jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xin‐Yu Gao | Yuanjiu Xiao | Quanxin Peng | Fujian Yang | Daojing Li | Guoqiang Wang | Yu Qian | Jian‐Jun Feng
Source: ChemRxiv (preprint, 2025-06-23)
Field: Chemistry — Organic Chemistry

Rapid and Modular Access to All-Carbon Quaternary Center-Containing 2,2-Disubstituted Bicyclo[1.1.1]pentanes via Cyanocarbene Addition to Bicyclo[1.1.0]butanes While 1,3-disubstituted bicyclo[1.1.1]pentanes (BCPs) have garnered considerable interest in medicinal chemistry as bioisosteres of para-substituted benzenes, the utilization of bridge-functionalized BCPs, especially those containing all-carbon quaternary centers at the bridge-positions, in drug design has lagged behind. This is primarily due to the synthetic challenges associated with these scaffolds. Herein, we report the insertion of diazo-free donor-acceptor carbenes into the C−C bond of bicyclo[1.1.0]butanes (BCBs), enabling the rapid and modular synthesis of 1,2,2,3-tetrasubstituted bicyclo[1.1.1]pentanes (BCPs) that bear three all-carbon quaternary centers, with yields reaching up to 83%. The utilization of easily accessible starting materials, mild reaction conditions, a metal-free one-pot procedure, and a broad substrate scope, along with the discovery that 2,2-disubstituted BCP-decorated bioactive molecules exhibit superior antitumor activity compared to the anticancer drug Sonidegib, render this method highly practical and appealing. Density functional theory (DFT) calculations combined with control experiments reveal that the reaction proceeds through a stepwise nucleophilic ring-opening/recyclization pathway, involving the reaction between a singlet carbene species and a BCB ring. Bicyclic molecule ; Modular design ; Center (category theory) ; Chemistry ; Medicinal chemistry ; Stereochemistry ; Computer science ; Crystallography Cyclopropane Reaction Mechanisms ; Oxidative Organic Chemistry Reactions ; Synthetic Organic Chemistry Methods
Authors: Zhiyun Li | Tianyi Gao | Shirong Wang | Sheng Bi | Rulin Feng | Zhenyu Zhu | Yilin Zhao | Wenjie Yan | Lingyue Yu | Qirui Gao | Zihan Lin | Jianming Wu | Igor Ying Zhang | Xin Xu
Source: Chinese Journal of Chemical Physics (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

REST: Embracing the rust programming language for modern electronic structure theory REST (Rust-based electronic structure toolkit) is a modern open-source electronic structure code entirely written in Rust, combining high performance, memory safety, and expressive concurrency. As a community-driven project, its source code is freely available at https://gitee.com/restgroup, fostering open collaboration and transparent development. It supports a wide range of density functional methods—from local density approximation (LDA), generalized gradient approximation (GGA), meta-GGA, and hybrids to doubly hybrids, as well as machine learning-augmented functionals—enabling high-accuracy simulations with low computational overhead. Its “diskfree” RI-based (RI: resolution-of-the-identity) implementation and efficient shared-memory parallelism (via Rayon) ensure rapid calculations even for challenging systems. REST also offers unique user support through large language model-assisted input generation and developer-friendly tensor libraries for rapid algorithm prototyping. Range (aeronautics) ; Code (set theory) ; Tensor (intrinsic definition) ; Electronic structure ; Parallelism (grammar) ; Density functional theory Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Madeleine A. Gaidimas | Abhijoy Mandal | Pan Chen | Shi Xuan Leong | G. C. Kim | Akshay Talekar | Kent O. Kirlikovali | Kourosh Darvish | Omar K. Farha | Varinia Bernales | Alan Ramayee Aspuru-Guzik
Source: Digital Discovery (article, 2025-12-23)
Field: Chemistry — Inorganic Chemistry

Computer vision for high-throughput materials synthesis: a tutorial for experimentalists Computer vision enables the rapid identification of chemical phases, such as solid metal–organic framework (MOF) materials, from images of sample vials. Identification (biology) ; Sample (material) ; Machine vision ; Image processing ; Feature (linguistics) Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Qian Wang | Ryuhei Sato | Regina García-Méndez | Woosun Jang | Pengfei Ou | Aloysius Soon | Jie Zhao | Xiaonan Wang | Shin-ichi Orimo | Eric Jianfeng Cheng
Source: AI Agent (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

