jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abolfazl Mokhtari | Mohammad Hossein Nikzad
Source: Materials Today Communications (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

From classical machine learning to modern neural networks for multi-output prediction of metallic composites mechanical properties using a small molecular dynamics-driven dataset: Application to Al–Cu alloys with pre-existing voids Artificial neural network ; Metal ; Deep neural networks ; Composite number Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Muhammad Asif Shakoori | Misbah Khan | Baiqiu Liu | H. Li | Rui-Qin Zhang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

In-plane thermal transport in graphene/quasi-hexagonal phase C60 heterostructure: insight from machine learning molecular dynamics Thermal conductivity ; Graphene ; Nanoelectronics ; Anisotropy ; van der Waals force ; Heterojunction ; Molecular dynamics ; Phase (matter) Thermal properties of materials ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Julia Schumann | Hampus Näsström | M Götte | Lauri Himanen | Abdulrhman Moshantaf | Markus Scheidgen | J.A. Marquez | Claudia Draxl | Annette Trunschke
Source: ChemRxiv (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Towards a new era for open and FAIR data in catalysis research – A catalysis plugin for NOMAD The field of catalysis currently lacks a structured repository for experimental data, and the publication of machine-readable datasets remains uncommon. To address this gap and support FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, we introduce a new plugin within the NOMAD platform for managing and publishing heterogeneous catalysis data, the nomad_catalysis plugin. This plugin enables the upload of structured experimental data and metadata with built-in visualization and alignment to the community-developed vocabulary Voc4Cat, ensuring long-term interpretability. In addition to facilitating efficient data sharing, the catalysis app offers intuitive search functionality, enabling researchers to quickly identify relevant catalytic reactions, catalyst materials, reaction conditions and kinetic properties. This infrastructure lays the foundation for advanced data analytics and machine learning applications, supporting more efficient and reproducible catalyst development. Upload ; Plug-in ; Analytics ; Metadata ; Visualization ; Field (mathematics) ; Controlled vocabulary Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Zhuang Li | Shan Lei | Yi Gao | Mengjun Zhu | Saiwen Zhang | Jianguo Lin
Source: Zhongguo kexue. Wulixue Lixue Tianwenxue (article, 2026-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable Machine Learning-Driven Synergistic Optimization of Magnetocaloric Properties and Design of Room-Temperature Refrigeration Materials for Amorphous Alloys Magnetic refrigeration ; Refrigeration ; Amorphous metal ; Amorphous solid ; Alloy Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science ; Shape Memory Alloy Transformations
Authors: R. Juneja | M. Wirtz | P. A. Huber | J. Rapp | E. Unterberg
Source: Journal of Nuclear Materials (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Machine learning for microstructure synthesis in fusion materials: A physics-aware validation framework for tungsten plasma-facing components Microstructure ; Similarity (geometry) ; Tungsten ; Kernel (algebra) ; Discriminator ; Fusion ; Workflow ; Pattern recognition (psychology) Fusion materials and technologies ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Salman Arafath Mohammed
Source: International Journal of Integrated Research and Practice (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Meets Materials Science: Predicting Novel Compounds The field of artificial intelligence (AI) and materials science are coming together to transform the process of discovering and designing new compounds. The traditional means of creating materials include the trial-and-error-based method of materials development, the hypothetical estimation, and the long-term laboratory testing, which in the vast majority of instances consume much time and money. The paper will explore how AI-based algorithms including machine learning and deep neural networks enhance predictive power in connection with the identification of new materials with particular structural, electronic and mechanical properties. The AI systems with the assistance of enormous experimental data and findings of the computational simulation can detect complex patterns that are not discoverable with the help of classical approaches to analysis. The paper takes into account the unsupervised and supervised learning approaches to the materials datasets and their capacity to accelerate the process of screening of compounds and optimization of chemical structures. It is concerned with pre-processing of the data, feature engineering as well as validation of the model to ensure reliability and reproducibility. The case analysis confirms that predictive models have been useful in the discovery of alloys and energy storage materials and semiconductor compounds that are more efficient and stable. In addition, the hybrid solution, that is the combination of AI and density functional theory and high-throughput computational strategies, is an example of a solution that can strike a balance between the efficiency of the computations and the scientific interpretability. Even though major progress has been made, there are still such issues as the lack of data, the transparency of models, and the adaptation to the domain. There are also issues about ethical concerns and necessity of cooperative platforms between computational scientists and experimental researchers. The results indicate that AI-based materials prediction is not only able to shorten development times but also sustainable innovation by eliminating resource waste and experiment redundancy. This cross-disciplinary combination eventually forms a platform of materials discovery in the next generation, which makes AI the core instrument of scientific and industrial application in the fields of energy, electronics, healthcare and environmental technologies. Field (mathematics) ; Process (computing) ; Artificial neural network ; Identification (biology) ; Reliability (semiconductor) ; Computation ; Deep learning ; Computational intelligence ; Feature (linguistics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Big Data and Digital Economy
Authors: Shuyu Bi | Zhede Zhao | Qiangchao Sun | Tao Hu | Xionggang Lu | Hongwei Cheng
Source: arXiv (Cornell University) (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Universal and efficient graph neural networks with dynamic attention for machine learning interatomic potentials The core of molecular dynamics simulation fundamentally lies in the interatomic potential. Traditional empirical potentials lack accuracy, while first-principles methods are computationally prohibitive. Machine learning interatomic potentials (MLIPs) promise near-quantum accuracy at linear cost, but existing models still face challenges in efficiency and stability. We presents Machine Learning Advances Neural Network (MLANet), an efficient and robust graph neural network framework. MLANet introduces a dual-path dynamic attention mechanism for geometry-aware message passing and a multi-perspective pooling strategy to construct comprehensive system representations. This design enables highly accurate modeling of atomic environments while achieving exceptional computational efficiency, making high-fidelity simulations more accessible. Tested across a wide range of datasets spanning diverse systems, including organic molecules (e.g., QM7, MD17), periodic inorganic materials (e.g., Li-containing crystals), two-dimensional materials (e.g., bilayer graphene, black phosphorus), surface catalytic reactions (e.g., formate decomposition), and charged systems, MLANet maintains competitive prediction accuracy while its computational cost is markedly lower than mainstream equivariant models, and it enables stable long-time molecular dynamics simulations. MLANet provides an efficient and practical tool for large-scale, high-accuracy atomic simulations. Artificial neural network ; Pooling ; Graph ; Bridging (networking) ; Computational model ; Interatomic potential ; Molecular dynamics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Shuyu Bi | Zhede Zhao | Qiangchao Sun | Tao Hu | Xionggang Lu | Hongwei Cheng
Source: arXiv (Cornell University) (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

Universal and efficient graph neural networks with dynamic attention for machine learning interatomic potentials The core of molecular dynamics simulation fundamentally lies in the interatomic potential. Traditional empirical potentials lack accuracy, while first-principles methods are computationally prohibitive. Machine learning interatomic potentials (MLIPs) promise near-quantum accuracy at linear cost, but existing models still face challenges in efficiency and stability. We presents Machine Learning Advances Neural Network (MLANet), an efficient and robust graph neural network framework. MLANet introduces a dual-path dynamic attention mechanism for geometry-aware message passing and a multi-perspective pooling strategy to construct comprehensive system representations. This design enables highly accurate modeling of atomic environments while achieving exceptional computational efficiency, making high-fidelity simulations more accessible. Tested across a wide range of datasets spanning diverse systems, including organic molecules (e.g., QM7, MD17), periodic inorganic materials (e.g., Li-containing crystals), two-dimensional materials (e.g., bilayer graphene, black phosphorus), surface catalytic reactions (e.g., formate decomposition), and charged systems, MLANet maintains competitive prediction accuracy while its computational cost is markedly lower than mainstream equivariant models, and it enables stable long-time molecular dynamics simulations. MLANet provides an efficient and practical tool for large-scale, high-accuracy atomic simulations. Artificial neural network ; Pooling ; Graph ; Bridging (networking) ; Computational model ; Interatomic potential ; Molecular dynamics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Sanjanasri JP | Pratiti Bhadra | N. Sukumar | Soman KP
Source: Open MIND (preprint, 2026-02-24)
Field: Materials Science — Materials Chemistry

Molecular Representations for AI in Chemistry and Materials Science: An NLP Perspective Deep learning, a subfield of machine learning, has gained importance in various application areas in recent years. Its growing popularity has led it to enter the natural sciences as well. This has created the need for molecular representations that are both machine-readable and understandable to scientists from different fields. Over the years, many chemical molecular representations have been constructed, and new ones continue to be developed as computer technology advances and knowledge of molecular complexity increases. This paper presents some of the most popular digital molecular representations inspired by natural language processing (NLP) and used in chemical informatics. In addition, the paper discusses some notable AI-based applications that use these representations. This paper aims to provide a guide to structural representations that are important for the application of AI in chemistry and materials science from the perspective of an NLP researcher. This review is a reference tool for researchers with little experience working with chemical representations who wish to work on projects at the interface of these fields. Perspective (graphical) ; Popularity ; Interface (matter) ; Natural (archaeology) ; Natural language ; Computational linguistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Cheng Zheng Tong | Qingling Liu | Hu Wang | Mingfa Yao
Source: Environmental Science Nano (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

The convergence of data science and catalysis: a comprehensive review of machine learning applications in accelerating selective catalytic reduction (SCR) catalyst design and performance prediction This review critically assesses ML approaches—from traditional algorithms to LLMs—for accelerating SCR catalyst design, highlighting data limitations and pathways toward knowledge-guided NO x abatement solutions. Transformative learning ; Catalysis ; Reduction (mathematics) ; Convergence (economics) ; Throughput ; Oxide Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Tong Yao | Yanjun Guo | Xiaojing Yang | Bohan Cheng | X. Wang | Guangyuan Du | Haoyang Luo | Wanyu Xiang | Rudan Zhang
Source: Langmuir (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Nanomechanical Properties and Constitutive Behavior of CVD-ZnS Crystals: A Comprehensive Study via Machine Learning, Numerical Simulation, and Experimental Verification Chemical vapor deposited (CVD) ZnS, widely used in infrared windows and conformal radomes for high-speed aircraft, requires accurate characterization of its plastic constitutive behavior to ensure service reliability. However, the complex deformation mechanisms of CVD-ZnS at the micro-/nanoscale make it challenging to achieve high-precision parameter identification using conventional approaches. In this study, a novel multiobjective parameter inversion method based on a metaheuristic-tuned neural network is proposed to establish a reliable framework for characterizing the micro-nanoscale mechanical behavior of CVD-ZnS. An automated Python-driven finite element simulation was developed to construct a large-scale nanoindentation data set. A high-accuracy surrogate model was then built by integrating the Crested Porcupine Optimizer (CPO) with a back-propagation neural network (BPNN), and its superiority was validated against several other metaheuristic algorithms. The surrogate model was embedded into the NSGA-II multiobjective optimization framework, and entropy-weighting analysis was employed for comprehensive evaluation, leading to the identification of the optimal Johnson-Cook (J-C) constitutive parameters of CVD-ZnS. The results demonstrated that the CPO-BPNN improved prediction accuracy by approximately 40-56% compared with the traditional BPNN. The final inverted J-C parameters (<i>A</i> = 322.8 MPa, <i>B</i> = 1014.1 MPa, <i>n</i> = 0.32) effectively captured the mechanical responses in both nanoindentation and nanoscratch experiments. Furthermore, nanoscratch experiments revealed the deformation mechanisms of CVD-ZnS and identified an equivalent residual groove depth at the brittle-to-ductile transition (DBT) point of 133.96 ± 4.64 nm, which serves as a geometric indicator of the transition region. Overall, the proposed framework provides a reliable and integrated route for precise inversion and characterization of the plastic constitutive behavior of CVD-ZnS at the micro-/nanoscale. Nanoindentation ; Constitutive equation ; Artificial neural network ; Finite element method ; Inverse problem ; Experimental data ; Characterization (materials science) ; Conformal map ; Deformation (meteorology) Microstructure and mechanical properties ; Numerical methods in engineering ; Nonlocal and gradient elasticity in micro/nano structures
Authors: Véronique Van Speybroeck | Wim Temmerman | Massimo Bocus | Louis Vanduyfhuys
Source: Annual Review of Physical Chemistry (article, 2026-02-23)
Field: Chemistry — Inorganic Chemistry

First-Principles Simulations of Chemical Transformations in Nanoporous Materials and Industrial Catalysts Nanoporous materials including zeolites, metal-organic frameworks, and covalent organic frameworks offer high tunability and surface area, making them ideally suited to address global challenges such as CO<sub>2</sub> capture and conversion, utilization of renewable feedstocks, and air purification. Molecular modeling is essential to enable atomic-scale design for optimal performance. Chemical transformations in these materials include not only catalytic reactions, but also local or global structural rearrangements and are strongly dependent on extreme operating conditions typical for industrial processes. The performance of industrial catalysts is governed by complex reaction networks and multiscale phenomena like diffusion and reactions, spanning a broad range of timescales and length scales. Recent advances at the intersection of quantum mechanics, statistical physics, and machine learning have significantly improved our ability to model complex chemical transformations in industrial catalysts and nanoporous materials. Herein, we review current modeling strategies and highlight future directions for predictive, multiscale simulations of nanoporous catalysts under realistic conditions. Nanoporous ; Catalysis ; Multiscale modeling ; Quantum chemical ; Diffusion ; Chemical industry Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Bouchra Kaima Mechraoui | R. Arar | Mohamed Halit | Zoulikha Hebboul
Source: ACS Applied Energy Materials (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Ionic Transport Predicting: A Machine Learning Dive into K <sub>3</sub> HCh (Ch: S, Se, and Te) for Next-Generation Solid State Batteries In this study, we investigate the ionic transport behavior of potassium-based antiperovskites (K3HCh, Ch: S, Se, and Te) through a comprehensive machine learning framework. The workflow included 809 Inorganic electrolyte compounds, feature generation from elemental properties, selection of the top 50 features, and training and testing of several predictive models, and the XGBoost model achieves a mean absolute error (MAE) of approximately 0.45. To validate the model, we applied it to lithium-based antiperovskites (Li3HCh, Ch = S, Se, Te) at both high and low temperatures. The predicted ionic conductivities via the XGBoost model showed minimum errors of 7.34%, 2.65%, and 1.58% for Li3HS, Li3HSe, and Li3HTe, respectively, compared to experimental values. For potassium analogues, the predicted ionic conductivities at 100 °C were 7.64 × 10 –8, 3.5 × 10–8, and 1.9 × 10–7 S·cm–1 for K3HS, K3HSe, and K3HTe, respectively. In addition, Nudged elastic band (NEB) calculations confirm that K3HTe possesses a low activation energy of 0.38 eV, highlighting its potential for efficient ion transport. Solid-state ; State (computer science) ; Ionic bonding ; Work (physics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Marcel Hiltscher | Marc Bianciotto | Francesca Grisoni
Source: ChemRxiv (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Explaining What Matters: Faithfulness in Molecular Deep Learning Deep learning ; Perspective (graphical) ; Focus (optics) ; Feature (linguistics) ; Task (project management) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Advanced Graph Neural Networks
Authors: Kwok‐Yan Lam | Regina Qianru ZHANG | He Wen | Ming Li | Xiaojin Zhang | Qiang Yang | Siu-Ming Yiu | Pietro Liò
Source: Research Square (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

From Physics Constraints to Adaptive Discovery: OG-QIMP Enables Quantum-Informed Molecular Property Prediction Weighting ; Property (philosophy) ; Limiting ; Consistency (knowledge bases) ; Quantum ; Graph ; Redundancy (engineering) ; Coupling (piping) ; Topology (electrical circuits) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Brandon K. Phan | Chiho Kim | Janhavi Nistane | Wei Xiong | H Chen | Woo Jin Jang | Farzad Gholami | Yang Su | Jerry Qi | Ryan P. Lively | Will R. Gutekunst | Rampi Ramprasad
Source: Polymers (article, 2026-03-17)
Field: Materials Science — Polymers and Plastics

AI-Assisted Design of Chemically Recyclable Polymers for Food Packaging Polymer packaging plays a crucial role in food preservation but poses major challenges in recycling and environmental persistence. To address the need for sustainable, high-performance alternatives, we employed a polymer informatics workflow to identify single- and multi-layer drop-in replacements for polymer-based packaging materials. Machine learning (ML) models, trained on carefully curated polymer datasets, predicted eight key properties across a library of approximately 7.4 million ring-opening polymerization (ROP) polymers generated by virtual forward synthesis (VFS). Candidates were prioritized by the enthalpy of polymerization, a critical metric for chemical recyclability. This screening yielded thousands of promising candidates, demonstrating the feasibility of replacing diverse packaging architectures. We then experimentally validated poly(<i>p</i>-dioxanone) (poly-PDO), an existing ROP polymer whose barrier performance had not been previously reported. Validation showed that poly-PDO exhibits strong water barrier performance, mechanical and thermal properties consistent with predictions, and excellent chemical recyclability (∼95% monomer recovery), thereby meeting the design targets and underscoring its potential for sustainable packaging. These findings highlight the power of informatics-driven approaches to accelerate the discovery of sustainable polymers by uncovering opportunities in both existing and novel chemistries. Beyond identifying potential replacements, this work establishes a generalizable framework for navigating vast polymer design spaces under competing performance constraints. The results illustrate how data-driven polymer design can bridge the gap between sustainability concepts and experimentally realizable materials for real-world packaging applications. Food packaging ; Polymer ; Workflow ; Sustainability ; Work (physics) ; Metric (unit) ; Resource (disambiguation) Polymer composites and self-healing ; Polymer crystallization and properties ; biodegradable polymer synthesis and properties