jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ahn, Jihun | Irianti, Gabriella Pasya | Thapar, Vikram | Hur, Su-Mi
Source: ArXiv.org (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

Chemistry Integrated Language Model using Hierarchical Molecular Representation for Polymer Informatics Machine learning has transformed material discovery for inorganic compounds and small molecules, yet polymers remain largely inaccessible to these methods. While data scarcity is often cited as the primary bottleneck, we demonstrate that strategic molecular representations can overcome this limitation. We introduce CI-LLM (Chemically Informed Language Model), a framework combining HAPPY (Hierarchically Abstracted rePeat unit of PolYmer), which encodes chemical substructures as tokens, with numerical descriptors within transformer architectures. For property prediction, De$^3$BERTa, our descriptor-enriched encoder, achieves 3.5x faster inference than SMILES-based models with improved accuracy ($R^2$ score gains of 0.9-4.1 percent across four properties), while providing interpretable structure-property insights at the subgroup level. For inverse design, our GPT-based generator produces polymers with targeted properties, achieving 100 percent scaffold retention and successful multi-property optimization for negatively correlated objectives. This comprehensive framework demonstrates both forward prediction and inverse design capabilities, showcasing how strategic molecular representation advances machine learning applications in polymer science. Representation (politics) ; Inference ; Language model ; Inverse ; Polymer ; Generator (circuit theory) ; Informatics ; Terminology Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Deborah Braide | Eslam G. Al-Sakkari | Christopher Panaritis | Long Li | Ntumba Kabongo Etienne | Laura Lavigne | Alexander Abanobi | Ahmed Ragab | Gregory S. Patience | Daria C. Boffito
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Ultrasound Accelerates Zeolite Synthesis: Predictive Modelling Via Machine Learning Zeolite ; Computer science ; Ultrasound ; Materials science ; Artificial intelligence ; Chemistry ; Organic chemistry ; Acoustics ; Physics ; Catalysis Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Advanced Data Processing Techniques
Authors: Yi-Wen Zhang (471276) | Zihao Ye (16325100) | Dejun Hu (20977763) | Shutao Qi (3774982) | Zuobang Sun (6817247) | Junfeng Yang (1590952) | Yan Ma (88810) | Wayne Zhang (18349105) | Junliang Zhang (1470220) | Zhiming Li (1445812)
Source: Figshare (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

QC-Augmented NN/GNNfor Few-Shot Prediction of AmorphousPolymer Properties Via Multi-Scale Microstructures Accurate prediction of polymer properties is essential for accelerating materials design. However, deep learning techniques, as well as traditional density functional theory (DFT) and molecular dynamics (MD) methods, often face limitations due to the scarcity of experimental data and insufficient understanding of amorphous polymer microstructures, particularly in few-shot learning scenarios. To address this challenge, we introduce a paradigm based on “local clusters,” structural motifs whose properties can be efficiently computed using low-cost quantum chemical (QC) methods. These clusters, simulated across multiple scales, serve as key descriptors that capture essential microstructural features of amorphous polymers. By integrating QC-derived descriptors with graph convolutional networks (GNN) or neural networks (NN), we developed Locluster, a multiscale, microstructure-informed predictive framework tailored for few-shot learning scenarios in amorphous polymer research. Notably, Locluster eliminates the need for full-scale QC simulations of entire polymers and requires only 2–5 descriptors and as few as two dozen training samples to accurately predict critical properties such as density, refractive index, dielectric constant, and glass transition temperature. The model achieves predictive accuracy comparable to large-data approaches and can generate predictions for 100–200 polymer candidates within 24 h on a single 128-core server, making it well-suited for rapid iteration and design updates in early- to midstage polymer development. This work offers an efficient and practical strategy for the rational design and accelerated discovery of amorphous polymeric materials. Amorphous solid ; Polymer ; Deep learning ; Dielectric ; Glass transition ; Work (physics) ; Molecular dynamics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Jun Jiang | Donglai Zhou | Ruyu Yang | Z. Jiao | Yuyang Cai | Luyuan Zhao | Lulu Guo | Guilin Ye | Song Wang | Linjiang Chen | Daobin Liu | Pieter E. S. Smith | Yan Huang | Qing Zhu
Source: Research Square (preprint, 2025-01-13)
Field: Materials Science — Materials Chemistry

A Practical Inverse Design Approach for High-Entropy Catalysts with Generative AI Inverse ; Generative grammar ; Computer science ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Ahn, Jihun | Irianti, Gabriella Pasya | Thapar, Vikram | Hur, Su-Mi
Source: arXiv (Cornell University) (preprint, 2025-12-06)
Field: Materials Science — Materials Chemistry

Chemistry Integrated Language Model using Hierarchical Molecular Representation for Polymer Informatics Machine learning has transformed material discovery for inorganic compounds and small molecules, yet polymers remain largely inaccessible to these methods. While data scarcity is often cited as the primary bottleneck, we demonstrate that strategic molecular representations can overcome this limitation. We introduce CI-LLM (Chemically Informed Language Model), a framework combining HAPPY (Hierarchically Abstracted rePeat unit of PolYmer), which encodes chemical substructures as tokens, with numerical descriptors within transformer architectures. For property prediction, De$^3$BERTa, our descriptor-enriched encoder, achieves 3.5x faster inference than SMILES-based models with improved accuracy ($R^2$ score gains of 0.9-4.1 percent across four properties), while providing interpretable structure-property insights at the subgroup level. For inverse design, our GPT-based generator produces polymers with targeted properties, achieving 100 percent scaffold retention and successful multi-property optimization for negatively correlated objectives. This comprehensive framework demonstrates both forward prediction and inverse design capabilities, showcasing how strategic molecular representation advances machine learning applications in polymer science. Representation (politics) ; Inference ; Language model ; Inverse ; Polymer ; Generator (circuit theory) ; Informatics ; Terminology Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Maria G. Minotaki
Source: Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya) (article, 2025-04-09)
Field: Materials Science — Materials Chemistry

Insights into the Stability of Single-Atom Catalysts on Doped Carbon Using Machine Learning Techniques El desenvolupament de tecnologies sostenibles és crucial per superar els reptes ambientals i fomentar el progrés socioeconòmic. Els catalitzadors d'àtoms individuals (SACs) ofereixen una activitat i selectivitat excepcionals, però la seva estabilitat continua sent un repte clau en el disseny de catalitzadors. Les tècniques convencionals de caracterització i síntesi tenen dificultats per identificar amb precisió els llocs actius a causa de les àmplies configuracions dels SACs, mentre que la projecció de gran capacitat basada en la teoria del funcional de densitat es veu limitada pel cost computacional i el temps. En aquest treball, s'utilitza un enfocament multinivell, integrant l'aprenentatge automàtic amb mètodes computacionals, per investigar sistemàticament l'estabilitat dels SACs sobre carboni dopat. Es desenvolupa un marc d'aprenentatge automàtic per identificar els principals descriptors d'estabilitat, capturant la interacció entre els metalls i els suports. Es deriva una formulació d'energia d'adsorció, revelant les contribucions covalents i iòniques que regeixen l'estabilitat. L'anàlisi de l'estructura electrònica destaca la influència de l'especiació de l'azot en les propietats electròniques dels SACs, mentre que les tècniques de reducció de dimensionalitat descobreixen regions de similitud electrònica. S'avalua la transferibilitat dels descriptors d'estabilitat en catalitzadors d'àtoms dobles, demostrant la seva aplicabilitat a sistemes més complexos. Finalment, s'examina el mecanisme de desactivació dels grups Fe-NxCy en condicions de reacció de reducció d'oxigen, revelant l'efecte desestabilitzador de les espècies reactivades d'oxigen sobre els enllaços metall-suport. S'identifica un entorn de coordinació d'azot de quatre àtoms com la configuració més robusta. Aquest estudi avança en el disseny de catalitzadors aprofitant l'aprenentatge automàtic i la química computacional per dilucidar els factors d'estabilitat dels SACs. Els resultats proporcionen informació útil per desenvolupar catalitzadors més robustos, eficients i sostenibles per a aplicacions energètiques i mediambientals. Stability (learning theory) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Francesco Mele | Ana Maria Constantin | Marco Barezzi | Leonardo Rossi | Alex Ergasti | Tomaso Fontanini | Sofia Civardi | Remie Matthieu Sundermann | Paolo P. Mazzeo | Raimondo Maggi | Nicola Della Ca’ | Andrea Prati | Luca Capaldo
Source: ChemRxiv (article, 2025-11-10)
Field: Chemistry — Physical and Theoretical Chemistry

Machine Learning–Assisted Development of a Fast Mechanochemical Johnson–Corey–Chaykovsky Reaction Mechanochemistry ; Variety (cybernetics) ; Set (abstract data type) ; Work (physics) ; Cyclopropanation ; Reaction conditions Crystallography and molecular interactions ; Machine Learning in Materials Science ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Authors: Hoje Chun (5277392) | Minjoon Hong (21085210) | Seung Hyo Noh (3862519) | Byungchan Han (1591405)
Source: Figshare (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Learning PairwiseInteraction for Extrapolativeand Interpretable MachineLearning Interatomic Potentials with Physics-Informed Neural Network Achieving both robust extrapolation and physical interpretability in machine learning interatomic potentials (ML-IPs) for atomistic simulation remains a significant challenge, particularly in data-scarce areas such as chemical reactions or complex, multicomponent materials at extreme conditions. Here, we present a pairwise-decomposed physics-informed neural network (P2Net) that parametrizes an analytical bond-order potential (BOP) layer to decouple the energy contributions of atomic pairs. By leveraging fundamental physical principles, P2Net demonstrates excellence at extrapolating beyond its training regime and accurately capturing molecular geometries far from equilibrium. The pairwise energy decomposition further empowers the bond analyses for deprotonation and S<sub>N</sub>2 reactions, which is not easy with most ML-IPs. The atomic pair energy offers how to elucidate the evolution of interatomic interactions as a reaction proceeds. Our methodology highlights enhanced data efficiency in building ML-IPs and facilitates more informative postsimulation analysis, thereby broadening the applicability of ML-IPs to complex and reactive systems. Interpretability ; Extrapolation ; Pairwise comparison ; Artificial neural network ; Interatomic potential ; Energy (signal processing) ; Potential energy Machine Learning in Materials Science ; Quantum many-body systems ; CO2 Reduction Techniques and Catalysts
Authors: Guohai Liu | Huahu Xu | Hao Zhang
Source:  (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

DQN-RXN organic reaction prediction algorithm based on reinforcement learning Automated prediction of organic reaction pathways is a core challenge to accelerate molecular discovery and synthesis planning. Traditional methods are limited by the coverage of artificial rule bases, the high cost of quantum chemical calculations, or the mechanism blindness of statistical models, which makes it difficult to balance efficiency and chemical rationality in complex reactions. In this paper, we propose a framework based on Deep Reinforcement Learning (DRL), DQN-RXN, which achieves efficient and explainable organic reaction prediction by fusing a graph neural network and dynamic decision optimization. The model models the reaction programming as a Markov decision process (MDP), in which the state space is jointly defined by the Molecular Graph Neural Network (MPNN) and there action condition coding. The action space is constructed based on the USPTO template library, and the chemical legitimacy is guaranteed by the subgraph isomorphism algorithm. The multi-objective reward function is innovatively designed, combined with semi-empirical quantum verification (PM6), path efficiency penalty, and yield dynamics model, to drive the deep Q network (DQN) to optimize the strategy in the exploration-exploitation trade-off. Markov decision process ; Artificial neural network ; Reinforcement learning ; Graph ; Dynamic programming ; Chemical space ; State space ; Quantum chemical Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum Computing Algorithms and Architecture
Authors: Salvatore Sorrentino | Alessandro Gussoni | Francesco Calcagno | Gioele Pasotti | Davide Avagliano | Ivan Rivalta | Marco Garavelli | Dario Polli
Source: Digital Discovery (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Mol2Raman: a graph neural network model for predicting Raman spectra from SMILES representations Raman spectroscopy is a powerful technique for probing molecular vibrations, yet the computational prediction of Raman spectra remains challenging due to the high cost of quantum chemical methods and the complexity of structure-spectrum relationships. Here, we introduce Mol2Raman, a deep-learning framework that predicts spontaneous Raman spectra directly from SMILES representations of molecules. The model leverages Graph Isomorphism Networks with edge features (GINE) to encode molecular topology and bond characteristics, enabling accurate prediction of both peak positions and intensities across diverse chemical structures. Trained on a novel dataset of over 31 000 molecules with state-of-the-art Density Functional Theory (DFT)-calculated Raman spectra, Mol2Raman outperforms both fingerprint-based similarity models and Chemprop-based neural networks. It achieves a high fidelity in reproducing spectral features, including for molecules with low structural similarity to the training set and for enantiomeric inversion. The model offers fast inference times (22 ms per molecule), making it suitable for high-throughput molecular screening. We further deploy Mol2Raman as an open-access web application, enabling real-time predictions without specialized hardware. This work establishes a scalable, accurate, and interpretable platform for Raman spectral prediction, opening new opportunities in molecular design, materials discovery, and spectroscopic diagnostics. Raman spectroscopy ; Pattern recognition (psychology) ; Artificial neural network ; Set (abstract data type) ; Computational complexity theory ; ENCODE ; Similarity (geometry) ; Spectral line ; Graph Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zheng, Zhiling
Source: ArXiv.org (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

Developing an AI Course for Synthetic Chemistry Students Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chem-istry course created for students on the synthetic chemistry track with no prior programming background. The curricu-lum emphasizes chemical context over abstract algorithms, using an accessible web-based platform to ensure zero-install machine learning (ML) workflow development practice and in-class active learning. Assessment combines code-guided homework, literature-based mini-reviews, and collaborative projects in which students build AI-assisted workflows for real experimental problems. Learning gains include increased confidence with Python, molecular property prediction, reaction optimization, and data mining, and improved skills in evaluating AI tools in chemistry. All course materials are openly available, offering a discipline-specific, beginner-accessible framework for integrating AI into synthetic chemistry training. Workflow ; Context (archaeology) ; Synthetic biology ; Active learning (machine learning) ; Coding (social sciences) ; Chemistry education ; Course (navigation) ; Synthetic data Machine Learning in Materials Science ; Various Chemistry Research Topics ; Scientific Computing and Data Management
Authors: Jiyoung Lee (41796) | Logan J. Augustine (9541246) | Graeme Henkelman (1287513) | Ping Yang (56755) | Danny Perez (1809790)
Source: Figshare (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Data-Driven KineticReaction Networks for SeparationChemistry Understanding complex, multistep chemical reactions at the molecular level is a major challenge whose solution would greatly benefit the design and optimization of numerous chemical processes. The separation of rare-earth (4f) and actinide (5f) elements is an example where improving our chemical understanding is important for designing and optimizing new chemistries, even with a limited number of observations. In this work, we leverage data-driven artificial intelligence and machine-learning approaches to develop kinetic reaction networks that describe the liquid–liquid extraction mechanism of uranium using <i>N</i>,<i>N</i>-di-2-ethylhexyl-isobutyramide (DEHiBA). Specifically, we compare and contrast the properties of two classes of models: (1) purely data-driven models that are regularized using chemistry-agnostic, L1 regression and (2) chemistry-informed models that are regularized using relative reaction energies provided by quantum mechanical calculations. We observe that purely data-driven models are unbiased, simple, and accurate in their predictions of experimental measurements when provided with sufficient data but are difficult to fully constrain and interpret. In contrast, chemistry-informed models exhibit significantly improved chemical interpretability and consistency, providing a detailed description of the separation process while achieving high accuracy through ensemble averaging. Overall, the dominant species predicted to be extracted into the organic phase is UO<sub>2</sub>(NO<sub>3</sub>)<sub>2</sub>(DEHiBA)<sub>2</sub>, agreeing with experimental slope analysis, thermodynamic modeling, EXAFS, and crystal structures. This work demonstrates that leveraging the fundamental structure of the problem can lead to efficient learning schemes that provide both accurate predictions and chemical insights at a low computational cost. Interpretability ; Leverage (statistics) ; Chemical process ; Chemical reaction ; Experimental data ; Kinetic energy ; Process (computing) ; Quantum chemistry Machine Learning in Materials Science ; Radioactive element chemistry and processing ; Catalysis and Oxidation Reactions
Authors: Cohen, Jaron | Hasson, Alexander G. | Tanovic, Sara
Source: arXiv (Cornell University) (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

Unveiling Latent Knowledge in Chemistry Language Models through Sparse Autoencoders Since the advent of machine learning, interpretability has remained a persistent challenge, becoming increasingly urgent as generative models support high-stakes applications in drug and material discovery. Recent advances in large language model (LLM) architectures have yielded chemistry language models (CLMs) with impressive capabilities in molecular property prediction and molecular generation. However, how these models internally represent chemical knowledge remains poorly understood. In this work, we extend sparse autoencoder techniques to uncover and examine interpretable features within CLMs. Applying our methodology to the Foundation Models for Materials (FM4M) SMI-TED chemistry foundation model, we extract semantically meaningful latent features and analyse their activation patterns across diverse molecular datasets. Our findings reveal that these models encode a rich landscape of chemical concepts. We identify correlations between specific latent features and distinct domains of chemical knowledge, including structural motifs, physicochemical properties, and pharmacological drug classes. Our approach provides a generalisable framework for uncovering latent knowledge in chemistry-focused AI systems. This work has implications for both foundational understanding and practical deployment; with the potential to accelerate computational chemistry research. Interpretability ; Autoencoder ; Foundation (evidence) ; Generative grammar ; Property (philosophy) ; Computational model ; ENCODE ; Language model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Zheng, Zhiling
Source: arXiv (Cornell University) (preprint, 2025-11-23)
Field: Materials Science — Materials Chemistry

Developing an AI Course for Synthetic Chemistry Students Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chem-istry course created for students on the synthetic chemistry track with no prior programming background. The curricu-lum emphasizes chemical context over abstract algorithms, using an accessible web-based platform to ensure zero-install machine learning (ML) workflow development practice and in-class active learning. Assessment combines code-guided homework, literature-based mini-reviews, and collaborative projects in which students build AI-assisted workflows for real experimental problems. Learning gains include increased confidence with Python, molecular property prediction, reaction optimization, and data mining, and improved skills in evaluating AI tools in chemistry. All course materials are openly available, offering a discipline-specific, beginner-accessible framework for integrating AI into synthetic chemistry training. Workflow ; Context (archaeology) ; Synthetic biology ; Active learning (machine learning) ; Coding (social sciences) ; Chemistry education ; Course (navigation) ; Synthetic data Machine Learning in Materials Science ; Various Chemistry Research Topics ; Scientific Computing and Data Management
Authors: Mariachiara Grande | Tommaso Gallingani | Romolo Laurita | Matteo Gherardi
Source: Research Square (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

An explainable data-driven framework for material processing: integrating design of experiment and machine learning in laser ablation Process (computing) ; Modular design ; Automation ; Adaptability ; Representation (politics) ; Key (lock) ; Identification (biology) ; Industry 4.0 Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Explainable Artificial Intelligence (XAI)