jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew Ferguson | Marisa LaFleur | Lars Ruthotto | J. J. Thaler | Yuan-Sen Ting | Pratyush Tiwary | Soledad Villar
Source: Machine Learning Science and Technology (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

The future of artificial intelligence and the mathematical and physical sciences (AI+MPS) <sup>*</sup> Abstract This community paper developed out of the National Science Foundation Workshop on the Future of artificial intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community’s perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS. Transformative learning ; Workforce ; Position paper ; Workforce development ; Snapshot (computer storage) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Genetics, Bioinformatics, and Biomedical Research
Authors: Yichao Zhou | Siyu Hu | Xiangyu Zhang | Hongyu Wang | Guangming Tan | Weile Jia
Source: ArXiv.org (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials Foundation MLIPs demonstrate broad applicability across diverse material systems and have emerged as a powerful and transformative paradigm in chemical and computational materials science. Equivariant MLIPs achieve state-of-the-art accuracy in a wide range of benchmarks by incorporating equivariant inductive bias. However, the reliance on tensor products and high-degree representations makes them computationally costly. This raises a fundamental question: as quantum mechanical-based datasets continue to expand, can we develop a more compact model to thoroughly exploit high-dimensional atomic interactions? In this work, we present MatRIS (\textbf{Mat}erials \textbf{R}epresentation and \textbf{I}nteraction \textbf{S}imulation), an invariant MLIP that introduces attention-based modeling of three-body interactions. MatRIS leverages a novel separable attention mechanism with linear complexity $O(N)$, enabling both scalability and expressiveness. MatRIS delivers accuracy comparable to that of leading equivariant models on a wide range of popular benchmarks (Matbench-Discovery, MatPES, MDR phonon, Molecular dataset, etc). Taking Matbench-Discovery as an example, MatRIS achieves an F1 score of up to 0.847 and attains comparable accuracy at a lower training cost. The work indicates that our carefully designed invariant models can match or exceed the accuracy of equivariant models at a fraction of the cost, shedding light on the development of accurate and efficient MLIPs. Equivariant map ; Exploit ; Scalability ; Invariant (physics) ; Range (aeronautics) ; Tensor (intrinsic definition) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Kai Li | Shan Yang | Longhui Duan | Zhenhua Gu
Source: Nature Communications (article, 2026-01-24)
Field: Chemistry — Organic Chemistry

Atroposelective interrupted CuAAC reaction using cyclic diaryliodoniums Copper-catalyzed azide-alkyne cycloaddition (CuAAC) is a pivotal strategy for joining two fragments, including bioactive moieties under mild conditions. In recent years, "interrupted" CuAAC reaction has emerged by intercepting copper triazolide intermediates, thereby significantly broadening the scope of these transformations. However, asymmetric transformations based on the interruption of copper triazolide intermediates have remained elusive. In this work, we present an efficient and mild approach that intercepts a copper triazolide intermediate with a cyclic diaryliodonium reagent, enabling a three-component coupling of the cyclic diaryliodonium species, an alkyne, and an azide under atroposelective control. This method constitutes an asymmetric interrupted CuAAC reaction and furnishes a diverse array of structurally unique atropisomeric biaryl triazoles. Mechanistic investigations reveal an unusual secondary kinetic isotope effect for the terminal alkyne, while in situ calorimetry-based reaction progress kinetic analysis identifies the individual reaction orders of each substrate: formal 0.4th-order for cyclic diaryliodonium and first-order for both the alkyne and the azide. The overall rate is governed by copper-mediated cyclometallation of the alkyne and azide, as well as the oxidative addition of the copper triazolide intermediate to the cyclic diaryliodonium reagent. Cycloaddition ; Copper ; Azide ; Alkyne ; Reactive intermediate ; Coupling reaction ; Reaction intermediate Axial and Atropisomeric Chirality Synthesis ; Click Chemistry and Applications ; Catalytic C–H Functionalization Methods
Authors: Jiawei Li | Minzhou Li | Qi Yang | Sanzhong Luo
Source: Nature Communications (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis The application of artificial intelligence (AI) to chemical discovery is critically hindered by the inaccessibility of data locked within unstructured scientific literature. Existing data acquisition methods are often manual, limited in scope, or require extensive custom software development, impeding progress in leveraging AI for chemical discovery. Here, we introduce ReactionSeek, a framework that synergistically combines large language models (LLMs) with established cheminformatics tools to automate multi-modal data mining from organic synthesis literature. Through sophisticated prompt engineering with minimal custom code, ReactionSeek extracts and standardizes complex textual, graphical, and semantic chemical information. We validate this framework on the century-spanning Organic Syntheses collection, achieving over 95% precision and recall for key reaction parameters. This enables three applications: the generation of a large, AI-ready dataset; an interactive Synthetic Chatbot (SynChat) for natural language querying of chemical data; and an autonomous analysis that revealed decades-long trends in catalysis. ReactionSeek thus provides a general solution to the data curation bottleneck, representing a step forward in for AI-driven archive mining and knowledge discovery across the chemical sciences. Cheminformatics ; Knowledge extraction ; Chatbot ; Key (lock) ; Knowledge acquisition ; Software mining ; Software ; Data curation Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Yixi Shen | Ledu Wang | Yan Huang | Xiaolong Zhang | MENG HUANG | Huirong Li | Jing He | Aoran Cai | Yang Wang | Pieter Smith | Jun Jiang | Zhuoying Zhu | Linjiang Chen
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Unlocking azobenzene isomerization mechanisms <i>via</i> an LLM agent-driven workflow integrating simulation, experiment, and machine learning Bridged azobenzene derivatives are key photo-responsive molecular switches. However, probing and interpreting their microscopic Z ↔ E isomerization mechanisms remain challenging as isolated spectroscopic and computational efforts struggle to establish clear structure-spectrum relationships. We report an integrated, large-language-model (LLM) agent-driven workflow that links literature-guided planning, <i>ab initio</i> molecular dynamics (AIMD) sampling, density functional theory spectral calculations, robotic infrared/Raman measurements, and interpretable machine learning for structural-spectral analysis of bridged azobenzenes. Central to the analysis is an attention-based convolutional neural network (ATT-CNN) that predicts the C-N[double bond, length as m-dash]N-C dihedral angle directly from vibrational spectra with <i>r</i> = 0.99 and MAE = 5°. Attention maps highlight mechanistically informative bands and support holistic (non-marker-dependent) interpretation; transfer learning extends performance across chemical environments and experimental datasets. LLM agents formulated the research plan and coordinated automated simulations and measurements, whereas neural-network architecture design, training, and comparative benchmarking were performed by human researchers to retain full flexibility for model exploration and ensure rigorous interpretation. To our knowledge, this is the first LLM-agent-planned and -orchestrated mechanistic study unifying literature synthesis, theory, experiment, and machine learning. The resulting strategy advances quantitative insight into azobenzene photoisomerization and provides a generalizable blueprint for AI-driven investigations of dynamic molecular systems. Azobenzene ; Isomerization ; Workflow ; Key (lock) ; Molecular machine Photochromic and Fluorescence Chemistry ; Machine Learning in Materials Science ; Click Chemistry and Applications
Authors: Qi Yang | Yidi Liu | Wenxuan Zhang | Long Zhang | Yufei Chen | Sanzhong Luo
Source: ChemRxiv (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

OrgAIcat: Leveraging Literature Data for Enantioselectivity Prediction and Optimization in Organocatalysis The vast repository of published chemical literature constitutes a rich yet deeply flawed resource, where systemic publication bias and data heterogeneity have fostered widespread skepticism regarding its utility for predictive modeling. Here, we demonstrate that this imperfect literature data can be transformed into a robust and generalizable predictive model for asymmetric catalysis. We introduce OrgAlcat, a machine learning framework anchored by iSynth, a rigorously curated dataset of over 22,000 aminocatalytic reactions that bridge the gap between structural repositories and raw patent data. Powered by R-SPOC, a chemically interpretable descriptor that obviates computationally prohibitive quantum mechanical calculations, OrgAlcat predicts enantioselectivity with high fidelity (R 2 > 0.7, MAE < 0.30 kcal/mol) and, crucially, distinguishes intrinsic structure-selectivity relationships from mere literature trends. Validating its practical utility in a "human-in-the-loop" workflow, OrgAlcat guided the Bayesian optimization of a challenging Aldol reaction, boosting enantiomeric excess (ee) from 41% to 96% in just 12 experiments. This work establishes a validated methodology for converting historical literature into actionable intelligence, offering a powerful tool to accelerate catalyst discovery and reaction optimization. Boosting (machine learning) ; Skepticism ; Fidelity ; Toolbox ; Bayesian optimization ; Bayesian probability ; Imperfect ; Workflow ; Raw data Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Guangjian Cheng | Hongping Liu | Guozhen Zhang | Xiaolong Zhang | Baicheng Zhang | Aoyuan Cheng | Pieter E. S. Smith | Wenhuan Huang | Yang Zhang | Jun Jiang | Zhenchao Dong | Yi Luo | Guoqing Zhang
Source: Nature Communications (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Controlling narrowband phosphorescence and delayed fluorescence by aggregate size Exciton coherence is a phenomenon involving collective electronic transition dipole moments of molecular aggregates with interesting photophysical behaviors such as superradiance and subradiance in J- and H-type aggregates, respectively. Although singlet aggregate excitons have been extensively studied, the understanding of triplet exciton coherence in terms of specific conditions is still lacking due to limited experimental observations. Here, by synthesizing model organic compounds of fluorene monomer, dimer, trimer, and polymer, we systematically studied their photoluminescence in both solution and aggregation states. Strong triplet exciton coherence is present in polyfluorene aggregates, including nanoparticles, microparticles, and thin films, and is manifested either as HJ-aggregate phosphorescence or delayed fluorescence depending on the size of these aggregates. Notably, in the phosphorescence state, the polyfluorene polymer exhibits an unusually sharp atomic-spectrum-like emission band with a narrow full width at half maximum (FWHM) of only 0.05 eV (14 nm) and the longest lifetime of 0.63 s. This study shows that the size of organic aggregates is essential in dictating organic exciton dynamics in the solid state and holds importance for the development of advanced optoelectronic technologies. Phosphorescence ; Fluorescence ; Narrowband ; Aggregate (composite) ; J-aggregate Luminescence and Fluorescent Materials ; Analytical Chemistry and Sensors ; Advanced Fluorescence Microscopy Techniques
Authors: Yingdong Duan | Fanzhu Zeng | Sanzhong Luo
Source: ChemRxiv (article, 2026-01-13)
Field: Chemistry — Organic Chemistry

