jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Job H. Berkhout | Merel Florian | Domenico Gadaleta | Aldert H Piersma | Harm J. Heusinkveld
Source: Journal of Cheminformatics (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

A pipeline for developing AI-driven models to predict molecular initiating events: a case study on neural tube defects Adverse Outcome Pathways (AOPs) describe the sequence of molecular and cellular events that lead to toxicity. Each pathway begins with a Molecular Initiating Event (MIE) and ends in an Adverse Outcome. Early identification of chemical activity on MIE-relevant protein targets supports first-line toxicity assessment and helps researchers prioritize mechanisms for subsequent experimental investigation. Here we present an automated AI pipeline that converts raw ChEMBL bioactivity data into optimized deep learning models for MIE prediction. The pipeline builds on the Knowledge-Guided Pre-training of Graph Transformer (KPGT) framework, which represents chemical structures as knowledge-enriched molecular graphs. It integrates data curation, molecular graph generation, and model training and tuning. This integration enables users to construct target-specific prediction models in a seamless and reproducible way, starting from initial data and ending with deployable AI. We demonstrate its use in a neural tube defect (NTD) case study, where fine-tuned KPGT models outperformed traditional Support Vector Machine models with a radial basis function kernel (SVM-RBF) when predicting MIEs linked to developmental toxicity. The results highlight the potential of AI-driven toxicity modeling to accelerate AOP development, improve endpoint prioritization, and prioritize chemicals for experimental follow-up. By providing an end-to-end, data-to-model workflow, the pipeline lowers the technical barrier to using modern graph-based neural architectures in toxicology. It offers a reproducible route to deployable MIE prediction models that support AOP development, compound prioritization, and early-stage chemical safety evaluation. Pipeline (software) ; Artificial neural network ; Neural tube ; Tube (container) ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Y. Liu | Yajie Zhang | Tianmeng He | Zhengsi Yin | Chenhao Li | Xiaoyi Xue | Hao Wang | Ao Li | Suojiang Zhang
Source: Advanced Functional Materials (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Material Innovations and System Challenges in Hydrogen Storage: A Comprehensive Review ABSTRACT As a critical enabler for the global transition to low‐carbon energy, hydrogen storage technologies are undergoing rapid innovation and diversification. This review systematically examines the four dominant hydrogen storage technologies, i.e., high‐pressure gaseous, cryogenic liquid, solid‐state (AB 5 , AB 2 , superlattices, magnesium‐based materials, metal‐organic frameworks (MOF) and activated carbon) and liquid organic hydrogen carriers (LOHCs) hydrogen storage technologies, highlighting their research advances in storage density, cost, energy consumption, hydrogen storage/release mechanisms, system integration and applications. Whilst high‐pressure gaseous hydrogen storage remains the most mature technology, breakthroughs in high‐capacity storage materials such as nanostructured magnesium‐based materials, MOFs, and novel LOHCs catalysts, continuing to enhance storage capacity, kinetic and thermodynamic performances, and cycling stability. Looking ahead, the field is evolving toward machine learning‐based intelligent material design and the development of hybrid energy systems integrating energy storage with flexible thermal/electrical loads. Establishing a diversified portfolio of hydrogen storage technologies, tailored for specific applications ranging from transportation to grid balancing, will effectively drive the large‐scale adoption of hydrogen energy. Hydrogen storage ; Hydrogen technologies ; Hydrogen ; Energy storage ; Enabling ; Energy carrier ; Hydrogen economy ; Hydrogen vehicle Hydrogen Storage and Materials ; Metal-Organic Frameworks: Synthesis and Applications ; Layered Double Hydroxides Synthesis and Applications
Authors: Dylan Bissuel | Léo Orveillon | Benjamin Arrondeau | Paulo Mendonça | Irina Piazza | Martin Uhrin | E Polack | Akshay Krishna Ammothum Kandy | David Martin-Calle | Jonathan Chapignac | A. Arivazhagan | Lorenzo Paulatto | Pierre-Antoine Bouttier | Magali Richard | Thierry Deutsch | David Rodney | A. Marco Saitta | Nöel Jakse
Source: Advanced Engineering Materials (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Reproducible Container Solutions for Codes and Workflows in Materials Science A computing solution combining the GNU Guix functional package manager with the Apptainer container system is presented. This approach provides fully declarative and reproducible software environments suitable for computational materials science. Its versatility and performance enable the construction of complete frameworks integrating workflow managers such as AiiDA and Ewoks that can be deployed on HPC infrastructures. The efficiency of the solution is illustrated through several examples: (i) AiiDA workflows for automated dataset construction and analysis as well as path‐integral molecular dynamics based on ab initio calculations; (ii) workflows for the training of machine‐learning interatomic potentials; and (iii) an Ewoks workflow for the automated analysis of coherent X‐ray diffraction data in large‐scale synchrotron facilities. These examples demonstrate that the proposed environment provides a reliable and reproducible basis for computational and data‐driven research in materials science. Workflow ; Container (type theory) ; Software ; Workflow management system ; Automation ; Supercomputer Machine Learning in Materials Science ; Scientific Computing and Data Management ; Enzyme Structure and Function
Authors: Wenguang Hu | Zebin Wu | Menglu Li | Shan Feng | Hangbo Qi | Xingjian Lu | Xiaotao Zu | Haiyan Xiao | Liang Qiao
Source: npj Computational Materials (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Accelerated discovery of supertetragonal perovskites with giant polarization via machine learning Ferroelectric perovskites with giant spontaneous polarization have extensive applications in electronic devices, energy conversion, sensor and so on. However, the rapid discovery of new perovskites with giant polarization remains an open challenge especially when thousands of candidates are treated. Here, combining machine learning (ML) and first-principles calculations, we successfully predict 8 perovskites with giant polarization from 2021 different possible compounds, among which seven candidates have never been reported before. These perovskites have large c/a ratio and giant polarization compared to the reported ferroelectric perovskites, and room temperature stability. Among them, the polarization of SnFeO3 with G-AFM magnetic ordering is as high as 138.63 µC/cm2. The non-magnetic SrPbO3 and magnetic EuSnO3 not only exhibit giant polarization, but also possess band gaps close to the ideal value for photovoltaic applications, showing great potential in the field of ferroelectric photovoltaics. Besides, polarity and metallicity coexist in SnFeO3 and CaTaO3, which are suggested to have potential applications in fields such as spintronics and superconductivity. This work thus provides an effective strategy for discovering new functional materials. Ferroelectricity ; Polarization (electrochemistry) ; Spintronics ; Giant magnetoresistance Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Multiferroics and related materials
Authors: Wanyi Zeng | Minzhong Huang | BIN CHEN | Kok Bing Tan | Dongren Cai | Wanchai Pluempanupat | Guowu Zhan
Source: Chinese Journal of Chemical Engineering (article, 2026-02-01)
Field: Chemistry — Inorganic Chemistry

Engineering metal–organic framework architectures for enhanced carbon dioxide capture Adsorption ; Surface engineering ; Carbon capture and storage (timeline) ; Commercialization ; Scalability ; Rational design ; Lead (geology) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: PAULO SÉRGIO DE ANDRADE | 10 MATH
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Conquering the Curse of Dimensionality with Graph-Enhanced Scientific Machine Learning The "curse of dimensionality" presents significant challenges in scientific machine learning (SciML), manifesting as data sparsity, increased computational complexity, and degraded model performance, particularly in high-dimensional datasets prevalent in scientific domains. Traditional machine learning algorithms often struggle to generalize effectively in such vast feature spaces, leading to overfitting and unreliable predictions. This paper explores the integration of graph-enhanced techniques into SciML to mitigate these issues. By leveraging graph structures, which inherently capture relational information and topological properties, data can be represented in a more meaningful and compact form. This approach allows for the encoding of physical interactions, chemical bonds, or functional relationships, thereby providing a powerful inductive bias that reduces the effective dimensionality of the learning problem. We review existing challenges posed by high-dimensional data, discuss the theoretical underpinnings and practical advantages of graph-enhanced methods, and propose a general framework for their application in scientific contexts. Our analysis suggests that graph-enhanced SciML can lead to more robust, interpretable, and computationally efficient models, ultimately advancing discovery in fields ranging from materials science to drug design by moving beyond mere statistical correlations to capture underlying scientific principles. Overfitting ; Curse of dimensionality ; Dimensionality reduction ; Feature (linguistics) ; Big data ; Computational learning theory ; Nonlinear dimensionality reduction ; Graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topological and Geometric Data Analysis
Authors: Beichen Dai | Chang-Chun Ding | Yang-Shun Lan | Junshan Hu | Wei Jin | Tong Liu
Source: The Journal of Physical Chemistry C (article, 2026-02-13)
Field: Chemistry — Inorganic Chemistry

An Effective Screening Method of CO <sub>2</sub> Adsorption and Selectivity for Metal–Organic Frameworks by Machine Learning and High-Throughput Computation Due to the large specific surface area, adjustable pore structure, diverse active sites, and flexible structural design, MOFs have significant advantages in gas adsorption and electrocatalysis, but the low utilization rate of catalytic sites and the poor reactant/product selectivity also hinder the completed application in industry. Therefore, based on the relationship between the structure properties (void fraction, gravimetric surface area, largest cavity diameter, pore limiting diameter, density and metal percentage) and the potential catalysis features (reactant adsorption, activation and reactant/product selectivity) in MOFs, the corresponding screening method by combining machine learning and high-throughput computing proposed in the present work will be helpful to put forward a rule of potential catalysis for the capture and conversion of CO2 as well as the release of products CH4. Determined from the experimental MOFs database ToBaCCo, 6200 MOFs were randomly selected to perform the machine learning training (ML) of the adsorption of CO2 in single adsorption NCO2, trade-off between adsorption and selectivity (TSN) in CO2/CH4 as well as the adsorption enthalpy of CO2 and CH4 in binary adsorption (HCO2 and HCH4)). The results revealed that the density (P) and gravimetric surface area (GSA) played an important role in the adsorption performance of MOFs for capturing CO2, separation, and conversion. After screening the four widely used databases (coremof2019, coremof2014, hmofs, ToBaCCo), the optimal MOFs due to the suitable P (0.5 g/cm3 > P > 0.8 g/cm3) and GSA (5500 m2/g > GSA > 1000 m2/g) generated the preferable CO2 adsorption capacity and selectivity. Adsorption ; Gravimetric analysis ; Selectivity ; Limiting ; Catalysis ; Work (physics) Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Carbon Dioxide Capture Technologies
Authors: PAULO SÉRGIO DE ANDRADE | 10 MATH
Source: Open MIND (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Conquering the Curse of Dimensionality with Graph-Enhanced Scientific Machine Learning The "curse of dimensionality" presents significant challenges in scientific machine learning (SciML), manifesting as data sparsity, increased computational complexity, and degraded model performance, particularly in high-dimensional datasets prevalent in scientific domains. Traditional machine learning algorithms often struggle to generalize effectively in such vast feature spaces, leading to overfitting and unreliable predictions. This paper explores the integration of graph-enhanced techniques into SciML to mitigate these issues. By leveraging graph structures, which inherently capture relational information and topological properties, data can be represented in a more meaningful and compact form. This approach allows for the encoding of physical interactions, chemical bonds, or functional relationships, thereby providing a powerful inductive bias that reduces the effective dimensionality of the learning problem. We review existing challenges posed by high-dimensional data, discuss the theoretical underpinnings and practical advantages of graph-enhanced methods, and propose a general framework for their application in scientific contexts. Our analysis suggests that graph-enhanced SciML can lead to more robust, interpretable, and computationally efficient models, ultimately advancing discovery in fields ranging from materials science to drug design by moving beyond mere statistical correlations to capture underlying scientific principles. Overfitting ; Curse of dimensionality ; Dimensionality reduction ; Feature (linguistics) ; Big data ; Computational learning theory ; Nonlinear dimensionality reduction ; Graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topological and Geometric Data Analysis
Authors: Shahed Haghiri | Andrés S. Urbina | Lyudmila V. Slipchenko
Source: The Journal of Chemical Physics (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Beyond the cutoff: Hybrid ML/MM electrostatics for neural network potentials Atomistic Neural Network Potentials (NNPs) have been developed to predict molecular properties and electronic ground-state energies of small molecules with kcal/mol accuracy. However, despite their excellent scalability and low computational cost, atomistic NNPs are intrinsically local, making them unreliable for modeling long-range interactions in condensed-phase systems relevant to biological, engineering, and pharmaceutical applications. Recently, we demonstrated how to explicitly incorporate information on long-range interactions into the ANI NNP by retraining it in the presence of electrostatic potentials arising from the molecular environment. Effectively, the introduced embedded ANI/MM model is similar in spirit to quantum mechanics/molecular mechanics. Here, we extend this line of work by developing and training the ANI/MM network to predict binding energies for two protein-ligand complexes. We show that this network predicts forces with an error of less than 1 kcal/mol/Å, opening the possibility of using it for geometry optimizations and molecular dynamics. The resulting ANI/MM NNP outperforms the accurate, ab initio-fitted classical force field Q-Force and exhibits good transferability to new solutes, provided that the training set includes relevant structural fragments of the target molecule. Together, these findings demonstrate that hybrid ML/MM neural architectures offer a promising route toward chemically accurate, scalable modeling of complex molecular systems. Scalability ; Artificial neural network ; Transferability ; Electrostatics ; Molecular dynamics ; Set (abstract data type) ; Force field (fiction) ; Field (mathematics) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Physical and Chemical Molecular Interactions
Authors: Xinyu Yan | Xu Dong Sheng | Toshihiro Omori | Takahiro Kaneko | Kyosuke Yoshimi
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Fracture Surface Morphology–Property Relationships in Mo–Ti–C Alloys Revealed by Quantitative Descriptor Analysis Fracture (geology) ; Fractal dimension ; Fracture toughness ; Surface finish ; Characterization (materials science) ; Texture (cosmology) ; Fracture mechanics ; Surface roughness Microstructure and mechanical properties ; Titanium Alloys Microstructure and Properties ; Material Properties and Failure Mechanisms
Authors: Ting Wang | Qiankun Li | Li Jin | Chuanlai Liu | Jian Zeng | Fang Wang | Shuo Dong | Fulin Wang | Jie Dong
Source: Journal of Material Science and Technology (article, 2026-03-19)
Field: Materials Science — Biomaterials

Interpretable machine learning for predicting tensile twinning and revealing microstructural influences in pure Mg Crystal twinning ; Ultimate tensile strength ; Electron backscatter diffraction ; Nucleation ; Grain boundary ; Tensile testing ; Deformation (meteorology) Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties ; Boron and Carbon Nanomaterials Research
Authors: Jiabin Chen | Wenkai Zhao | Li Zhang | Guankui Long | Yu‐Wu Zhong | Jiannian Yao
Source: Science China Materials (article, 2026-03-17)
Field: Chemistry — Organic Chemistry

Unraveling the origin of circularly polarized luminescence by first-principles calculations Luminescence ; Photoluminescence ; Dipole ; Quantum ; Circular polarization ; Molecule ; Chirality (physics) Synthesis and Properties of Aromatic Compounds ; Magnetism in coordination complexes ; Luminescence and Fluorescent Materials
Authors: Martin Hoffmann Petersen | Steen Lysgaard | Arghya Bhowmik | Kedar Hippalgaonkar | Juan Maria Garcia Lastra
Source: arXiv (Cornell University) (preprint, 2026-03-27)
Field: Materials Science — Materials Chemistry

Importance of Electronic Entropy for Machine Learning Interatomic Potentials Machine learning interatomic potentials (MLIPs) enable large-scale atomistic simulations but remain challenged in describing mixed-valence materials where charge ordering strongly influences thermodynamic stability. Here we investigate the role of electronic entropy in MLIP structural optimization of the battery cathode material \ce{NaFePO4}. We show that conventional MLIPs fail to reproduce the correct stability of intermediate \ce{Na} concentrations because structural optimization leads to incorrect \ce{Fe^{2+}}/\ce{Fe^{3+}} charge assignments, resulting in erroneous energy ordering and convex-hull predictions. Analysis of magnetic moments during structural optimization reveals that MLIPs are unable to capture electronic entropy associated with charge ordering. To address this limitation, we introduce an approach that embeds charge-state information directly into the MLIP representation by distinguishing between \ce{Fe^{2+}} and \ce{Fe^{3+}} environments during training. Retraining CHGNet, cPaiNN, and MACE with this representation enables accurate structural optimization, correct identification of charge ordering, and improved agreement with density functional theory convex hulls. Our results demonstrate that incorporating electronic entropy into MLIP representations is essential for modeling charge-disordered materials and provide a practical framework for extending MLIP simulations to mixed-valence transition-metal systems. Entropy (arrow of time) ; Electronic structure ; Representation (politics) ; Charge (physics) ; Density functional theory ; Principle of maximum entropy ; Charge density ; Optimization problem Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Thermal Expansion and Ionic Conductivity
Authors: Doaa Mohamed | Samuel García Vázquez | Behnam Mardani | Victor Dudarev | Alfred Ludwig | Maribel Quiñonez Acosta | Markus Stricker
Source: arXiv (Cornell University) (preprint, 2026-01-14)
Field: Materials Science — Materials Chemistry

Field report from Collaborative Research Center 1625: Heterogeneous research data management using ontology representations The goal of the Collaborative Research Center 1625 is the establishment of a scientific basis for the atomic-scale understanding and design of multifunctional compositionally complex solid solution surfaces. Next to materials synthesis in form of thin-film materials libraries, various materials characterization and simulations techniques are used to explore the materials data space of the problem. Machine learning and artificial intelligence techniques guide its exploration and navigation. The effective use of the combined heterogeneous data requires more than just a simple research data management plan. Consequently, our research data management system maps different data modalities in different formats and resolutions from different labs to the correct spatial locations on physical samples. Besides a graphical user interface, the system can also be accessed through an application programming interface for reproducible data-driven workflows. It is implemented by a combination of a custom research data management system designed around a relational database, an ontology which builds upon materials science-specific ontologies, and the construction of a Knowledge Graph. Along with the technical solutions of research data management system and lessons learned, first use cases are shown which were not possible (or at least much harder to achieve) without it. Ontology ; Data management ; Field (mathematics) ; Interface (matter) ; Relational database ; User interface ; Component (thermodynamics) ; Data model (GIS) ; Data modeling Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Jayram Desai | Sayan Das | Vikranth Racherla
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Large Deformation Behavior of powerlaw fluid-elastomer composites with application to self healing materials: 3D simulations, analytical framework, and ML model Hyperelastic material ; Elastomer ; Self-healing ; Deformation (meteorology) ; Compression (physics) ; Composite number ; Flow (mathematics) Polymer composites and self-healing ; Composite Material Mechanics ; Elasticity and Material Modeling
Authors: Zhaoxuan He | Shenggui Zhang Huiling Lin | Xingyu Liu | Wenqian Li | Yanyong Mao | Hao Shi
Source: Food Research International (article, 2026-01-13)
Field: Materials Science — Biomaterials

Artificial intelligence films for dynamic and sustainable preservation of high-value fruits Active packaging ; Postharvest ; Process (computing) ; Food packaging ; Sustainability ; Intelligent decision support system Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Polymer-Based Agricultural Enhancements