jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bhawana Kesarwani
Source: JOURNAL OF ADVANCE AND FUTURE RESEARCH (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Driven Chemistry: A Review Artificial intelligence (AI) is increasingly changing the research paradigms of science and technology. Several examples show the enormous potential of AI to revolutionize the research in the scientific field. The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. The application of AI in chemistry is also related to drug development, molecules-chemical bond, building blocks. AI can help with everything from molecule synthesis to molecular property identification. AI also automatedly designed an industrial-scale central processing unit (CPU) within 5 h. Chemistry, as a basic science that studies the composition, structure, properties of substances and the transformations they undergo, has long suffered with huge parameter space, large span of dimensions and low experimental efficiency due to traditional trial and error methods. In recent years, with the deep intersection of AI and chemistry, these problems are gradually being solved. High-precision, high-efficiency, multi-scale chemical prediction models have been developed. Large model-driven, human-machine collaborative intelligent chemical laboratories have been constructed. Google Maps is among the most useful examples of AI. The AI has developed into a helpful tool in many sectors ranged from developing games and speech recognition to autonomous vehicle operation and spaceships since the early 2000s. Applications of artificial intelligence ; Intersection (aeronautics) ; Deep learning ; Artificial life ; Property (philosophy) ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Big Data and Digital Economy
Authors: Darshana Anand Upar | Sarifa Regina Fernandes | Narendra Nath Ghosh
Source: ACS Omega (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

MBenes─An Emerging 2D Layered Material: Synthesis, DFT Studies, and Application as Electrocatalyst for Hydrogen Evolution Reaction: A Review MBenes are a relatively new addition to the family of 2-D layered materials and are still in its infancy. Their unique layered structure and remarkable propertiesincluding high electrical conductivity, magnetic properties, mechanical strength, surface functional groups, and abundant active siteshave established them as a rising star in the field of 2-D materials, and their potential use in a plethora of applications has been anticipated. Initially, in 2017, theoretical studies predicted the possible structures of MBenes (<i>M</i> <sub>n</sub>B<sub>2n‑2</sub>), and then experimental synthesis of certain MBenes confirmed their existence, paving the way for further research and development. In this review paper, we have discussed the fundamental aspects of MBenes, including their theoretically predicted crystal structures, stability, chemical compositions, and their synthesis from precursor MAB phases. The challenges currently hindering the progress of MBenes have also been mentioned. In this review, special emphasis is placed on the performance of various MBenes as electrocatalysts for the hydrogen evolution reaction (HER), along with the insights provided by theoretical studies. Electrocatalyst ; Hydrogen ; Density functional theory ; Transition metal ; Fuel cells MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; 2D Materials and Applications
Authors: Nicolas Wong | Julia H. Yang
Source: arXiv (Cornell University) (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning Universal machine learned interatomic potentials (uMLIPs) embody a growing area of interest due to their transferability across the periodic table, displaying an error of about 0.6 kcal/mol against the Matbench Discovery test set. However, we show that achieving more accurate predictions on out-of-domain tasks requires fine-tuning. Additionally, we investigate the existence and influence of model biases in molecular dynamics (MD) by examining two approaches for data generation: from multiple MD trajectories in parallel, which we call naive fine-tuning, and from a single MD trajectory with fine-tuning after set intervals, which we call periodic fine-tuning. Our results find that naive fine-tuning generates constrained datasets that fail to represent MD simulations, and thus downstream fine-tuned models fail during extrapolation. In contrast, periodic fine-tuning yields models which are more generalizable and accurate, producing low-error dynamics. These findings indicate the role of uMLIP bias in fine-tuning, and highlights the need for multiple fine-tuning steps. Lastly, we relate unphysical behavior to principal component space, and quantify extrapolations through Q-residual analysis, which are useful as a proxy for epistemic uncertainty for larger simulations. Transferability ; Trajectory ; Set (abstract data type) ; Interatomic potential ; Data set ; Proxy (statistics) ; Component (thermodynamics) ; Dynamics (music) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Keran Lv | Chenghu Wei | Rui Tu | Wei‐Qiao Deng | Tie Yu
Source: ChemSusChem (article, 2026-02-01)
Field: Chemistry — Physical and Theoretical Chemistry

Mechanocatalysis: Driving Sustainable Chemical Reactions Under Mild Conditions The growing energy shortages and environmental damage make it urgent to activate chemical reactions under mild conditions, reducing energy consumption and improving efficiency. Mechanocatalysis, with its advantages of simplicity, scalability, and sustainability, has demonstrated exceptional performance in many key heterogeneous catalytic reactions and surpassed traditional catalytic methods. It possesses significant potential for future applications and development. In this review, recent advances in the field of mechanocatalysis for energy and environmental applications are systematically summarized. Meanwhile, insights into the design of effective mechanical catalysts and the mechanocatalytic reactions, especially those with gaseous reactants, are highlighted and discussed in detail. Lastly, challenges and future perspectives in the mechanocatalysis are described to guide its broader application in the field of catalysis. Economic shortage ; Catalysis ; Energy consumption ; Sustainable energy ; Chemical reaction ; Chemical energy ; Field (mathematics) ; Chemical process ; Sustainable development Crystallography and molecular interactions ; Force Microscopy Techniques and Applications ; Polyoxometalates: Synthesis and Applications
Authors: Lili Liu | Y. Wang | X. Christine Wang | Liang Yang
Source: The Chemical Record (article, 2026-02-05)
Field: Chemistry — Inorganic Chemistry

Advanced Eco‐Friendly Applications of Metal–Organic Frameworks: From Pollution Control to Energy and Health Technologies Metal-organic frameworks (MOFs), owing to their highly tunable structures, large specific surface areas, rich pore architectures, and diverse functionalities, have emerged as promising candidates for addressing environmental and energy challenges. With continuous advances in green synthesis techniques, eco-friendly applications of MOFs are progressively transitioning from laboratory research to real-world engineering. This review systematically summarizes recent progress in MOF applications across multiple green technology domains, including environmental remediation, sustainable energy conversion and storage, agricultural and food sciences, and healthcare. Emphasis is placed on the mechanisms and performance of MOFs in air pollution control, water treatment, photo/electrocatalytic water splitting and hydrogen storage, lithium-ion batteries and supercapacitors, pesticide delivery systems, food packaging materials, drug delivery, and bioimaging. Furthermore, key challenges facing practical MOF applications, such as material stability, regenerability, scalability in synthesis, and environmental safety, are critically analyzed. Prospects for future research directions are also outlined. This review aims to provide theoretical support and research guidance for the advanced application of MOFs in green chemistry, low-carbon energy, smart agriculture, and precision medicine, thereby promoting their further engineering implementation and industrialization within the framework of sustainable development. Sustainability ; Emerging technologies ; Environmental pollution ; Sustainable energy ; Scalability ; Efficient energy use ; Sustainable development ; Key (lock) Metal-Organic Frameworks: Synthesis and Applications ; Magnesium Oxide Properties and Applications ; Covalent Organic Framework Applications
Authors: Santosh U. U. Sharma | Hung-Ju Yen
Source: Nanoscale (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Conductive metal–organic frameworks: emerging strategies for high-performance energy storage Conductive metal-organic frameworks (c-MOFs) have emerged as a distinctive class of crystalline, electronically active materials that bridge molecular design with electrochemical energy-storage functionality. Their significance is underscored by the recent Nobel Prize in Chemistry recognizing pioneering advances in MOF chemistry, which highlights the transformative impact of reticular design on modern materials science. Unlike conventional MOFs, whose insulating coordination bonds limit charge transport, c-MOFs integrate extended π-d conjugation, redox-active metal nodes, and permanent porosity to enable simultaneous electronic and ionic transport within an ordered framework. This review critically examines the fundamental charge-transport mechanisms governing c-MOFs-including through-bond, through-space, mixed-valence, and guest-mediated pathways-and elucidates how coordination geometry, defect chemistry, pore topology, and interfacial structure collectively regulate charge mobility and redox accessibility. By systematically linking these structure-property relationships to device-level behavior, we compare the performance of c-MOFs across lithium-, sodium-, and potassium-ion batteries, metal-air systems, supercapacitors, and redox-flow batteries, while clarifying their advantages and intrinsic limitations relative to carbons, metal oxides, conducting polymers, and perovskites. Particular emphasis is placed on identifying which conduction mechanisms and framework architectures remain most effective under practical operating conditions, including high areal loading, extended cycling, and commercial electrolytes. Beyond summarizing recent advances, this review provides a critical perspective on unresolved challenges-such as durability, scalable synthesis, and interfacial compatibility-and outlines emerging strategies, including hierarchical pore engineering, hybrid MOF-based architectures, data-driven materials discovery, and chemistry-conserving scale-up routes, that define concrete pathways toward deployable energy-storage technologies. Electrical conductor ; Electrochemical energy storage ; Scalability ; Bridge (graph theory) ; Energy storage ; Perspective (graphical) ; Ionic bonding ; Transformative learning ; Charge (physics) Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Covalent Organic Framework Applications
Authors: Oluwaseye Samson Adedoja | Professor Rendani Wilson Maladzhi | Oludolapo Akanni Olanrewaju | Samson Oluropo Adeosun | Oluwatoyin Joseph Gbadeyan
Source: Nanomaterials (article, 2026-02-24)
Field: Materials Science — Polymers and Plastics

Density Functional Theory Insights into Polypyrrole-Based Functional Composites for Advanced Energy Storage, Sensing, and Environmental Applications Polypyrrole-based functional composites are increasingly explored and extensively adopted for energy storage, sensing, and environmental applications due to their tunable electronic properties, chemical versatility, and mechanical stability. However, rational optimization of these composites requires a unified understanding of electronic, mechanical, thermal, and chemical behavior at the atomic scale, which underlies their multifunctional behavior, and remains fragmented. Notably, Density Functional Theory (DFT) provides indispensable atomistic insight into the electronic, mechanical, thermal, and chemical interactions that govern the performance of multifunctional materials. To bridge these gaps, this review presents a comprehensive assessment of recent DFT and time-dependent DFT (TD-DFT) studies that elucidate the electronic, mechanical, thermal, and chemical characteristics of polypyrrole and its hybrid composites. Key theoretical descriptors, including electronic structure modulation, charge transfer behavior, adsorption energetics, interfacial binding energies, hydrogen bond formation, and charge redistribution, are critically assessed to establish structure-property relationships across diverse functional systems. Considerable attention is given to interfacial interactions, doping strategies, and composite architectures that govern durability, conductivity, and chemical stability. By consolidating current atomistic insights and identifying existing limitations, this review provides a coherent framework for rational material design. Notably, it presents the first systematic quantification of dopant steric effects in PPy multifunctional composites, linking atomistic-scale modifications to the optimization of functional properties in next-generation applications. Density functional theory ; Dopant ; Rational design ; Hybrid functional ; Steric effects ; Chemical bond ; Functional group ; Functional theory ; Doping ; Electronic structure Conducting polymers and applications ; Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials
Authors: Ahmed A. Alamiery
Source: Discover Chemistry. (article, 2026-02-10)
Field: Chemistry — Physical and Theoretical Chemistry

Standardized protocols and applications in mechanochemical synthesis for organic and inorganic materials Mechanochemistry, the utilization of mechanical energy to drive chemical reactions, has emerged as a powerful, sustainable, and solvent-free alternative to traditional synthetic methods. This review presents a comprehensive exploration of standard experimental protocols and methodologies critical to successful mechanochemical synthesis. Fundamental aspects such as the choice of milling equipment, optimization of milling parameters, selection of grinding media, and the application of additives and liquid-assisted grinding (LAG) are discussed in detail. The diverse scope of mechanochemical reactions, encompassing organic transformations, inorganic and solid-state processes, coordination and organometallic chemistry, and polymer and composite formation, is systematically reviewed with representative case studies. Key characterization techniques, including X-ray diffraction (XRD), thermal analysis (TGA/DSC), spectroscopic methods (FTIR, Raman, NMR), electron and atomic force microscopy (SEM, TEM, AFM), and BET surface area and porosity measurements, are highlighted for evaluating the structure and properties of mechanochemical products. The paper further addresses critical challenges impeding the broader adoption of mechanochemistry, including scale-up limitations, reproducibility issues, mechanistic ambiguities, and equipment accessibility. Emerging trends such as the integration of green chemistry principles, in situ and operando reaction monitoring, computational modeling, artificial intelligence-driven reaction optimization, and expanding applications in pharmaceutical and materials science are critically analyzed. Overall, this review aims to provide a foundational guide for researchers entering the field and to serve as a reference for advancing mechanochemistry toward mainstream industrial and scientific applications. Mechanochemistry ; Characterization (materials science) ; Scope (computer science) ; Grinding ; Field (mathematics) Crystallography and molecular interactions ; Force Microscopy Techniques and Applications ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Xuexue Pan | Jiayao Peng | Jun Wang
Source: Biomedical Materials & Devices (article, 2026-02-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Supercapacitors for Neural Signal Simulation: Biomimetic Potential, Technical Challenges, and Future Perspectives This review examines the potential of supercapacitors (SCs) in simulating biological signals, with a particular focus on neural signaling applications. Owing to their rapid charge–discharge kinetics and high power density, SCs exhibit functional parallels with neuronal response mechanisms, positioning them as promising candidates for bioinspired electrochemical devices. The paper begins by outlining the fundamental principles, material architectures, and performance characteristics of SCs. Subsequently, it establishes the theoretical framework for biological signal simulation, encompassing neural signal propagation mechanisms and the properties of bioelectrical signals. The core discussion centers on supercapacitor-based neural signal simulation, addressing the similarities in dynamic behavior with neuronal activity, supported by experimental findings and computational modeling. Current technological limitations, including material optimization, structural design, and biocompatibility challenges, are critically analyzed alongside potential solutions. The review concludes by highlighting emerging applications in neural interfaces and biomedical devices, while identifying key research directions for future investigation. This synthesis highlights the distinct advantages of SCs in bioelectronic signal emulation and underscores the need for continued exploration in this interdisciplinary field. Emulation ; SIGNAL (programming language) ; Artificial neural network ; Biocompatible material ; Supercapacitor ; Neural activity ; Signal processing ; Mechanism (biology) Supercapacitor Materials and Fabrication ; Neuroscience and Neural Engineering ; Advanced Memory and Neural Computing
Authors: Fayçal Adrar | Nicolas Folastre | Chloé Pablos | Stefan Stanescu | Sufal Swaraj | Raghvender Raghvender | François Cadiou | Laurence Croguennec | Matthieu Bugnet | Arnaud Demortière
Source: arXiv (Cornell University) (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

AI-Driven Phase Identification from X-ray Hyperspectral Imaging of cycled Na-ion Cathode Materials Na-ion batteries have emerged as viable candidates for large-scale energy storage applica- tions due to resource abundance and cost advantages. The constraints imposed on their performance and durability, for instance, by complex phase transformations in positive electrode materials during electrochemical cycling, can be addressed and are thus not detrimental to their development. However, diffusion-limited Na-ion transport can drive spatially heterogeneous phase nucleation and propagation, leading to multiphase coexis- tence and locally non-uniform electrochemical activity, generating complex reaction path- ways that challenge both mechanistic understanding and predictive material optimization. These challenges can be addressed by investigating single-crystalline regions of materials, i.e. down to the scale of individual particles, although such analyses are often constrained by energetically and/or spatially sparse hyperspectral datasets. Here, we developed an AI-driven method to process hyperspectral data under sparse sampling conditions and generate multiphase maps with nanometer-scale resolution over a micrometer-scale field of view. We applied this processing on scanning transmission X-ray microscopy (STXM) data to determine the distribution and coexistence of phases in individual particles of NaxV2(PO4)2F3 cathode materials, at different states of charge. The methodology relies on a workflow which combines a Gaussian mixture variational autoencoder (GMVAE) algorithm with the Pearson corre- lation coefficient to identify the sodium content and map their spatial distribution. Our approach reveals nanoscale phase heterogeneity and evolution within individual particles, and improves the reliability of phase detection by identifying ambiguity zones, false assign- ments, and transition phases localized at grain boundaries. Hyperspectral imaging ; Phase (matter) ; Sampling (signal processing) ; Pattern recognition (psychology) ; Mixture model ; Scale (ratio) ; Reliability (semiconductor) ; Gaussian process ; Gaussian ; Workflow Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Ren Okubo | Yu Fujikata | Izumi Takahara | Teruyasu Mizoguchi
Source: ArXiv.org (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Generative Inverse Estimation of 3D Atomic Coordination from Near-Edge Spectra via Equivariant Diffusion Models Extracting 3D atomic coordinates from spectroscopic data is a longstanding inverse problem. We present an equivariant diffusion model that generates site-specific 3D structures directly from near-edge spectra (ELNES/XANES). Trained on Si-O crystals, the model achieves radial accuracy comparable to Extended X-ray Absorption Fine Structure (EXAFS) (RMSD ~0.06 Å) but with superior coordination number precision (errors < 4.3% vs. EXAFS ~20%). Crucially, it reconstructs full 3D geometries including bond angles, overcoming the limitations of 1D radial distribution analysis. The model demonstrates robust out-of-distribution generalization, accurately predicting local structures in amorphous systems despite being trained exclusively on crystalline lattices. Application to experimental O K-edge spectra from α-quartz validates practical applicability. This generative approach outperforms template matching and establishes automated, quantitative 3D structure determination from spectroscopic data. Equivariant map ; Spectral line ; Matching (statistics) ; Diffusion ; Inverse ; Distribution (mathematics) ; Inverse problem ; Generative grammar X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications