jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amy Wenxuan Ding | Shibo Li
Source: Scientific Reports (article, 2025-03-20)
Field: Materials Science — Materials Chemistry

Generative AI lacks the human creativity to achieve scientific discovery from scratch Scientists are interested in whether generative artificial intelligence (GenAI) can make scientific discoveries similar to those of humans. However, the results are mixed. Here, we examine whether, how and what scientific discovery GenAI can make in terms of the origin of hypotheses and experimental design through the interpretation of results. With the help of a computer-supported molecular genetic laboratory, GenAI assumes the role of a scientist tasked with investigating a Nobel-worthy scientific discovery in the molecular genetics field. We find that current GenAI can make only incremental discoveries but cannot achieve fundamental discoveries from scratch as humans can. Regarding the origin of the hypothesis, it is unable to generate truly original hypotheses and is incapable of having an epiphany to detect anomalies in experimental results. Therefore, current GenAI is good only at discovery tasks involving either a known representation of the domain knowledge or access to the human scientists' knowledge space. Furthermore, it has the illusion of making a completely successful discovery with overconfidence. We discuss approaches to address the limitations of current GenAI and its ethical concerns and biases in scientific discovery. This research provides insight into the role of GenAI in scientific discovery and general scientific innovation. Scientific discovery ; Computer science ; Data science ; Generative grammar ; Interpretation (philosophy) ; Cognitive science ; Field (mathematics) ; Artificial intelligence ; Epistemology ; Psychology Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Bioinformatics and Genomic Networks
Authors: Jiahui Guo | Sai Chu | Fangli Yuan | Ken‐ichi Otake | Ming‐Shui Yao | Susumu Kitagawa
Source: Industrial Chemistry and Materials (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Soft porous crystals: flexible MOFs as a new class of adaptive materials Clarifying the dose sensitivity of soft porous crystals (SPCs), particularly soft/flexible metal–organic framework (MOFs), is vital for optimizing precision-driven and large-scale applications across multiple advanced technologies. Porosity ; Class (philosophy) ; Porous medium ; Materials science ; Nanotechnology ; Soft materials ; Composite material ; Computer science ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Yunfeng Guan | Sheng Zhou | Lidan Tan | Rong Zhao | Qin Zhang | Hui Zhu | Xuanke Li | Zhijun Dong | Haiyan Duan | Dunzhu Li | Valeria Nicolosi | Ye Cong | Ke Li
Source: Energy storage materials (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Double transition metal Ti2NbC2T MXene with modulated electronic structure for advanced lithium-ion capacitors Double transition metal (DTM) MXenes are garnering increasing attention owe to their wide diversity, controllable properties, tunable electronic structure and surface chemistry. Nevertheless, research on the exciting DTM MXenes is still in its infancy. Herein, we present a novel out-of-plan ordered DTM MXene, Ti 2 NbC 2 T x , achieved by introducing Nb species into the M (Ti) site of Ti 3 C 2 T x MXene. In which, Ti and Nb atoms occupy the outer and middle transition metal layers, respectively. This structure endows the as-synthesized Ti 2 NbC 2 T x MXene with significantly higher chemical affinity and absorbability for lithium ions than Ti 3 C 2 T x , showing a high reversible capacity of up to 272 mAh g -1 at 0.1 A g -1 and exceptional long-term stability (no capacity loss after 1000 cycles). Moreover, the lithium-ion capacitors (LICs) assembled with Ti 2 NbC 2 T x MXene anode and activated carbon (AC) cathode exhibit high energy and power densities of 39 Wh Kg -1 and 4600 W kg -1 , respectively, surpassing the most state-of-the-art MXene-based LICs. This work demonstrates the significant potential of DTM MXenes in advancing energy storage applications. An out-of-plane ordered double transition metal Ti 2 NbC 2 T x MXene with modulated electronic structure was successfully synthesized by introducing Nb into Ti 3 C 2 T x MXene via an alloy engineering strategy. This structure endows the synthesized Ti 2 NbC 2 T x MXene with significantly higher lithium-ion chemical affinity/absorbability than Ti 3 C 2 T x , thus showcasing outstanding lithium-ion storage performance. Materials science ; Lithium (medication) ; Capacitor ; Transition metal ; Ion ; Electronic structure ; MXenes ; Nanotechnology ; Engineering physics ; Condensed matter physics ; Electrical engineering ; Voltage MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices ; 2D Materials and Applications
Authors: Mengyuan Wang | Yi-Min Tu | Qinghua Zeng | Kun Li | Wei Xiong | Zhongzheng Cai | Jian‐Bo Zhu
Source: Nature Chemistry (article, 2025-05-16)
Field: Materials Science — Biomaterials

High-performance recyclable polymers enabled by stereo- and sequence-controlled polymerization Chemistry ; Polymerization ; Polymer ; Sequence (biology) ; ROMP ; Polymer chemistry ; Polymer science ; Nanotechnology ; Combinatorial chemistry ; Chemical engineering ; Organic chemistry ; Metathesis ; Biochemistry biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Additive Manufacturing and 3D Printing Technologies
Authors: Viggo Moro | Charlotte Loh | Rumen Dangovski | Ali Ghorashi | Andrew Ma | Zhuo Chen | Samuel Kim | Peter Y. Lu | Thomas Christensen | Marin Soljačić
Source: Newton (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

Multimodal foundation models for material property prediction and discovery Publisher of over 50 scientific journals across the life, physical, earth, and health sciences, both independently and in partnership with scientific societies including Cell, Neuron, Immunity, Current Biology, AJHG, and the Trends Journals. Foundation (evidence) ; Property (philosophy) ; Computer science ; History ; Philosophy ; Epistemology ; Archaeology Machine Learning in Materials Science ; Drilling and Well Engineering ; Mineral Processing and Grinding
Authors: Jyothi Budida | Ch. Srinivasa Rao | Navin Chand | Ravi Kumar Guntu | N. Krishna Mohan
Source: Journal of Alloys and Compounds (article, 2025-07-22)
Field: Materials Science — Ceramics and Composites

Multifunctional Pr2O3 doped lithium fluoro lanthanum oxy lead borate glasses for advanced photonic and radiation shielding applications Lithium borate ; Boron ; Electromagnetic shielding ; Lithium (medication) ; Doping ; Lanthanum ; Materials science ; Radiation shielding ; Lead (geology) ; Radiation ; Borate glass ; Inorganic chemistry ; Optoelectronics ; Radiochemistry ; Chemistry ; Optics ; Composite material ; Physics Glass properties and applications ; Luminescence Properties of Advanced Materials ; Nuclear materials and radiation effects
Authors: Fang Lei | S. Qiu | Hui Xu | Ye Tao | Liangchun Li
Source: Advanced Functional Materials (article, 2025-04-16)
Field: Materials Science — Materials Chemistry

Encapsulation of Single‐Atoms into Covalent Organic Frameworks for Highly Efficient and Persistent H <sub>2</sub> O <sub>2</sub> Photosynthesis via Biphasic Flow Chemistry Abstract Covalent organic frameworks (COFs) metallization offers a potent protocol for augmenting the efficacy of artificial photosynthesis of H 2 O 2 . However, hurdles like the intractable short exciton lifetime and the precarious metal coordination of metallo‐COFs impede their photocatalytic proficiency. Herein, a facile and universal single‐atom encapsulation strategy is proposed, through which Fe atoms are precisely anchored in the pincer structure to achieve the planar locking of the PP‐COF and enhance its π‐conjugation effect. The Fe/PP‐COF with extremely high single‐atom stability exhibits remarkable exciton migration distance (6.48–53.20 nm) and exciton lifetime (296.75 ps), representing the state‐of‐the‐art single‐atom COF hitherto. In a brand‐new biphasic flow reactor, within 60 h the reaction allows continuous photosynthesis of H 2 O 2 at a rate of 1556 µmol h −1 and an unprecedented concentration of 100 m m , with no single‐atom leakage. This work offers valuable insights into developing single‐atom COFs and precise regulation of single‐atoms to achieve high space‐time continuous H 2 O 2 production. Covalent bond ; Materials science ; Photosynthesis ; Nanotechnology ; Chemical physics ; Chemistry ; Organic chemistry Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Geemi P. Wellawatte | Philippe Schwaller
Source: Communications Chemistry (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in chemistry to understand structure-property relationships. However, one of the main limitations of XAI methods is that they are developed for technically oriented users. We propose the XpertAI framework that integrates XAI methods with large language models (LLMs) accessing scientific literature to generate accessible natural language explanations of raw chemical data automatically. We conducted 5 case studies to evaluate the performance of XpertAI. Our results show that XpertAI combines the strengths of LLMs and XAI tools in generating specific, scientific, and interpretable explanations. Property (philosophy) ; Computer science ; Artificial intelligence ; Natural language processing ; Field (mathematics) ; Raw data ; Natural (archaeology) ; Data science ; Machine learning ; Epistemology ; Mathematics ; Geography ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Teng Long | Qianqian Pang | Yanyan Deng | Xuguang Pang | Yixuan Zhang | Rui Yang | Chuanjian Zhou
Source: Polymers (review, 2025-06-16)
Field: Materials Science — Materials Chemistry

Recent Progress of Artificial Intelligence Application in Polymer Materials Artificial intelligence (AI) technology has made remarkable progress in polymer materials, which has changed polymer science significantly. However, this community still relies heavily on the traditional research paradigm instead of the data-driven paradigm. This review advocates for a fundamental paradigm shift in polymer research from traditional experience-driven methods to data-driven approaches enabled by AI. While AI has made transformative advances in polymer design, property prediction, and process optimization, the field remains anchored in conventional methodologies. AI's computational advantages against persistent barriers are also evaluated, such as data scarcity, inadequate material descriptors, and algorithmic complexity. Potential solutions, including collaborative data platforms, domain-adapted descriptor frameworks, and active learning strategies, are also discussed. Furthermore, we demonstrate how high-quality data and explainable AI methodologies overcome computational limitations while ensuring result credibility in other areas, which can benefit polymer research. Ultimately, this work provides a roadmap for accelerating the sustainable convergence of data-driven AI innovation with polymer science. Materials science ; Polymer ; Polymer science ; Composite material Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Changxi Yang | Chenyu Wu | Wenbo Xie | Daiqian Xie | P. Hu
Source: Nature Catalysis (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

General reactive element-based machine learning potentials for heterogeneous catalysis Generality ; Density functional theory ; Range (aeronautics) ; Heterogeneous catalysis ; Catalysis ; Sampling (signal processing) ; Process (computing) ; Complex system Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Chao Wang | Bing Wang | Changhao Wang | A. Li | Zhipeng Chang | Ru‐Zhi Wang
Source: npj Computational Materials (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

A machine learning model with minimize feature parameters for multi-type hydrogen evolution catalyst prediction The vast chemical compositional space presents challenges in catalyst development using traditional methods. Machine learning (ML) offers new opportunities, but current ML models are typically limited to screening a single catalyst type. In this work, we developed an efficient ML model to predict hydrogen evolution reaction (HER) activity across diverse catalysts. By minimizing features, we introduced a key energy-related feature φ = $${{\rm{Nd}}0}^{2}/{\rm{\psi }}0$$ , which correlates with HER free energy. Using just ten features, the Extremely Randomized Trees model achieved R² = 0.922. We predicted 132 new catalysts from the Material Project database, among which several exhibited promising HER performance. The time consumed by the ML model for predictions is one 200,000th of that required by traditional density functional theory (DFT) methods. The model provides an efficient approach for discovering high-performance HER catalysts using a small number of key features and offers insights for the development of other catalysts. Feature (linguistics) ; Catalysis ; Artificial intelligence ; Computer science ; Type (biology) ; Machine learning ; Materials science ; Chemistry ; Geology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Luozhijie Jin | Zijian Du | Le Shu | Yan Cen | Yuanfeng Xu | Yongfeng Mei | Hao Zhang
Source: Nature Communications (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Transformer-generated atomic embeddings to enhance prediction accuracy of crystal properties with machine learning Accelerating the discovery of novel crystal materials by machine learning is crucial for advancing various technologies from clean energy to information processing. The machine-learning models for prediction of materials properties require embedding atomic information, while traditional methods have limited effectiveness in enhancing prediction accuracy. Here, we proposed an atomic embedding strategy called universal atomic embeddings (UAEs) for their broad applicability as atomic fingerprints, and generated the UAE tensors based on the proposed CrystalTransformer model. By performing experiments on widely-used materials database, our CrystalTransformer-based UAEs (ct-UAEs) are shown to accurately capture complex atomic features, leading to a 14% improvement in prediction accuracy on CGCNN and 18% on ALIGNN when using formation energies as the target, based on the Materials Project database. We also demonstrated the good transferability of ct-UAEs across various databases. Based on the clustering analysis for multi-task ct-UAEs, the elements in the periodic table can be categorized with reasonable connections between atomic features and targeted crystal properties. After applying ct-UAEs to predict formation energy in hybrid perovskites database, we realized an improvement in accuracy, with a 34% boost in MEGNET and 16% in CGCNN, showcasing their potential as atomic fingerprints to address the data scarcity challenges. Transformer ; Computer science ; Machine learning ; Artificial intelligence ; Materials science ; Electrical engineering ; Engineering ; Voltage Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Yue Liu | Shuchang Ma | Zhengwei Yang | Duo Wu | Yali Zhao | Maxim Avdeev | Siqi Shi
Source: Journal of Materiomics (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Domain knowledge-assisted materials data anomaly detection towards constructing high-performance machine learning models Machine learning (ML) is widely applied to accelerate materials design and discovery due to its outperforming capability of data analysis and information extraction. However, experimental and computational errors typically lead to emerging data anomalies, harming the performance of ML models. Most currently used anomaly detection methods are purely data-driven, which has limited capability of learning complicated factors in materials data. Here, we propose a domain knowledge-assisted data anomaly detection (DKA-DAD) workflow, where materials domain knowledge is encoded as symbolic rules. Three detection models are designed for evaluating the correctness of individual descriptor value, correlation between descriptors, and similarity between samples, respectively, and one modification model is constructed for comprehensive governance. We construct 180 synthetic datasets by injecting noise into 60 structured materials datasets collected from materials ML studies, to validate its potential utility and applications. DKA-DAD achieves a 12% F1-score improvement in anomaly detection accuracy on synthetic datasets compared to purely data-driven approach and the ML models trained on materials datasets processed through DKA exhibit an average 9.6% improvement in R 2 for the property prediction. Our work provides a data anomaly detecting approach under the guidance of materials domain knowledge towards accelerating materials design and discovery based on ML. • A domain knowledge-assisted data anomaly detection (DKA-DAD) workflow is first proposed. • Domain knowledge is symbolized and embedded into the 3 designed detection models for evaluation from different dimensions. • DKA-DAD governs 60 collected materials datasets with an average 9 % insight improvement, outperforming existing methods. Anomaly detection ; Domain (mathematical analysis) ; Anomaly (physics) ; Materials science ; Data science ; Artificial intelligence ; Computer science ; Machine learning ; Physics Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Computational Drug Discovery Methods
Authors: C. D. Chavare | D.S. Sawant | Satish V. Gaikwad | Akash V. Fulari | H. R. Kulkarni | Deepak P. Dubal | G. M. Lohar
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nickel cobalt phosphate/phosphide as a promising electrode material for extrinsic supercapacitors: machine learning analysis This review outlines the recent advancements in nickel cobalt phosphate- and phosphide-based supercapacitors by providing their structure–property–performance relationships and employing advanced machine learning as a predictive tool. Phosphide ; Supercapacitor ; Cobalt ; Materials science ; Nickel ; Electrode ; Metallurgy ; Chemistry ; Electrochemistry Supercapacitor Materials and Fabrication
Authors: Satoru Takeshita | Shinya Mine | Takumi Ono
Source: Angewandte Chemie International Edition (review, 2025-04-01)
Field: Chemistry — Spectroscopy

Data‐Driven Review on Aerogels and Polymeric Aerogels A thoroughly data-driven review on the chemistry, properties, and applications of aerogels is provided. Aerogel is a category of solid materials having homogeneous, highly porous nanostructures. Unique physicochemical properties of aerogels have attracted much attention in wide fields due to their potentials for energy/environmental issues in the 21st century, while an exponentially increasing number of papers are causing an academic fever and unreliable data issues. Statistical analysis based on an exhaustive literature survey from almost all the papers with "aerogel" in their titles reveals a flood of freeze-dried aerogels mainly consisting of organic polymers and organic-inorganic composites. Process-property data from polymer aerogel papers are analyzed by a machine-learning approach aiming to establish data infrastructure toward future aerogel studies. Data-evidenced critical discussion specifies a tendency in unreliable thermal conductivity data of these papers. Materials science ; Aerogel ; Composite material ; Chemical engineering ; Nanotechnology ; Engineering Aerogels and thermal insulation ; Surface Modification and Superhydrophobicity
Authors: Changwei Zhang | Yang Zhong | Zhi-Guo Tao | Xinming Qin | Honghui Shang | Zhenggang Lan | Oleg V. Prezhdo | Xin-Gao Gong | Weibin Chu | Hongjun Xiang
Source: Nature Communications (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N<sup>2</sup>AMD (Neural-Network Non-Adiabatic Molecular Dynamics), which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. Distinct from conventional machine learning methods that predict key quantities in NAMD, N<sup>2</sup>AMD computes these quantities directly with a deep neural Hamiltonian, ensuring excellent accuracy, efficiency, and consistency. N<sup>2</sup>AMD not only achieves impressive efficiency in performing NAMD simulations at the hybrid functional level within the framework of the classical path approximation (CPA), but also demonstrates great potential in predicting non-adiabatic coupling vectors and suggests a method to go beyond CPA. Furthermore, N<sup>2</sup>AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework offers a reliable and efficient approach for conducting accurate NAMD simulations across various condensed materials. Hamiltonian (control theory) ; Adiabatic process ; Computer science ; Molecular dynamics ; Artificial neural network ; Statistical physics ; Physics ; Artificial intelligence ; Quantum mechanics ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Chemical Physics Studies