AI agents for solid electrolytes: opportunities, challenges, and future directions Artificial intelligence (AI) and autonomous agents are transforming the discovery and optimization of solid electrolytes, a class of materials crucial to the safety and performance of next-generation batteries. This review summarizes recent progress in integrating machine learning, molecular dynamics, and density functional theory within closed-loop or semi-autonomous workflows that accelerate the evaluation of ionic conductivity, electrochemical and chemical stability, and processability. Data-driven frameworks now accelerate the screening of sulfides, oxides, and halides, while phase-field and multiscale models have provided mechanistic insight into dendrite formation, interfacial degradation, and chemo-mechanical coupling. Autonomous laboratories that combine robotic synthesis, in situ characterization, and Bayesian optimization further enable closed-loop experimental discovery. Despite this progress, challenges remain in data quality, model interpretability, and the limited autonomy of current systems. Future development will rely on five key directions: (1) constructing interoperable multiscale databases, (2) developing explainable and data-efficient algorithms, (3) tightly integrating computation with experiment, (4) exploring new solid-electrolyte chemistries via agent-driven optimization, and (5) fostering coordinated global collaboration among open AI agents. Together, these developments mark a transition from empirical discovery to an integrated, self-improving research paradigm, where AI evolves from a predictive assistant into an active collaborator that learns, reasons, and supports materials innovation alongside human researchers. Workflow ; Interoperability ; Key (lock) ; Bayesian optimization ; Automation ; Open research Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Yuze Hao | Lamberto Duò | Jinlu He
Source: ChemRxiv (preprint, 2025-08-04)
Field: Materials Science — Materials Chemistry

Autonomous Materials Synthesis Laboratories: Integrating Artificial Intelligence with Advanced Robotics for Accelerated Discovery This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials Acceleration Platforms (MAPs) aim to reduce this to 1-2 years through closed-loop systems combining physical experimentation with computational intelligence. The review analyzes critical components including robotic hardware architectures, integrated analytical instrumentation, closed-loop optimization strategies, AI-driven decision-making frameworks, and multi-agent systems for laboratory coordination. Case studies demonstrate remarkable success across various domains: nanomaterials synthesis, inorganic materials exploration, electrocatalyst discovery, and reaction mechanism elucidation. Large language models have recently enhanced these systems by improving knowledge extraction, experimental planning, and multi-agent coordination. The integration of standardized data formats, accessible materials databases, and sophisticated knowledge representation frameworks further accelerates discovery while enhancing reproducibility. This transformative paradigm represents a fundamental reimagining of materials science methodology, shifting from human-guided exploration to AI-orchestrated discovery campaigns. Robotics ; Artificial intelligence ; Engineering ; Computer science ; Systems engineering ; Manufacturing engineering ; Robot Machine Learning in Materials Science
Authors: Ruiliang Zhou | Hailong Liu | Ivan S. Babichuk | Yurii A. Romaniuk | Anton Tiutiunnyk | Jianan Zhang | Yan Pu | Zisen Zhou | D. Laroze | Jian Yang
Source: Computational Materials Science (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

A lightweight model and multi-agent system for layer identification in two-dimensional materials Layer (electronics) ; Identification (biology) ; Materials science ; Nanotechnology ; Computer science ; Biology 2D Materials and Applications ; Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Simha Sridharan | Tom Bailey | Agnese Marcato | Elena Simone | Nicholas J. Watson
Source: Current Opinion in Colloid & Interface Science (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Artificial intelligence in colloid and interface science: Current research, challenges and future directions Artificial intelligence (AI) and Machine learning (ML) are transforming colloid and interface science by enabling predictive modelling, autonomous experimentation, and accelerated material design. This review highlights recent advances organised in four topics: (1) prediction of basic physical properties; (2) image analysis; (3) process design, monitoring and optimisation; and (4) morphology and phase behaviour prediction. AI models have improved the prediction accuracy of interfacial tension, critical micelle concentration, foam stability, and complex structure–function relationships, in particular, integrated generative AI approaches support the design of new surfactants and emulsifiers. Image analysis has automated microstructural characterisation and enabled real-time quality control, while AI-enhanced process design has delivered digital twins, closed-loop optimisation, and sustainability-oriented workflows. Morphology and phase behaviour prediction has combined simulation-driven neural networks with generative approaches to accelerate material discovery. The future of AI applications in colloids will be shaped by experimental database design and standardisation, hybrid AI methods integrating physics and surrogate modelling, and AI agents leveraging large language models for literature mining, data curation, and experimental optimisation. Together, these developments promise to establish data-rich, physics informed, and increasingly autonomous research ecosystems for colloids and interface science, accelerating material understanding and design. • AI and ML allow prediction of key physical and thermodynamic properties of colloids • Image-based ML is a powerful tool for automatic microstructural characterisation • AI-enhanced process design and monitoring can support sustainable manufacturing • Data standardisation, accessibility and interpretability remain key challenges Interpretability ; Interface (matter) ; Process (computing) ; Generative grammar ; Artificial neural network ; Key (lock) Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yao Jia | Yuehui Xian | Yangyang Xu | Pengfei Dang | Xiangdong Ding | Jun Sun | Yumei Zhou | Dezhen Xue
Source: Research Square (preprint, 2025-05-12)
Field: Materials Science — Materials Chemistry

Universal Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery Inference ; Computer science ; Data science ; Artificial intelligence Machine Learning in Materials Science
Authors: Nik Reeves‐McLaren | Sarah Moth-Lund Christensen
Source: Journal of Materials Chemistry A (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Data integrity in materials science in the era of AI: balancing accelerated discovery with responsible science and innovation Artificial intelligence promises to revolutionise materials discovery through accelerated prediction and optimisation, yet this transformation brings critical data integrity challenges that threaten the scientific record. Scientific integrity ; Data integrity ; Scientific discovery ; Research integrity ; Big data ; Data collection Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Teruyasu Mizoguchi
Source: Microscopy (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Data-driven ELNES/XANES analysis: predicting spectra, unveiling structures and quantifying properties Core-loss spectroscopies using electrons and X-rays, such as electron energy loss near-edge structures (ELNES) and X-ray absorption near-edge structures (XANES), are indispensable tools for materials characterization and development. These techniques provide detailed insights into atomic environments, chemical bonding, and vibrational properties that underpin material functionality. Traditionally, ELNES/XANES analyses have relied on qualitative interpretation or comparisons with reference spectra obtained from experiments and/or simulations. Recent advances in data-driven approaches, however, have enabled more quantitative and predictive use of these spectra. This review highlights newly developed data-driven methodologies that extend far beyond conventional ELNES/XANES analysis. These approaches accelerate ELNES/XANES simulations, enable the extraction of radial distribution functions, and quantify multiple material properties directly from spectral data. To enhance the interpretability of machine learning (ML) predictions, sensitivity analysis is employed to elucidate the relationships between specific spectral features and target properties. The rapid growth of open materials databases, coupled with increasingly powerful ML models, has further fueled these developments. Together, these advances would point to a future in which automated, interpretable and scalable spectroscopy serves as a central driver for deeper understandings and accelerated materials discovery. Interpretability ; Characterization (materials science) ; Interpretation (philosophy) ; Identification (biology) ; Point (geometry) ; Scalability Machine Learning in Materials Science
Authors: Dongfang Yu | Sean D. Griesemer | Tzu‐chen Liu | Chris Wolverton | Yizhou Zhu
Source: npj Computational Materials (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Automated phase mapping of high-throughput X-ray diffraction data encoded with domain-specific materials science knowledge Abstract Combinatorial synthesis and high-throughput characterization have become powerful tools to accelerate the discovery and design of novel materials. Correctly extracting information about the constituent phases and gaining materials insight from high-throughput X-ray diffraction data of combinatorial libraries is a crucial step in establishing the composition–structure–property relationship. Basic information includes the number, identity, and fraction of present phases in all the samples, while advanced information includes the lattice change, texture information, solid solution behavior, etc. Encoding domain-specific knowledge, such as crystallography, X-ray diffraction, thermodynamics, kinetics, and solid-state chemistry, into automated algorithms is crucial for the development of automated phase mapping algorithms. In this study, we present an unsupervised optimization-based solver to tackle the phase mapping challenge in high-throughput X-ray diffraction datasets. Besides leveraging robust fitting abilities of neural-network optimization algorithms, we integrated various material information, including first-principles calculated thermodynamic data, crystallography, X-ray diffraction, and texture into our automated solver. Our approach exhibits robust performance across multiple experimental datasets. We emphasize the importance of correctly integrating material information for automated solvers, contributing to the development of future automated characterization tools. Characterization (materials science) ; Solver ; Diffraction ; Phase (matter) ; Encoding (memory) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Abi Cit | Billy Elly | Dastgir Alam
Source: Preprints.org (preprint, 2025-02-12)
Field: Materials Science — Materials Chemistry

AI-Driven Optimization of Drug Synthesis Pathways The optimization of drug synthesis pathways is a critical challenge in pharmaceutical research, requiring efficient strategies to enhance yield, reduce costs, and minimize environmental impact. Artificial Intelligence (AI) has emerged as a transformative tool in this domain, leveraging machine learning, reinforcement learning, and generative models to predict optimal reaction conditions, streamline multi-step synthesis, and identify novel synthetic routes. This study explores AI-driven methodologies for optimizing drug synthesis pathways, focusing on data-driven retrosynthetic analysis, reaction prediction models, and high-throughput screening simulations. By integrating AI with cheminformatics and quantum chemistry simulations, the research aims to accelerate the drug development process, improve reaction efficiency, and reduce reliance on trial-and-error experimentation. The findings highlight the potential of AI in revolutionizing pharmaceutical synthesis, ultimately leading to more sustainable and cost-effective drug production. Computer science ; Cheminformatics ; Transformative learning ; Biochemical engineering ; Artificial intelligence ; Machine learning ; Reinforcement learning ; Generative grammar ; Process (computing) ; Drug discovery ; Risk analysis (engineering) ; Management science ; Data science ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Adekunle Babajide Rowaiye | Abiodun Abidemi Folarin | Tobilola Akingbade | Joel Okoli | Oluwabukunmi Ifedamola Rowaiye | Temitope R. Folorunso | Doofan Bur
Source: Discover Chemistry. (article, 2025-11-16)
Field: Materials Science — Materials Chemistry

Advancing predictive modeling in computational chemistry through quantum chemistry, molecular mechanics, and machine learning Computational chemistry plays a critical role in advancing molecular science by bridging theoretical frameworks and experimental observations. It provides detailed insight into the structural, electronic, and reactive properties of molecules and materials. This article examines recent developments that are influencing the direction of the field, with a focus on the integration of quantum chemistry (QC), molecular mechanics (MM), and machine learning (ML) into cohesive modeling strategies. The objective is to assess how these combined approaches are improving the accuracy of simulations, informing molecular design, and contributing to progress in areas such as drug discovery, catalysis, and materials engineering. The review covers core methodologies including ab initio quantum mechanical calculations, hybrid quantum mechanics/molecular mechanics (QM/MM) models, and classical MD techniques. It also discusses emerging advances in data-driven models and neural network-based potentials. More attention is given to recent studies (2018–2025) that demonstrate how these techniques complement one another in addressing complex chemical systems. The analysis shows that combining quantum methods with ML enhances electronic structure predictions, while molecular mechanics provides efficient, large-scale modeling of structural and energetic properties across diverse environments, especially when coupled with simulation techniques such as molecular dynamics or Monte Carlo. In synergy, these tools support the construction of more robust and scalable models, narrowing the gap between computational results and laboratory findings. The article concludes by outlining the expanding influence of integrated computational approaches and their potential to drive innovation across scientific disciplines. Bridging (networking) ; Computational model ; Quantum ; Molecular dynamics ; Complement (music) ; Computational mechanics ; Ab initio ; Multiscale modeling Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Li Fu | Qingwei Zhou | Meiqing Jin | Weihong Wu
Source: Environmental chemistry and safety (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Large Language Models as Spectrographic Assistants: Opportunities and Challenges in Laboratory Data Analysis Background: The integration of large language models (LLMs) into analytical chemistry has emerged as a pivotal innovation, offering transformative solutions to handle complex analytical workflows and exponentially growing datasets. This review critically examines the capabilities, applications, and limitations of LLMs in advancing spectral interpretation, predictive modeling, and experimental automation within the field. Methods: Through a systematic analysis of recent literature and case studies, we evaluate LLMs’ performance in processing multimodal data (e.g., spectral, textual, and numerical inputs), their adaptability to domain-specific tasks, and their synergy with traditional computational tools. Key applications include automated spectral analysis, environmental monitoring, and real-time decision support. Results: LLMs demonstrate remarkable versatility in tasks such as chemical state identification from spectra and predictive modeling of environmental parameters, achieving parity with conventional machine learning models in accuracy. However, their deployment is constrained by challenges in contextual reasoning, data security, and interpretability, necessitating hybrid human-AI frameworks for validation. Conclusions: This review underscores the importance of domain-specific optimization, robust validation protocols, and ethical governance to harness LLMs’ potential fully. Future efforts should prioritize interdisciplinary collaboration to refine LLM architectures, enhance interoperability, and address scalability in real-world scientific applications. Computer science ; Data science Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling