jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Bharath | R.K. Singh Raman | Alankar Alankar
Source: Progress in Materials Science (article, 2025-12-11)
Field: Materials Science — Biomaterials

The role of protein content in body fluids in magnesium alloy bioimplant degradation: A machine learning approach Generative grammar ; Bovine serum albumin ; Reliability (semiconductor) ; Biocompatibility ; Magnesium ; Feature (linguistics) Magnesium Alloys: Properties and Applications ; Nanoporous metals and alloys ; Titanium Alloys Microstructure and Properties
Authors: Yingjie Zhao | Hongbo Zhou | Zian Zhang | Zhenxing Bo | Baoan Sun | Minqiang Jiang | Zhiping Xu
Source: Matter (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Discovering high-strength alloys via physics-transfer learning Transfer of learning ; Materials science ; Physics ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Materials Characterization Techniques
Authors: Muhammad Imran | Gerald Schuller
Source: ArXiv.org (preprint, 2025-09-29)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Prediction of Energy Decay Curves from Room Geometry and Material Properties Accurate prediction of energy decay curves (EDCs) enables robust analysis of room acoustics and reliable estimation of key parameters. We present a deep learning framework that predicts EDCs directly from room geometry and surface absorption. A dataset of 6000 shoebox rooms with realistic dimensions, source-receiver placements, and frequency-dependent wall absorptions was synthesized. For each configuration we simulate room impulse responses (RIRs) using Pyroomacoustics and compute target EDCs. Normalized room features are provided to a long short-term memory (LSTM) network that maps configuration to EDC. Performance is evaluated with mean absolute error (MAE) and root mean square error (RMSE) over time. We further derive early decay time (EDT), reverberation time (T20), and clarity index (C50) from predicted and target EDCs; close agreement is observed (e.g., EDT MAE 0.017 s, T20 MAE 0.021 s). The approach generalizes across diverse rooms and supports efficient room-acoustics modeling for early-stage design and real-time applications. Reverberation ; Impulse (physics) ; Energy (signal processing) ; Mean squared error ; Mean absolute error ; Measure (data warehouse) ; Key (lock) ; Impulse response Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Recycling and Waste Management Techniques
Authors: Surbhi Khewle | Pratyush Dayal
Source: The Journal of Physical Chemistry B (article, 2025-06-28)
Field: Materials Science — Polymers and Plastics

Photoactivated Nano-Compatibilized Two-Phase Polymer Blends: An Approach for Determining Mechanical Behavior Light-activated polymers (LAPs) are shape-shifting materials capable of transforming their shapes in response to photoinduced chemical reactions, such as <i>cis-trans</i> isomerization and dimerization. Owing to the underlying photochemical reaction, these materials often exhibit behavior analogous to multicomponent/phase polymer blends. In this work, we present a free-energy-based theoretical framework to predict the mechanical behavior of nanoparticle-compatibilized elastic LAP blends that exhibit phase separation. In particular, we incorporate the impact of domain sizes and interfacial areas and establish a criterion for the materials' susceptibility to mechanical failure under various loading conditions, namely uniaxial and biaxial stretching. Our framework can also be adapted to high-entropy polymers and thermoresponsive or light-activated systems, with potential applications in soft robotics, biomedical devices, micromechanics, 4D printing, and material origami. Additionally, by integrating our model with physics-informed neural networks, we facilitate efficient analysis of complex domain geometries and enable comprehensive parametric studies. Nano- ; Materials science ; Polymer ; Phase (matter) ; Composite material ; Polymer science ; Chemistry ; Organic chemistry Polymer composites and self-healing ; Photochromic and Fluorescence Chemistry ; Advanced Polymer Synthesis and Characterization
Authors: Sergei Manzhos
Source: Institutional Repositories DataBase (IRDB) (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Large-scale electronic structure materials modeling with the help of machine learning-enhanced DFTB and OF-DFT Electronic structure ; Work (physics) ; Electronics ; Electronic systems ; Finite element method Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Boron and Carbon Nanomaterials Research
Authors: Matteo Rinaldi | Matthias Kick | Karsten Reuter | Christian Carbogno
Source: Journal of Physics Materials (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Advancing our understanding of optoionic effects for the design of solar batteries: a theoretical perspective Abstract Optoionics, a promising new field that aims at controlling ion dynamics using light, links photovoltaic power generation with electrochemical charge storage. This has the potential to drive and accelerate the energy revolution by utilizing materials that integrate the functionality of batteries and photovoltaic cells. Finding, optimizing, and customizing these materials is a complex task, though. Computational modeling can play a crucial role in guiding and speeding up such developments, particularly when the acting atomic mechanisms are not well understood. This does however require expertise in various areas, including advanced electronic-structure theory, machine learning, and multi-scale approaches. In this perspective, we shed light on the intricacies of modeling optoionic effects for solar battery materials. We first discuss the underlying physical and chemical mechanisms, as well as the computational tools that are available to date for describing these processes. Furthermore, we discuss the limits of these approaches and identify key challenges that need to be tackled to advance this field. Perspective (graphical) ; Engineering ethics ; Management science ; Engineering ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Thin-Film Transistor Technologies
Authors: Huabing Cai | Aoni Xu | Gobinath Pillai Rajarathnam | Ang Cao | Jianhua Yan
Source: AI Agent (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Exploring structures of nanoclusters combining high-dimensional neural network potentials with unsupervised machine learning algorithms Understanding the structural evolution of catalysts with temperature is crucial for elucidating the real active sites under thermal catalytic conditions. Cerium oxides have emerged as versatile catalysts in such environments; however, the temperature-dependent stability of pristine nanoclusters remains poorly understood, creating a knowledge gap between their idealized and working structures. Herein, we developed a machine learning workflow combining high-dimensional neural network potentials and unsupervised machine learning algorithms to uncover the intrinsic structural evolution of stoichiometric Ce&lt;sub&gt;n&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;&lt;sub&gt;n&lt;/sub&gt; (n &lt; 21) clusters. We first identified the most stable configurations for each cluster size and determined several magic numbers (n = 5, 8, 10, 12, 14, 20) with enhanced stability at 0 K. Nanosecond-scale neural network potential molecular dynamics simulations were then employed to explore temperature-dependent behavior, where the most frequently appearing structures extracted via K-means clustering were adopted as stability indicators. The results reveal that the temperature-sensitive stability varies with cluster size: magic-number clusters such as Ce&lt;sub&gt;14&lt;/sub&gt;O&lt;sub&gt;28&lt;/sub&gt; maintain relative robust stability at elevated temperatures, while non-magic-number clusters such as Ce&lt;sub&gt;4&lt;/sub&gt;O&lt;sub&gt;8&lt;/sub&gt; undergo pronounced structural fluctuations. This work not only offers new insights into temperature-induced distortion and reconstruction of oxide clusters, but also establishes an efficient workflow for high-precision structural prediction especially under high temperatures. Nanoclusters ; Artificial neural network ; Stability (learning theory) ; Cluster analysis ; Cluster (spacecraft) ; Unsupervised learning Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Zongru Li | Xingsheng Chen | Honggang Wen | Zhang, Regina | Li, Ming | Xiaojin Zhang | Hongzhi Yin | Yang, Qiang | Kwok-Yan Lam | Lio, Pietro | Siu-Ming Yiu
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Comprehensive Survey and Benchmarks of Deep Learning for Molecular Property Prediction in the Foundation Model Era Deep learning ; Foundation (evidence) ; Property (philosophy) ; Big data ; Systematic review Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Jun-Jie Luo | Bao Qin | Biao Wang
Source: Materials Science and Engineering A (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Generative adversarial network-based data augmentation for improving creep life prediction of high-temperature titanium alloys Creep ; Convolutional neural network ; Generative grammar ; Generalization ; Reliability (semiconductor) ; Artificial neural network ; Adversarial system ; Experimental data Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; High Temperature Alloys and Creep
Authors: Jia-Lan Chen (17992818) | Xin-Ze Qi (22398468) | Jinze Zhu (17617515) | Jin Li (119007) | Xue-Chun Jiang (22398471) | Wei-Xue Li (1555876) | Jin-Xun Liu (1672297)
Source: Figshare (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Grand-CanonicalEquivariant Neural Potentials forElectrochemical Interfaces Electrochemical reactions under constant potential underpin critical processes in energy storage, catalysis, and corrosion but remain challenging to model owing to the voltage insensitivity of conventional machine learning potentials. The lack of a unified framework incorporating grand-canonical constraints into machine-learned models fundamentally limits accurate, scalable simulations of potential-dependent interfacial phenomena. Here, we present a constant-potential, E(3)-equivariant message-passing neural network (CPMPNN) that integrates grand-canonical electronic structure principles with a global excess-charge parameter that is dynamically redistributed via a multihead attention mechanism. The atomic geometry is encoded through a graph neural network that preserves the full symmetry of the Euclidean group in three dimensions (E(3))including translations, rotations, and reflections. Benchmarking against the grand-canonical DFT confirms that the CPMPNN retains first-principles accuracy while achieving a three-orders-of-magnitude computational speedup. Applied to key electrocatalytic processesCO dimerization in CO<sub>2</sub> reduction and the Volmer step in hydrogen evolution on Cu(100)CPMPNN captures how the applied potential modulates the reaction thermodynamics, charge distribution, and transition-state structures, providing mechanistic insight into potential-dependent kinetics. By bridging first-principle accuracy with molecular dynamics scalability, CPMPNN provides a transferable framework for operando modeling of electrified interfaces, enabling new mechanistic insights into potential-controlled electrocatalysis. Artificial neural network ; Scalability ; Equivariant map ; Potential energy ; Electrochemistry ; Topology (electrical circuits) ; Bridging (networking) ; Key (lock) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Mudassir Hussain Tahir | Sumaira Naeem | Ihab Mohamed Moussa
Source: Synthetic Metals (article, 2025-10-06)
Field: Chemistry — Organic Chemistry

Designing high electron affinity small molecule acceptors through comprehensive chemical library generation Small molecule ; Molecule ; Electron affinity (data page) ; Drug discovery ; Affinities ; Polymer ; Electron acceptor ; Chemical library Synthesis and Biological Evaluation ; Molecular Junctions and Nanostructures ; Click Chemistry and Applications
Authors: Alberto Torres | Alan Barros de Oliveira | Maria Barbosa‐Ducharne | Leonardo Villegas Lelovsky | Valdirene Gonzaga de Resende | Fernando Dutra | Felipe M. Pimenta | Fabrício Vilela Parreira | Amaury de Souza | Matheus J. S. Matos | Alexandre Reily Rocha
Source: Research Square (preprint, 2025-11-25)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential for the structural properties iron oxides Interatomic potential ; Density functional theory ; Molecular dynamics ; Ab initio ; Transferability ; Computational model ; Anisotropy ; Artificial neural network ; Range (aeronautics) Machine Learning in Materials Science ; Iron oxide chemistry and applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Suvendu Kumar | Saveena Solanki | Munishwar Nath Gupta | Sanjay Kumar Mohanty | Shiva Satija | Sonam Chauhan | Subhadeep Duari | Arushi Sharma | Vishakha Gautam | Sakshi Arora | Rajeswari Shome | Sourav Sinha | Abhinav Kumar Sharma | Aayushi Mittal | Debarka Sengupta | Natarajan Arul Murugan | Gaurav Ahuja
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

Chemical Dice Integrator (CDI): A Scalable Framework for Multimodal Molecular Representation Learning ABSTRACT The machine learning landscape for molecular property prediction is fragmented, with numerous Featurizers each capturing a narrow, specialized view of chemical structure. This heterogeneity forces a suboptimal choice of representation a priori, limiting model generalizability. We introduce the Chemical Dice Integrator (CDI), a hierarchical framework that unifies six orthogonal molecular representations, physicochemical (Mordred), topological (GROVER), visual (ImageMol), biological (Signaturizer), quantum-mechanical (MOPAC), and linguistic (ChemBERTa), into a single, coherent embedding. The framework consists of CDI-Basic, a two-tiered autoencoder that fuses these modalities, and CDI-Generalised, a Mamba State-Space Model (SSM) that learns a direct, efficient map from SMILES strings to the unified embedding space. Extensive benchmarking across 23 classification (171 tasks) and 10 regression datasets demonstrates that CDI embeddings consistently achieve superior predictive performance compared to individual Featurizers and standard feature aggregation methods. The CDI-Generalised model achieves this performance with exceptional computational efficiency, outperforming deep learning Featurizers in terms of speed and resource overhead. Furthermore, we demonstrate that the CDI embedding is chemically intuitive, allowing for the sensitive distinction of nuanced structural variants, such as chiral enantiomers and kekulized SMILES forms. By bridging multimodal chemical intelligence with scalable, sequence-based inference, CDI offers a strong foundation for molecular machine learning. Embedding ; Representation (politics) ; Scalability ; Feature learning ; Autoencoder ; Feature (linguistics) ; Interpretability ; Bridging (networking) ; Dice ; Property (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Ignacio Funes Ardoiz | Ana B Penín Estévez | Aimara García Camacho | Diego Sampedro Ruiz | Nil Sanosa Ferro | J.A. Hueffel | Cecilia Merino Robledillo
Source: Dialnet (Universidad de la Rioja) (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Th B-Rad and react B-Rad maps: machine learning insights into boryl radical Chemistry Autorías: Aimara García Camacho, Ana B Penín Estévez, Nil Sanosa i Ferro, J.A. Hueffel, Cecilia Merino Robledillo, Diego Sampedro Ruiz, Ignacio Funes Ardoiz. Localización: II Jornada de Química IQUR: Instituto de Investigación en Química de la Universidad de La Rioja, 2025. Artículo de Libro en Dialnet. Government (linguistics) ; Feature (linguistics) ; Expert system Organoboron and organosilicon chemistry ; Machine Learning in Materials Science ; Boron Compounds in Chemistry
Authors: Zongru Li | Xingsheng Chen | Honggang Wen | Regina Qianru ZHANG | Ming Li | Xiaojin Zhang | Hongzhi Yin | Qiang Yang | Kwok-Yan Lam | Pietro Lio | Siu-Ming Yiu
Source: Open MIND (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Comprehensive Survey and Benchmarks of Deep Learning for Molecular Property Prediction in the Foundation Model Era Deep learning ; Foundation (evidence) ; Property (philosophy) ; Big data ; Data collection Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Xiaopeng Li | Xin Ye | Xiaojian Zeng | Ningyi Cui | Donghua Liu | Yufan Bao | Zhengli Zhang | Mingfei Xiao | Tao Zhang | Yecheng Zhou
Source: Surfaces and Interfaces (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

GFDM descriptor-driven machine learning prediction of surface properties of molecular modified surfaces Surface (topology) ; Response surface methodology ; Surface modification ; Artificial neural network Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Block Copolymer Self-Assembly
Authors: Eya Cherif | Teja Kattenborn | Luke A. Brown | Michael Ewald | Katja Berger | Phuong D. Dao | Tobias Hank | Étienne Laliberté | Bing Lu | Hannes Feilhauer
Source:  (preprint, 2025-04-09)
Field: Chemistry — Analytical Chemistry

Supplementary material to "Uncertainty Assessment in Deep Learning-based Plant Trait Retrievals from Hyperspectral data" Hyperspectral imaging ; Trait ; Environmental science ; Remote sensing ; Artificial intelligence ; Computer science ; Machine learning ; Geography Spectroscopy and Chemometric Analyses
Authors: García Camacho, Aimara | Penín Estévez, Ana B | Sanosa Ferro, Nil [0000-0002-9314-0824] | Hueffel, J.A. | Merino Robledillo, Cecilia | Sampedro Ruiz, Diego [0000-0003-2772-6453] | Funes Ardoiz, Ignacio [0000-0002-5843-9660]
Source: RIUR (Universidad de La Rioja) (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Th B-Rad and react B-Rad maps: machine learning insights into boryl radical Chemistry Government (linguistics) ; Feature (linguistics) Organoboron and organosilicon chemistry ; Boron Compounds in Chemistry ; Machine Learning in Materials Science
Authors: Alan Kai Hassen | Andrius Bernatavicius | Antonius P. A. Janssen | Mike Preuß | Gerard J. P. van Westen | Djork-Arné Clevert
Source: ArXiv.org (preprint, 2025-10-18)
Field: Materials Science — Materials Chemistry

Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration Applications of machine learning in chemistry are often limited by the scarcity and expense of labeled data, restricting traditional supervised methods. In this work, we introduce a framework for molecular reasoning using general-purpose Large Language Models (LLMs) that operates without requiring labeled training data. Our method anchors chain-of-thought reasoning to the molecular structure by using unique atomic identifiers. First, the LLM performs a one-shot task to identify relevant fragments and their associated chemical labels or transformation classes. In an optional second step, this position-aware information is used in a few-shot task with provided class examples to predict the chemical transformation. We apply our framework to single-step retrosynthesis, a task where LLMs have previously underperformed. Across academic benchmarks and expert-validated drug discovery molecules, our work enables LLMs to achieve high success rates in identifying chemically plausible reaction sites ($\geq90\%$), named reaction classes ($\geq40\%$), and final reactants ($\geq74\%$). Beyond solving complex chemical tasks, our work also provides a method to generate theoretically grounded synthetic datasets by mapping chemical knowledge onto the molecular structure and thereby addressing data scarcity. Task (project management) ; Retrosynthetic analysis ; Scarcity ; Class (philosophy) ; Work (physics) ; Cheminformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Swagata Pal | Jayaseelan Arun | Abiram Karanam Rathankumar | P. Senthil Kumar | Dhanya Vishnu
Source: International Journal of Environmental Research (article, 2025-11-27)
Field: Chemistry — Organic Chemistry

Zirconium Nanomaterials for Treatment of Wastewater: Augmenting Antimicrobial Effectiveness and Optimization Through Artificial Intelligence Integration Zirconium ; Nanomaterials ; Nanoparticle ; Environmental pollution ; Degradation (telecommunications) ; Photodegradation Nanomaterials for catalytic reactions ; Nanoparticles: synthesis and applications ; Advanced Photocatalysis Techniques
Authors: Matthew S. Johnson | Richard H. West | Judit Zádor
Source: ChemRxiv (preprint, 2025-12-11)
Field: Materials Science — Materials Chemistry

An Interpretable Machine Learning Framework for Prediction of Adsorption Energies and Generative Design of Active Sites on Arbitrary Catalysts Interpretability ; Generative grammar ; Adsorption ; Generative model ; Sequence (biology) ; Decomposition ; Function (biology) ; Graph Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Catalysis and Oxidation Reactions
Authors: Chun-I Wang | Aidan C. Lindsay | Nicholas E. Jackson
Source: ChemRxiv (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Machine Learning Intermolecular Electronic Couplings: Opportunities and Challenges Intermolecular electronic couplings (IECs) play a critical role in theories of charge and excitation transfer. However, the considerable computational cost of evaluating IECs limits the exploration of large molecular systems and long timescales key to nu- merous molecular and materials applications. Recent advances in machine learning (ML) offer a means of accelerating IEC predictions, enabling previously intractable calculations for large disordered systems and their optoelectronic properties. In this pedagogical review, we summarize recent progress in applying ML to predict IECs, and leverage a pedagogical example of IEC prediction applied to a canonical molecular dopant and organic semiconductor system to illustrate conformational data sampling, feature engineering, and ML algorithms. For each step, we discuss the existing chal- lenges and outline future research directions to improve the accuracy, interpretability, and transferability of ML models for IEC predictions. Intermolecular force ; Coupling (piping) ; Materials science ; Physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Computational Drug Discovery Methods