Materials Chemistry

Large Language Models as Materials Science Adapted Learners Mathematics education ; Computer science ; Linguistics ; Psychology ; Philosophy Machine Learning in Materials Science
Authors: Pengcheng Hu | Jilu Zheng | Hongjuan Wang | Yongxin Li | Tao Ye | Quanjun Li | Xiaopeng Lan | Chunzhao Liu | Chunlei Liu
Source: International Journal of Nanomedicine (review, 2025-02-01)
Field: Materials Science — Materials Chemistry

Supramolecular Nanozymes Based on Self-Assembly of Biomolecule for Cancer Therapy Natural enzyme systems possess extraordinary functions and characteristics, making them highly appealing for use in eco-friendly technologies and innovative cancer treatments. However, their inherent instability and structural complexity often limit their practical applications, leading to the exploration of biomolecular nanozyme alternatives. Supramolecular nanozymes, constructed using self-assembly techniques and various non-covalent interactions, have emerged as a promising solution. Amino acids, peptides, and protein motifs offer flexible building blocks for constructing these nanozymes. Importantly, the well-defined structural regulation mechanisms of biomolecular nanozymes, along with their unique properties as fundamental biological modules in living systems-such as selectivity, permeability, retention, and biocompatibility-present new opportunities for cancer therapy. This review highlights recent advances in supramolecular self-assembled nanozymes, including peroxidases, oxidases, catalases, superoxide dismutases, and other nanozyme systems, as building blocks for tumor therapy. Additionally, it discusses precise functional modulation through supramolecular non-covalent interactions and their therapeutic applications in targeting the tumor microenvironment. These studies provide valuable insights that may inspire the design of novel supramolecular nanozymes with enhanced catalytic selectivity, biocompatibility, and tumor-killing efficacy. Cancer therapy ; Nanotechnology ; Biomolecule ; Supramolecular chemistry ; Cancer ; Cancer imaging ; Materials science ; Self-assembly ; Chemistry ; Medicine ; Molecule Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Rui Ye | Yupei Zhang | Wan Xu | Li Lai | Zhongwei Zhang | Yan Chen | Shugang Qin
Source: Theranostics (review, 2025-11-07)
Field: Materials Science — Biomaterials

AI and organoid platforms for brain-targeted theranostics Developing therapies for complex brain diseases faces significant challenges due to biological complexity and the stringent blood-brain barrier. While nanomedicine holds promise, traditional R&D paradigms suffer from inefficiency. This review introduces an intelligent theranostic paradigm that integrates high-fidelity brain organoid models, high-throughput screening (HTS/HCS), and Artificial Intelligence (AI). In this closed-loop workflow, organoid platforms serve a diagnostic role, generating predictive data on nanomedicine performance. AI then provides therapeutic guidance by processing this data to drive rational drug design, synthesis, and interaction prediction. This AI-driven convergence is poised to significantly accelerate the development of precisely targeted and individualized nanomedicines, offering new hope for breakthroughs in treating brain diseases. Organoid ; Nanomedicine ; Drug discovery ; Personalized medicine ; Drug development Nanoparticle-Based Drug Delivery ; Chemical Reactions and Isotopes ; Computational Drug Discovery Methods
Authors: Rion Brattig Correia | Jordan C. Rozum | Leonard E. Cross | Jack Felag | Michael Gallant | Ziqi Guo | Bruce W. Herr | Aehong Min | Jon Sánchez-Valle | Deborah Stungis Rocha | Alfonso Valencia | Xuan Wang | Katy Börner | Wendy R. Miller | Luís M. Rocha
Source: Journal of the American Medical Informatics Association (article, 2025-01-17)
Field: Chemistry — Inorganic Chemistry

<i>myAURA</i> : a personalized health library for epilepsy management via knowledge graph sparsification and visualization Our stakeholder-driven, scalable approach to integrating traditional and nontraditional data sources enables both clinical discovery and data-powered patient self-management in epilepsy and can be generalized to other chronic conditions. Computer science ; Visualization ; Scalability ; Stakeholder ; Epilepsy ; Graph ; Inference ; Data science ; Information retrieval ; Data mining ; Artificial intelligence ; Medicine ; Database ; Theoretical computer science Asymmetric Hydrogenation and Catalysis ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Seyedeh Fatemeh Nabavi | Hamid Garmestani | Faramarz Fekri
Source: Journal of Manufacturing Processes (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

AI-powered language models for alloy design and laser-based manufacturing: A review of NLP applications in materials science Materials informatics ; Bridging (networking) ; Process (computing) ; Modeling language ; Key (lock) ; Computational model ; Process modeling ; Natural language ; Informatics Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Bing Ma | Na Qin | Qianqian Yan | Wei Zhou | Sheng Zhang | Xiao Wang | Lipiao Bao | Xing Lu
Source: Digital Discovery (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Advancing metal organic framework and covalent organic framework design <i>via</i> the digital-intelligent paradigm AI and machine learning combined with multiscale simulations accelerate framework materials design. This review summarizes AI-assisted strategies for synthesis prediction, condition optimization, and inverse functional design. Metal-organic framework ; Conceptual framework ; Context (archaeology) ; Key (lock) ; Covalent organic framework Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Dong Jin | Yuan Liang | Zihao Xiong | Xiaojie Yang | Haifeng Wang | Jie Zeng | Shuang‐Xi Gu
Source: Molecules (review, 2025-01-23)
Field: Materials Science — Materials Chemistry

Application of Transformers to Chemical Synthesis Efficient chemical synthesis is critical for the production of organic chemicals, particularly in the pharmaceutical industry. Leveraging machine learning to predict chemical synthesis and improve the development efficiency has become a significant research focus in modern chemistry. Among various machine learning models, the Transformer, a leading model in natural language processing, has revolutionized numerous fields due to its powerful feature-extraction and representation-learning capabilities. Recent applications demonstrated that Transformer models can also significantly enhance the performance in chemical synthesis tasks, particularly in reaction prediction and retrosynthetic planning. This article provides a comprehensive review of the applications and innovations of Transformer models in the qualitative prediction tasks of chemical synthesis, with a focus on technical approaches, performance advantages, and the challenges associated with applying the Transformer architecture to chemical reactions. Furthermore, we discuss the future directions for improving the applications of Transformer models in chemical synthesis. Transformer ; Computer science ; Retrosynthetic analysis ; Architecture ; Machine learning ; Artificial intelligence ; Biochemical engineering ; Engineering ; Chemistry ; Electrical engineering ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Hong Jiang | Chao Jiang | Xiangxiang Zhao | Yan Liu | Yong Cui
Source: Transformative Chemistry (article, 2025-11-30)
Field: Chemistry — Inorganic Chemistry

Crystallizing Chiral Molecular Catalysts for Heterogeneous Asymmetric Catalysis ABSTRACT The development of chiral heterogeneous catalysts that combine high activity, stability, and stereoselectivity remains a central challenge in asymmetric catalysis. Traditional strategies that immobilize chiral molecular catalysts on amorphous supports typically suffer from non‐uniform active site distribution, poorly defined local environments, and catalyst leaching, which collectively compromise stereocontrol and long‐term stability. Crystallizing chiral molecular catalysts within metal–organic frameworks (MOFs) offers a transformative solution. The inherent crystallinity of MOFs ensures deterministic positioning and uniform distribution of catalytic sites, effectively propagating chirality throughout the framework. Site isolation stabilizes active species and prevents side reactions, whereas confined chiral pockets orient substrates for precise transition‐state control. Moreover, the modular design of MOFs allows fine‐tuning of pore structure and functionality, enabling selective diffusion, cooperative catalysis, and tandem reactions. This perspective outlines strategies for crystallizing chiral molecular catalysts in MOFs and highlights the structural and functional advantages of chiral MOFs (CMOFs) over homogeneous and conventional heterogeneous systems, aiming to inspire innovative CMOF‐based catalysts for sustainable synthesis of high‐value chemicals and pharmaceuticals. We hope this perspective will deepen readers' understanding of CMOF chemistry and inspire the development of innovative CMOF‐based heterogeneous catalysts for the sustainable synthesis of high‐value chemicals and pharmaceuticals. Catalysis ; Chirality (physics) ; Enantioselective synthesis ; Heterogeneous catalysis ; Stereocenter ; Homogeneous Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Multicomponent Synthesis of Heterocycles
Authors: Rongguo Fu | Tianhao Su | Musen Li | Yue Wu | Runhai Ouyang | Danica Solina | Michael Cortie | Tong‐Yi Zhang | Shunbo Hu | Zhongming Ren
Source: Communications Physics (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Physics-guided deep learning strategy for 2D structure reconstruction from diffraction patterns Two-dimensional (2D) materials have garnered significant attention due to their tunable electronic and optical properties and exceptional mechanical performance. Reconstructing 2D structures from diffraction patterns without prior assumptions or comprehensive knowledge is challenging, especially for heterogeneous stacking and quantum 2D materials. Here, we introduce DD2D (diffraction pattern deep-reconstruction 2D structures), a physics-guided deep learning method that predicts 2D structures directly from diffraction patterns. DD2D employs a twin-tower framework, integrating a crystallographic geometric encoder and a site texture encoder, and uses a self-attention mechanism to identify intrinsic correlations in physical information and corresponding areas in the diffraction pattern. The results demonstrate high anti-interference, robust recognition capabilities, reliable interpretability, and prediction accuracy of up to 99.0%, highlighting its potential for future 2D materials discoveries. The authors introduce DD2D, a physics-guided deep learning method that predicts 2D structures directly from diffraction patterns using a twin-tower framework. The method demonstrates high anti-interference, robust recognition, and up to 99.0% prediction accuracy, showing promise for future 2D materials discoveries. Diffraction ; Deep learning ; Artificial intelligence ; Mathematics education ; Physics ; Psychology ; Computer science ; Optics Machine Learning in Materials Science ; Geophysical Methods and Applications ; Medical Imaging Techniques and Applications