Reductive iminium-ion catalysis: electrochemical β-arylation and alkenylation of enals by primary amine/nickel dual catalysis For nearly two decades, radical reactivity based on aminocatalysis has predominantly relied on the single-electron oxidation of enamines to access α-imino radical cations, enabling versatile α-functionalization. Herein, we report a divergent reductive catalytic paradigm that leverages electrochemistry to achieve the direct single-electron reduction of electron-deficient iminium ions. This approach provides a mild and streamlined access to β-enaminyl radicals, bypassing the conventional requirements for enamine oxidation/deprotonation or iminium photoexcitation. Crucially, this reductive platform is successfully integrated into a dual nickel/amine catalytic cycle. By intercepting the transient β-enaminyl radical with a nickel center, we achieved the direct β-arylation and alkenylation of enals—substrates that are typically incompatible with traditional reductive cross coupling, with aryl or alkenyl halides with high productivity and good chemoselectivity. This work not only expands the synthetic utility of aminocatalytic intermediates through electrochemical tuning but also establishes a robust framework for merging organocatalytic radical generation with transition-metal catalyzed cross-coupling reactions. Iminium ; Catalysis ; Halide ; Electrochemistry ; Enamine ; Reactivity (psychology) ; Aryl ; Reductive elimination ; Aryl radical Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Lingxiao Guo | Qiangqiang Shi | Jin Zhang | Chunlai Yang | J. F. Tan | Keyu Li | Jinming Hu | Shiyong Liu
Source: Angewandte Chemie International Edition (article, 2026-02-18)
Field: Chemistry — Organic Chemistry

Sequence‐Defined Oligourethane Isomeric Mixtures for Irreversible Encryption Sequence-defined polymers (SDPs) offer a promising molecular medium for information storage and encryption, yet current strategies for enhancing security typically depend on extending chain length or increasing monomer diversity-approaches that impose substantial synthetic complexity. Here, we introduce a fundamentally different encryption mechanism based on isomeric mixtures of sequence-defined oligourethanes (SDOs). Because isomeric SDOs share identical molecular weights, their mixtures collapse into a single peak in primary MALDI-TOF MS, concealing compositional identity. More critically, tandem MS fragmentation generates multiple degenerate decoding paths, preventing reconstruction of the original mixture and establishing an intrinsically one-way, irreversible molecular encryption process. Using four octameric isomers as a proof-of-concept system, we further integrate these mixtures into CMYK molecular digital inks that enable direct printing and secure information transmission. Encrypted messages can be retrieved only through a bespoke MS/MS decoding algorithm coupled with a predefined ASCII-mapping scheme. This work introduces isomeric oligomer mixtures as a new class of molecular cryptographic media, bridging chemical design with information theory and offering a scalable platform for next-generation data security and anti-counterfeiting technologies. Encryption ; Decoding methods ; Scalability ; Monomer ; Cryptography ; Oligomer ; Bespoke ; Polymer Supramolecular Chemistry and Complexes ; Luminescence and Fluorescent Materials ; Chemical Synthesis and Analysis
Authors: Jacopo Teneggi | S. M. Bargeen A. Turzo | Tanya Marwah | Alberto Bietti | P. Douglas Renfrew | Vikram Khipple Mulligan | Siavash Golkar
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Protein Design with Agent Rosetta: A Case Study for Specialized Scientific Agents Large language models (LLMs) are capable of emulating reasoning and using tools, creating opportunities for autonomous agents that execute complex scientific tasks. Protein design provides a natural testbed: although machine learning (ML) methods achieve strong results, these are largely restricted to canonical amino acids and narrow objectives, leaving unfilled need for a generalist tool for broad design pipelines. We introduce Agent Rosetta, an LLM agent paired with a structured environment for operating Rosetta, the leading physics-based heteropolymer design software, capable of modeling non-canonical building blocks and geometries. Agent Rosetta iteratively refines designs to achieve user-defined objectives, combining LLM reasoning with Rosetta's generality. We evaluate Agent Rosetta on design with canonical amino acids, matching specialized models and expert baselines, and with non-canonical residues -- where ML approaches fail -- achieving comparable performance. Critically, prompt engineering alone often fails to generate Rosetta actions, demonstrating that environment design is essential for integrating LLM agents with specialized software. Our results show that properly designed environments enable LLM agents to make scientific software accessible while matching specialized tools and human experts. Matching (statistics) ; Software ; Software design ; Software agent ; Multi-agent system ; Intelligent agent Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Artificial Intelligence in Healthcare and Education
Authors: Chenxi Sheng | Meng Huang | Andrew P. Dove | Linjiang Chen
Source: Polymer Chemistry (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

A computational framework for tuning intra- and intermolecular ductility in polyurethanes The design framework of isohexide-based polyurethane. Ductility (Earth science) ; Intermolecular force ; Polymer ; Plastic waste ; Polyurethane Polymer composites and self-healing ; Polymer crystallization and properties ; Microplastics and Plastic Pollution
Authors: Pratik E. P. Michael
Source: Research Hub (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A JOURNEY FROM ALCHEMIST TO AI CHEMIST: A STUDY” The objective of technological advancement has been to compliment chemists from heavy labour, assist human efforts to predict, design and execute a process to achieve the desired results and the increased accessibility to communication. It has always been believed that humans possessed the knowledge of providing answers in complicated situations and making decisions. However, the scenario seems to be changing fast with the advent of Artificial Intelligence (AI) which is becoming increasingly important in the field of chemistry. Despite numerous applications of AI, it’s still a relatively new concept that has a number of disadvantages and challenges that can affect the process if not researched enough and done right. In the present article, we will provide a general picture of how AI can help chemists be faster and more creative in their research. Process (computing) ; Field (mathematics) ; Alchemy ; Applications of artificial intelligence ; Human intelligence Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Cristina Cornelio | Takuya Ito | Ryan Cory-Wright | Sanjeeb Dash | Lior Horesh
Source: Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

The need for verification in artificial intelligence-driven scientific discovery Artificial intelligence (AI) is transforming the practice of science. Machine learning (ML) and large language models (LLMs) can generate hypotheses at a scale and speed far exceeding traditional methods, offering the potential to accelerate discovery across diverse fields. However, the abundance of hypotheses introduces a critical challenge; without scalable and reliable mechanisms for verification, scientific progress risks being hindered rather than advanced. In this article, we trace the historical development of scientific discovery, examine how AI is reshaping established practices for discovery and review the principal approaches, ranging from data-driven methods and knowledge-aware neural architectures to symbolic reasoning frameworks and LLM agents. While these systems can uncover patterns and propose candidate laws, their scientific value ultimately depends on rigorous and transparent verification, which we argue must be the cornerstone of AI-assisted discovery. This article is part of the discussion meeting issue 'Symbolic regression in the physical sciences'. Scientific discovery ; Principal (computer security) ; Cornerstone ; TRACE (psycholinguistics) ; Scalability ; Value (mathematics) Machine Learning in Materials Science ; Computational and Text Analysis Methods ; Explainable Artificial Intelligence (XAI)
Authors: Chengxi Zhao | Zhaojia Ma | Daojin Fan | Siyu Hu | Leping Wang | Feng Hua | Weile Jia | En Shao | Guangming Tan | Jun Jiang | Linjiang Chen
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction BOMLIP-CSP is an open-source framework that accelerates crystal structure prediction via batched optimization with MLIPs while maintaining accuracy. Crystal structure prediction ; Crystal structure ; Interatomic potential ; Sampling (signal processing) ; Crystal (programming language) ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions