jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simon Daubner | Alexander E. Cohen | Benjamin Dörich | Samuel J. Cooper
Source: The Journal of Open Source Software (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

evoxels: A differentiable physics framework for voxel-based microstructure simulations Materials science inherently spans disciplines: experimentalists use advanced microscopy to uncover micro- and nanoscale structure, while theorists and computational scientists develop models that link processing, structure, and properties. Bridging these domains is essential for inverse material design where you start from desired performance and work backwards to optimal microstructures and manufacturing routes. Integrating high-resolution imaging with predictive simulations and data-driven optimization accelerates discovery and deepens understanding of process-structure-property relationships. The differentiable physics framework evoxels is based on a fully Pythonic, unified voxel-based approach that integrates segmented 3D microscopy data, physical simulations, inverse modeling, and machine learning. Differentiable function ; Microstructure ; Function (biology) ; R package Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Theoretical and Computational Physics
Authors: Wenjie Yin
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

[under submission] Released Code,Dataset and inference results of DRCAT This repo is for a paper under submission: A Chemistry-Informed Deep Learning Network for Mitigating the Stratospheric OH Data Gap. It includes source code, training and inference dataset for DRCAT model. Also, the inference results of DRCAT are also provided here. The backup link of DRCAT can refer to github link: https://github.com/TOMCAT-CTM/DRCAT Inference ; Backup ; Deep learning ; Training set ; Training (meteorology) ; Fiducial inference Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Vincenzo Bongiorno | Niek Hijnen | X. Zhou
Source: Corrosion Science (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Exploring the use of large language models for EIS: A feasibility study on organic coatings for corrosion protection Large Language Models (LLMs) were applied to automate the interpretation of electrochemical impedance spectroscopy (EIS) data, enabling classification and parameter estimation without the need for a task specific machine learning training. The approach achieved classification accuracies up to 96% and produced fitting results comparable to those obtained with specifically trained neural networks. The methodology reduces reliance on labelled data and manual intervention. While demonstrated in the context of organic coatings, the framework provides a scalable AI-based workflow that could, in principle, be extended to conceptually similar tasks in materials and corrosion research, subject to dedicated validation. • A structured methodology is proposed integrating Large Language Models (LLMs) with algorithmic logic to automate the interpretation of Electrochemical Impedance Spectroscopy, without the requirement of training dataset, increasing the accessibility and scalability. • The methodology enables the identification of the impedance response from mathematically different equivalent circuits using prompt-based and reference image comparisons and allows the estimation of electrical equivalent circuit components (fitting). • The proposed approach has been validated with simulated and experimental from organic coatings • The proposed approach has been benchmarked with traditional ML models used with the same datasets presented in this study. • This study demonstrates the practical potential of LLMs for fast, reliable, and expert-guided corrosion assessment. Workflow ; Context (archaeology) ; Identification (biology) ; Scalability ; Task (project management) ; Corrosion ; Electrical impedance ; Equivalent circuit Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Concrete Corrosion and Durability
Authors: R Jamuna | Johny Renoald Albert | Rahul Gujar | S Karvendhan | Poonam Yadav | T Sathish | K Brahma Raju | Sanskriti Gujar
Source: Journal of Environmental Nanotechnology (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

AI- and ML-driven Multimodal Analysis of Nanoparticle Morphology, Composition, and Dynamics The shape, composition, and kinetic properties of nanoparticles essentially define their application potential in catalysis, advanced materials, and nanomedicine. Current SEM/TEM analysis techniques rely on laborious manual processing steps that limit throughput and introduce subjectivity, which can potentially discourage large-scale studies. A Segment Anything Model (SAM)-based framework was developed for accurate nanoparticle segmentation, integrating EDS data and graph neural networks (GNNs) trained on time-resolved images to provide comprehensive characterization. The proposed pipeline delivers predictions of catalytic performance that are 15% more accurate than those obtained with conventional regression models, reduces analysis time from days to hours, and remains robust when characterizing diverse nanoparticle morphologies. The approach provides quantitative morphological parameters and elemental distribution patterns, as well as sub-second resolution for tracking aggregation dynamics. Such an integrated methodology enables rapid screening and targeted optimization of nanoparticles, especially for nanoparticle-driven applications such as drug delivery platforms and catalytic systems, where particle shape plays a decisive role in intrinsic performance. Pipeline (software) ; Nanoparticle ; Artificial neural network ; Limit (mathematics) ; Tracking (education) ; Throughput Machine Learning in Materials Science ; Nanomaterials for catalytic reactions ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Haifan Zhou | Yue Fang | Hancheng Ma | Alexander Perez Roxas | Puqing Deng | Guozhen Zhang | Yiming Wang | Yi Zhang | Wei Zhou | Linyan Li | Fung Ho Hung | Zhengtang Luo | Hanyu Gao
Source: Chemical Engineering Journal (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Large language models for automated data extraction and inverse design of copolymers with targeted glass transition temperatures Bottleneck ; Copolymer ; Inverse ; Glass transition ; Pipeline (software) ; Language model ; Bridging (networking) ; Thermal ; Data extraction Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Feng Hu | Estrid He | Karin Verspoor
Source: Research Square (preprint, 2026-02-19)
Field: Materials Science — Materials Chemistry

AdaptMol: Domain Adaptation for Molecular Image Recognition with Limited Supervision Robustness (evolution) ; Pipeline (software) ; ENCODE ; Domain adaptation ; Domain (mathematical analysis) ; Pattern recognition (psychology) ; Feature (linguistics) ; Transfer of learning ; Training set Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Computational Drug Discovery Methods
Authors: Weizhi Tian | Zepeng Jia | Tiren Peng | Zhiguo Wang | Zhikai Gao | Zishan Luo | Xi Sun | Hang Zhang | Yuhang Zhou | Rong Feng | Hongkuan Yuan | Hong Cui
Source: International Journal of Hydrogen Energy (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Bi/Cd synergistic engineering in quaternary TiFe alloys via feature expansion for enhanced hydrogen storage kinetics Hydrogen storage ; Ternary operation ; Hydride ; Hydrogen ; Alloy ; Gravimetric analysis ; Solid solution ; Density functional theory Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Maurício Dalla Costa Rodrigues da Silva | Luís Fernando Wentz Brum | Leandro Rodrigues Oviedo | Giovani Pavoski | Denise Crocce Romano Espinosa | William Leonardo da Silva
Source: Surfaces and Interfaces (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

Application of machine learning on the prediction of Niobium Oxide V nanoparticles’ properties for use in heterogeneous photocatalysis Metal oxide nanoparticles are promising materials for applications in a wide range of catalytic reactions. Among them, niobium oxide V nanoparticles (Nb 2 O 5 -NPs) present unique acid sites, stability, and electronic properties suitable for application in photocatalysis. However, despite these advantages, the number of studies using Nb 2 O 5 -NPs in photocatalysis remains minor compared to other nanomaterials. In this sense, the application of computational studies using Machine Learning (ML) can help the development of the field, by using algorithms to predict nanoparticle properties and identify cluster regions for further investigations. In this context, the present study aims to evaluate the application of ML study to predict the values of specific surface area and band gap energy of Nb 2 O 5 -NPs. A survey of data available in the literature was carried out for the exploratory analysis of the main synthesis variables and the prediction of specific surface area and bang gap energy values through the evaluation of three tree-based ML algorithms: Random Forest (RF), Extreme Gradient Boosting (XGB), and Gradient Boosting Regressor (GBR). The results of the exploratory analysis showed the presence of cluster regions due to the type of routes and possibilities for further studies to develop new Nb 2 O 5 -NPs for photocatalysis. The ML study showed that the GBR model presents the best performance to predict the values of specific surface area and band gap energy of Nb 2 O 5 -NPs based on the selected synthesis conditions. Therefore, the use of ML is presented as a tool to be used to evaluate Nb 2 O 5 -NPs properties before carrying out experimental steps. Photocatalysis ; Niobium oxide ; Gradient boosting ; Niobium ; Band gap ; Oxide ; Nanoparticle ; Boosting (machine learning) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Yin, Wenjie
Source: Open MIND (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

[under submission] Released Code,Dataset and inference results of DRCAT This repo is for a paper under submission: A Chemistry-Informed Deep Learning Network for Mitigating the Stratospheric OH Data Gap. It includes source code, training and inference dataset for DRCAT model. Also, the inference results of DRCAT are also provided here. The backup link of DRCAT can refer to github link: https://github.com/TOMCAT-CTM/DRCAT Inference ; Code (set theory) ; Source code ; Deep learning ; Training set ; Training (meteorology) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Runhan Shi | Zhicheng Zhang | Letian Chen | Gufeng Yu | Yang Yang
Source: Open MIND (preprint, 2026-02-07)
Field: Chemistry — Spectroscopy

Learning Molecular Chirality via Chiral Determinant Kernels Chirality is a fundamental molecular property that governs stereospecific behavior in chemistry and biology. Capturing chirality in machine learning models remains challenging due to the geometric complexity of stereochemical relationships and the limitations of traditional molecular representations that often lack explicit stereochemical encoding. Existing approaches to chiral molecular representation primarily focus on central chirality, relying on handcrafted stereochemical tags or limited 3D encodings, and thus fail to generalize to more complex forms such as axial chirality. In this work, we introduce ChiDeK (Chiral Determinant Kernels), a framework that systematically integrates stereogenic information into molecular representation learning. We propose the chiral determinant kernel to encode the SE(3)-invariant chirality matrix and employ cross-attention to integrate stereochemical information from local chiral centers into the global molecular representation. This design enables explicit modeling of chiral-related features within a unified architecture, capable of jointly encoding central and axial chirality. To support the evaluation of axial chirality, we construct a new benchmark for electronic circular dichroism (ECD) and optical rotation (OR) prediction. Across four tasks, including R/S configuration classification, enantiomer ranking, ECD spectrum prediction, and OR prediction, ChiDeK achieves substantial improvements over state-of-the-art baselines, most notably yielding over 7% higher accuracy on axially chiral tasks on average. Chirality (physics) ; Representation (politics) ; Circular dichroism ; Stereocenter ; Kernel (algebra) ; Enantiomer ; Axial chirality Molecular spectroscopy and chirality ; Axial and Atropisomeric Chirality Synthesis ; Origins and Evolution of Life
Authors: Runhan Shi | Zhicheng Zhang | Letian Chen | Gufeng Yu | Yang Yang
Source: ArXiv.org (article, 2026-02-07)
Field: Chemistry — Spectroscopy

Learning Molecular Chirality via Chiral Determinant Kernels Chirality is a fundamental molecular property that governs stereospecific behavior in chemistry and biology. Capturing chirality in machine learning models remains challenging due to the geometric complexity of stereochemical relationships and the limitations of traditional molecular representations that often lack explicit stereochemical encoding. Existing approaches to chiral molecular representation primarily focus on central chirality, relying on handcrafted stereochemical tags or limited 3D encodings, and thus fail to generalize to more complex forms such as axial chirality. In this work, we introduce ChiDeK (Chiral Determinant Kernels), a framework that systematically integrates stereogenic information into molecular representation learning. We propose the chiral determinant kernel to encode the SE(3)-invariant chirality matrix and employ cross-attention to integrate stereochemical information from local chiral centers into the global molecular representation. This design enables explicit modeling of chiral-related features within a unified architecture, capable of jointly encoding central and axial chirality. To support the evaluation of axial chirality, we construct a new benchmark for electronic circular dichroism (ECD) and optical rotation (OR) prediction. Across four tasks, including R/S configuration classification, enantiomer ranking, ECD spectrum prediction, and OR prediction, ChiDeK achieves substantial improvements over state-of-the-art baselines, most notably yielding over 7% higher accuracy on axially chiral tasks on average. Chirality (physics) ; Representation (politics) ; Circular dichroism ; Stereocenter ; Kernel (algebra) ; Enantiomer ; Axial chirality Molecular spectroscopy and chirality ; Axial and Atropisomeric Chirality Synthesis ; Origins and Evolution of Life
Authors: Long Liu | Murat Yaylaci
Source: International Journal of Mechanics and Materials in Design (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Coupled aeroelastic–nanomechanical modeling of functionally graded piezoelectric tapered nanoplates used in sports equipment: results verification via a hybrid machine learning algorithm Aeroelasticity ; Nonlinear system ; Nanoelectromechanical systems ; Piezoelectricity ; Artificial neural network ; Solid mechanics ; Aerodynamics ; Fuzzy logic ; Coupling (piping) Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Thermoelastic and Magnetoelastic Phenomena
Authors: Pietro Chiarantoni | Oscar Serra | Mohammad Erfan Mowlaei | Venkata Surya Kumar Choutipalli | Mark DelloStritto | Xinghua Shi | Michael L. Klein | Vincenzo Carnevale
Source: Journal of Chemical Theory and Computation (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Generative Modeling of Entangled Polymers with a Distance-Based Variational Autoencoder We present a variational autoencoder framework for learning and generating configurations of structured polymer globules from distance matrices. We used coarse-grained molecular dynamics to sample polyethylene structures, which we used as the training set for our deep learning model. By combining convolution and attention layers, the model encodes the structural patterns of distance matrices into an organized and rototranslationally invariant latent space of lower dimensionality. The generative capability of the variational autoencoder, coupled with a postprocessing pipeline based on multidimensional scaling and short molecular dynamics, enables the recovery of physically meaningful configurations. The reconstructed and generated samples reproduce key observables, including energy, size, and entanglement, despite minor discrepancies in the raw decoder output. Interestingly, the length of the molecular dynamics relaxation required to generate canonical structures starting from the decoder outputs is 1 order of magnitude smaller than the corresponding relaxation time needed to sample structures using a pure MD approach, suggesting that our method can offer computational advantages in sampling uncorrelated structures. Autoencoder ; Scaling ; Generative model ; Convolution (computer science) ; Relaxation (psychology) ; Invariant (physics) ; Pattern recognition (psychology) ; Set (abstract data type) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Xinwei Wang | Irea Mosquera‐Lois | Aron Walsh
Source: Open MIND (preprint, 2026-03-05)
Field: Materials Science — Materials Chemistry

Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects Machine learning interatomic potentials (MLIPs) can now reproduce the energy, forces and stresses of bulk materials with high accuracy compared to first-principles calculations. The description of imperfections, where coordination environments and electron counts deviate from those found in pristine reference structures, remains a challenge. We find that the current generation of foundation MLIPs do not describe the defect physics of the semiconductor Sb2Se3. We introduce global defect charge embeddings that distinguish the bonding characteristics of different charge states. We further employ a multi-fidelity approach that combines low-cost (semi-local exchange-correlation functional) reference data with high-quality (non-local hybrid functional) energies and forces that describe well the subtleties of the defect energy landscape. The resulting defect-capable force fields can find stable structural configurations and predict charge-transition levels in quantitative agreement with direct quantum mechanical calculations, at a fraction of the computational cost. Charge (physics) ; Interatomic potential ; Point (geometry) ; Energy (signal processing) ; Electron ; Quantum ; Crystallographic defect ; Semiconductor Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Inorganic Chemistry and Materials
Authors: Joanna Zou | Youssef Marzouk
Source: arXiv (Cornell University) (preprint, 2026-03-23)
Field: Materials Science — Materials Chemistry

Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes The development of machine learning interatomic potentials faces a critical computational bottleneck with the generation and labeling of useful training datasets. We present a novel application of determinantal point processes (DPPs) to the task of selecting informative subsets of atomic configurations to label with reference energies and forces from costly quantum mechanical methods. Through experiments with hafnium oxide data, we show that DPPs are competitive with existing approaches to constructing compact but diverse training sets by utilizing kernels of molecular descriptors, leading to improved accuracy and robustness in machine learning representations of molecular systems. Our work identifies promising directions to employ DPPs for unsupervised training data curation with heterogeneous or multimodal data, or in online active learning schemes for iterative data augmentation during molecular dynamics simulation. Bottleneck ; Robustness (evolution) ; Training set ; Data point ; Task (project management) ; Point (geometry) ; Interatomic potential ; Process (computing) ; Data curation Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Quantum Computing Algorithms and Architecture
Authors: Valerii Andreichev | Sena Aydin | Kai Töpfer | Markus Meuwly | Luis Itza Vazquez-Salazar
Source: Machine Learning Science and Technology (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Design, assessment, and application of machine learning potential energy surfaces Abstract Potential energy surfaces (PES) are an indispensable tool in the investigation, characterization and understanding of chemical and biological systems in the gas and condensed phases. Advances in machine learning (ML) methodologies have led to the development of ML-PES, which are now widely used to simulate such systems. This work provides an overview of concepts, methodologies and recommendations for constructing and using ML-PESs. The choice of topics is focused on the practical issues that are commonly found. Application of the principles discussed are illustrated through two different systems of biomolecular importance: the non-reactive dynamics of the Alanine-Lysine-Alanine tripeptide in gas and solution phases, and double proton transfer reactions in DNA base pairs. Energy (signal processing) ; Work (physics) ; Base (topology) ; Machine tool ; Table (database) ; Energy transfer Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Surface Chemistry and Catalysis
Authors: Vsevolod Biryukov | Kamal Choudhary | Timur Bazhirov
Source: arXiv (Cornell University) (preprint, 2026-03-29)
Field: Materials Science — Materials Chemistry

AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D Artificial intelligence (AI) and machine learning (ML) models in materials science are predominantly trained on ideal bulk crystals, limiting their transferability to real-world applications where surfaces, interfaces, and defects dominate. We present Mat3ra-2D, an open-source framework for the rapid design of realistic two-dimensional materials and related structures, including slabs and heterogeneous interfaces, with support for disorder and defect-driven complexity. The approach combines: (1) well-defined standards for storing and exchanging materials data with a modular implementation of core concepts and (2) transformation workflows expressed as configuration-builder pipelines that preserve provenance and metadata. We implement typical structure generation tasks, such as constructing orientation-specific slabs or strain-matching interfaces, in reusable Jupyter notebooks that serve as both interactive documentation and templates for reproducible runs. To lower the barrier to adoption, we design the examples to run in any web browser and demonstrate how to incorporate these developments into a web application. Mat3ra-2D enables systematic creation and organization of realistic 2D- and interface-aware datasets for AI/ML-ready applications. Workflow ; Modular design ; Pipeline (software) ; Documentation ; Transferability ; Limiting ; Template ; User interface Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions