jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bin Liu | Jirui Jin | Mingjie Liu
Source: ChemRxiv (preprint, 2025-07-02)
Field: Chemistry — Organic Chemistry

Extrapolating Beyond C60: Advancing Prediction of Fullerene Isomers with FullereneNet Fullerenes, carbon-based nanomaterials with sp2-hybridized carbon atoms arranged in polyhedral cages, exhibit diverse isomeric structures with promising applications in optoelectronics, solar cells, and medicine. However, the vast number of possible fullerene isomers complicates efficient property prediction. In this study, we introduce FullereneNet, a graph neural network based model that predicts fundamental properties of fullerenes using topological features derived solely from unoptimized structures, eliminating the need for computationally expensive quantum chemistry optimizations. The model leverages topological representations based on the chemical environments of pentagon and hexagon rings, enabling efficient capture of local structural details. We show that this approach yields superior performance in predicting the C–C binding energy for a wide range of fullerene sizes, achieving mean absolute errors of 3 meV/atom for C60, 4 meV/atom for C70, and 6 meV/atom for C72–C100, surpassing state-of-the-art machine learning interatomic potentials GAP-20. Additionally, the FullereneNet model accurately predicts 11 other properties, including HOMO-LUMO gap and solvation free energy, demonstrating robustness and transferability across fullerene types. This work provides a computationally efficient framework for high-throughput screening of fullerene candidates and establishes a foundation for future data-driven studies in fullerene chemistry. Fullerene ; Chemistry ; Organic chemistry Fullerene Chemistry and Applications ; Machine Learning in Materials Science
Authors: Shailendra Yadav | H. Chandra | Kanha Singh Tiwari | M.K. Bharty | Rishikesh Chandravanshi | Dheeraj Singh Chauhan | M.A. Quraishi
Source: Discover Chemical Engineering (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

A review on the role of artificial intelligence and data science in green and sustainable chemistry: current trends and futuristic pathways The combination of artificial intelligence, data science and green chemistry is leading scientific activity in addressing the planetary scale environmental challenges. This collective approach is to minimise waste and maximise energy efficiency, in addition to safer chemical design. Green chemistry is pivotal for developing more sustainable processes and production systems that minimize harm to human health and the environment. The integration of artificial intelligence and data science is transforming green chemistry. These tools can now analyze very large and complex datasets, predict chemical behaviour and help optimize processes to make them more sustainable. This review highlights recent advances in artificial intelligence for reaction optimization, new material discovery, process automation, and real-time environmental monitoring. It shows how machine learning, cheminformatics and data analytics work together to accelerate innovation in green and sustainable chemistry. Although this transformation is promising, major challenges still exist, such as the lack of high-quality data, the difficulty in interpreting AI models, and the limited collaboration across disciplines. To overcome these barriers, the development of data standards, open platforms, and interconnected research ecosystems is needed. By unlocking the potential of artificial intelligence and data science for green chemistry, this work highlights a strategic route to sustainable development goals achievement and to a cleaner more sustainable future. Big data ; Process (computing) ; Work (physics) ; SAFER ; Sustainable development ; Backcasting ; Scale (ratio) Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: James J. De Yoreo | Sakshi Yadav | Benjamin A. Helfrecht | Amy E. Stegmann | Benjamin A. Legg | Harley Pyles | Jiajun Chen | John R. Edison | Maxim Ziatdinov | Zdeněk Preisler | Orion Dollar | Stephen Whitelam | Sergei V. Kalinin | David Baker | Christopher J. Mundy | Shuai Zhang
Source: Research Square (preprint, 2025-04-08)
Field: Materials Science — Surfaces, Coatings and Films

Impact of Colloidal Forces and Broken Symmetry on the Design of a Protein-Material Interface Interface (matter) ; Symmetry (geometry) ; Colloid ; Materials science ; Nanotechnology ; Engineering ; Composite material ; Chemical engineering ; Mathematics ; Geometry Polymer Surface Interaction Studies ; Silk-based biomaterials and applications ; 3D Printing in Biomedical Research
Authors: Huan Chen | Yating Gao | Xiaorong Ge | Xin Wang | Feng Cheng | Ya‐Ping Xue | Yu‐Guo Zheng
Source: Biotechnology Advances (article, 2025-10-17)
Field: Materials Science — Biomaterials

Material-binding peptides: sources, mechanisms, directed evolution and applications Directed evolution ; Protein engineering ; Synthetic biology ; Bacterial protein ; Emerging technologies ; Aqueous medium Diatoms and Algae Research ; Advanced biosensing and bioanalysis techniques ; Supramolecular Self-Assembly in Materials
Authors: Jiayu Zhu | Yun Zheng | Wenlai Zhao | Wei Yan | Jiujun Zhang
Source: Energy and AI (article, 2025-08-09)
Field: Materials Science — Materials Chemistry

Hybrid deep learning architecture for temperature gradient control of a solid oxide electrolysis cell under fluctuating wind power • A novel hybrid deep learning architecture (HDLA) framework • Combination of data-driven machine learning with multi-physics simulations • Reducing SOEC temperature gradients from ±20 to ±5°C under dynamic wind power • 99 % energy utilization efficiency and stable operation even at fluctuating renewable energy input • HDLA enhances grid-SOEC synergy, enabling carbon-neutral chemical production The co-electrolysis of CO₂ and H₂O through solid oxide electrolysis cells (SOECs), powered by renewable energy sources, offers a promising pathway to achieving carbon neutrality in the chemical industry. However, the inherent intermittency of renewable energy generation, such as wind power, leads to unstable power input for electrolysis. This variability induces significant thermal stress in SOECs, potentially causing cracks or even system failure. To address this challenge, a hybrid deep learning architecture (HDLA) was developed to control the temperature gradient of SOECs. The architecture combines a convolutional neural network (CNN) and a long short-term memory (LSTM) model for wind power prediction, a multi-physics model for temperature gradient simulation, and a linear neural network regression model to simulate the temperature distribution in SOECs. Training and verification are conducted using 16 datasets from an industrial wind farm. The results demonstrate that the application of HDLA successfully reduce the temperature gradient of SOECs from ±20°C to ±5°C. Additionally, the potential wind power utilization achieved near-complete wind power utilization, increasing from 18 % to 99 %. This real-time control strategy, which optimizes flow regulation, effectively mitigates thermal stress, thereby extending the lifespan of SOECs and ensuring continuous carbon reduction, efficient conversion, and utilization. Architecture ; Electrolysis ; Control (management) ; Power (physics) ; Wind power ; Materials science ; Computer science ; Electrical engineering ; Chemical engineering ; Engineering ; Physics ; Artificial intelligence ; Geography ; Thermodynamics ; Electrode Advancements in Solid Oxide Fuel Cells ; Machine Learning and ELM ; Fuel Cells and Related Materials
Authors: C. Ma | S. Joseph Poon
Source: ArXiv.org (preprint, 2025-08-30)
Field: Materials Science — Materials Chemistry

Reexamining Machine Learning Models on Predicting Thermoelectric Properties Thermoelectric materials can generate clean energy by transforming waste heat into electricity. The effectiveness of thermoelectric materials is measured by the dimensionless figure of merit, ZT. The quest for high ZT materials has drawn extensive research experimentally and theoretically. However, due to the vast material space, finding high ZT materials is time-consuming and costly. To improve the efficiency of discovering new thermoelectric materials, recent studies have employed machine learning with databases to search for high ZT candidates. In this work, we examine the effects of adding various physical concepts on the performance of machine learning models in predicting TE properties. The objective is to improve the model ability to capture the underlying physics in designing TE materials. These concepts include short range order and crystal structure class. Results show some improvements in accuracy. However, the current models do not distinguish between dilute alloys and concentrated alloys, rendering them inadequate in predicting doping effects. To better capture the electronic band structure effect from doping, we included various dopant properties as features. This increases the prediction accuracy in doped materials. Furthermore, we used a genetic algorithm to rank features for various thermoelectric properties to provide physical insight into key parameters in designing thermoelectric materials. Thermoelectric effect ; Thermoelectric materials ; Dopant ; Predictive modelling ; Material properties ; Thermoelectric cooling Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Mohammed Al‐Fahdi | Riccardo Rurali | Jianjun Hu | Christopher Wolverton | Ming Hu
Source: npj Computational Materials (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Accelerated discovery of extreme lattice thermal conductivity by crystal graph attention networks and chemical bonding Designing materials with targeted lattice thermal conductivity (LTC) demands electronic-level insight into chemical bonding. We introduce two bonding descriptors, namely normalized negative integrated COHP (-ICOHP) and normalized integrated COBI, that correlate strongly with LTC and rattling (mean-squared displacement), surpassing empirical rules and the unnormalized −ICOHP across >4500 inorganic crystals by first-principles. We train a crystal attention graph neural network (CATGNN) to predict these descriptors and screen ~200,000 database structures for extreme LTCs. From 367 (533) candidates with low (high) normalized -ICOHP and normalized ICOBI, first-principles validation identifies 106 dynamically stable compounds with LTC < 5 W m−1 K−1 (68% <2 W m−1 K−1) and 13 stable compounds with LTC > 100 W m−1 K−1. The descriptors’ low cost and clear physical meaning provide a rapid, reliable route to high-throughput discovery and inverse design of crystalline materials with ultralow or ultrahigh LTC for applications in thermal insulation, thermoelectrics, and electronics cooling. Thermal conductivity ; Crystal (programming language) ; Lattice (music) ; Inverse ; Electronics ; Graph ; Chemical bond ; Artificial neural network Thermal Expansion and Ionic Conductivity ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Jiahui Pei | Xiaoyu Mu | Ruoli Zhao | Yonghui Li | Yili Wang | Ke Chen | Ling Liu | Jiahao Wei | Guo Li | Jiarong Li | Danqi Ding | Hao Wang | Huanhuan Qiao | Jiang Yang | Changlong Liu | Xiaodong Zhang
Source: Nature Sustainability (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Scalable synthesis of sustainable single-atom catalysts for vascular healing Bimetallic strip ; Catalysis ; Nickel titanium ; Nitride ; Substrate (aquarium) ; Homogeneous Advanced Nanomaterials in Catalysis ; Electrocatalysts for Energy Conversion ; Infectious Aortic and Vascular Conditions
Authors: Rick Oerder | Gerrit Schmieden | Jan Hamaekers
Source: ChemRxiv (preprint, 2025-05-18)
Field: Materials Science — Materials Chemistry

Multi-Fidelity Learning for Atomistic Models via Trainable Data Embeddings We present an approach for end-to-end training of machine learning models for structure-property modeling on collections of datasets derived using different DFT functionals and basis sets. This approach overcomes the problem of data inconsistencies in the training of machine learning models on atomistic data. We rephrase the underlying problem as a multi-task learning scenario. We show that conditioning neural network-based models on trainable embedding vectors can effectively account for quantitative differences between methods. This allows for joint training on multiple datasets that would otherwise be incompatible. Therefore, this procedure circumvents the need for re-computations at a unified level of theory. Numerical experiments demonstrate that training on multiple reference methods enables transfer learning between tasks, resulting in even lower errors compared to training on separate tasks alone. Furthermore, we show that this approach can be used for multi-fidelity learning, improving data efficiency for the highest fidelity by an order of magnitude. To test scalability, we train a single model on a joint dataset compiled from 10 disjoint subsets of the MultiXC-QM9 dataset generated by different reference methods. Again, we observe transfer learning effects that improve the model errors by a factor of 2 compared to training on each subset alone. We extend our investigation to machine learning force fields for material simulations. To this end, we incorporate trainable embedding vectors into the readout layer of a deep graph neural network (M3GNet) that is simultaneously trained on PBE and r2SCAN labels of the MatPES dataset. We observe that joint training on both fidelity levels reduces the amount of r2SCAN data required to achieve the accuracy of a single-fidelity model by a factor of 10. Fidelity ; Computer science ; Artificial intelligence ; High fidelity ; Machine learning ; Engineering Machine Learning in Materials Science ; Machine Learning and Data Classification ; Gaussian Processes and Bayesian Inference
Authors: Wei‐Hsin Chen | K. Aishwarya | Kripasindhu Sardar
Source: Energy Materials (review, 2025-06-13)
Field: Materials Science — Materials Chemistry

Statistical and artificial intelligence approaches towards the optimization of thermoelectric materials synthesis: a review Thermoelectric (TE) materials, capable of directly converting heat to electricity, offer a promising sustainable energy and waste heat recovery solution. Despite extensive research, a significant bottleneck remains: the synthesis of high-performance TE materials still relies heavily on trial-and-error approaches, which are time-consuming and resource-intensive. Moreover, while machine learning (ML) and design of experiments (DOE) have shown potential in optimizing synthesis processes across materials science, their systematic application to TE materials remains underexplored. In particular, very few reviews have addressed the integration of statistical and AI-guided methods for synthesizing and optimizing TE materials. This manuscript comprehensively reviews recent advances in statistical and artificial intelligence techniques for optimizing TE material synthesis. It first discusses the role of DOE in identifying critical synthesis parameters and explores various ML methods for predicting TE performance. This study then highlights case studies involving different TE material systems, synthesis strategies (e.g., ball milling, sputtering, electrodeposition), and ML-based performance prediction and optimization. This work fills a critical gap by linking data-driven optimization techniques with experimental synthesis in the TE field. It not only consolidates current knowledge but also sets the stage for future studies aiming to bridge material discovery and practical manufacturing. The insights presented are instrumental in accelerating the development of next-generation TE devices. Thermoelectric effect ; Materials science ; Management science ; Artificial intelligence ; Computer science ; Engineering ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Yidu Liu | Bing Yan
Source: Angewandte Chemie International Edition (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Exciton Regulation and Fluorescence Switching via Redox‐Responsive Heterojunction Interfaces Redox-responsive fluorescence regulation at heterointerfaces remains a critically underdeveloped yet strategically significant domain in advanced chemical sensing. Herein, we present an exciton modulation strategy enabled by heterojunction engineering between electron-rich CdSe quantum dots and an electron-deficient covalent triazine framework (CTF). This type-I CdSe@CTF heterostructure achieves nanoscale electronic decoupling and directional charge redistribution, unveiling a previously unreported fluorescence-switching mechanism governed by redox-triggered interfacial reconfiguration. Beyond excitonic regulation, the heterostructure intrinsically mimics oxidase-like catalysis, enabling dual fluorescence-colorimetric readouts within a unified excitonic framework. When embedded into flexible electrospun membranes and combined with artificial neural network (ANN)-based image recognition, the system offers adaptive and intelligent detection under bio-relevant conditions. This study establishes a molecularly encoded design paradigm that bridges exciton dynamics, redox chemistry, and wearable photonic sensing, offering a blueprint for the development of multifunctional bioelectronic platforms. Heterojunction ; Exciton ; Nanotechnology ; Redox ; Materials science ; Quantum dot ; Photonics ; Fluorescence ; Chemistry ; Optoelectronics ; Physics Luminescence and Fluorescent Materials ; Conducting polymers and applications ; Perovskite Materials and Applications
Authors: Jiale Zhang | An-Dong Fan | Ziqi Wang | Lei Zheng | Yu Liu | Zhijian Wang | Peng Kang
Source: The Journal of Physical Chemistry B (article, 2025-08-15)
Field: Materials Science — Polymers and Plastics

Data-Driven Design of High-Temperature-Resistant Polyimides Using Hierarchical Gaussian Process Regression Accurate prediction of Tg for polyimides (PIs) is essential for assessing material performance in high-temperature applications in aerospace, electronics, microelectronics, and flexible display technology. However, experimental measurements remain critically challenging due to the labor-intensive synthesis, conventional instrument limits, and time-consuming characterization processes. Meanwhile, force field limitations, timescale discrepancy, and validation difficulties exist in the prediction of Tg for PIs using molecular dynamic simulation. In this study, we introduce a hierarchical Gaussian process regression machine learning method that integrates prior knowledge to predict Tg for PIs with small-sample data sets. We employ RDKit for molecular descriptor calculation and feature selection. Twenty-one key descriptors are identified, and exceptional model performance with a coefficient of determination <i>R</i><sup>2</sup> of 0.98/0.74 on the training/test set is achieved, surpassing conventional machine learning approaches. We further use Shapley additive explanations analysis to study the actionable insights for designing thermally stable PIs. The number of rotatable bonds and minimum partial charge act as dominant factors influencing Tg. Validations through experimental synthesis and molecular dynamics simulations confirm that the prediction errors are below 15%, while a Bayesian update strategy employing a radial basis function kernel corrected systematic underestimation in the high-Tg regime (>270 °C). This work provides a robust, validated Tg prediction tool, elucidates critical structure-property relationships, and establishes a transferable framework for data-driven materials design, advancing the development of high-performance polymers. Kriging ; Gaussian process ; Computer science ; Artificial intelligence ; Machine learning ; Microelectronics ; Regression ; Process (computing) ; Field (mathematics) ; Gaussian ; Materials science ; Nanotechnology ; Mathematics ; Statistics Synthesis and properties of polymers ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Li Duan | Yinghao Zhou | Zilin Yan | Zehua Pan | Zheng Zhong
Source: Applied Energy (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Explainable machine learning unveils a critical trade-off in SOFCs: The role of cathode-to-anode reaction site ratio Cathode ; Anode ; Power density ; Reliability (semiconductor) ; Power (physics) ; Flow (mathematics) ; Work (physics) ; Nonlinear system ; Boundary (topology) ; Electrode Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Alex Kutana | Koki Yoshimochi | Ryoji Asahi
Source: Science and Technology of Advanced Materials Methods (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

Dielectric tensor of perovskite oxides at finite temperature using equivariant graph neural network potentials Atomistic simulations of properties of materials at finite temperatures are computationally demanding and require models that are more efficient than the ab initio approaches. Machine learning (ML) and artificial intelligence (AI) address this issue by enabling accurate models with close to ab initio accuracy. Here, we demonstrate the utility of ML models in capturing properties of realistic materials by performing finite temperature molecular dynamics simulations of perovskite oxides using a force field based on equivariant graph neural networks. The models demonstrate efficient learning from a small training dataset of energies, forces, stresses, and tensors of Born effective charges. We qualitatively capture the temperature dependence of the dielectric tensor and structural phase transitions in calcium titanate. Dielectric ; Materials science ; Equivariant map ; Perovskite (structure) ; Artificial neural network ; Condensed matter physics ; Graph ; Tensor (intrinsic definition) ; Composite material ; Mathematics ; Pure mathematics ; Computer science ; Physics ; Discrete mathematics ; Chemistry ; Optoelectronics ; Artificial intelligence ; Crystallography Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials