jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fangyuan Zheng | Baoyin Yuan | Youfeng Cai | Hongjun Xiang | Chunmei Tang | Ling Meng | Lei Du | Xiting Zhang | Feng Jiao | Yoshitaka Aoki | Ning Wang | Siyu Ye
Source: Nano-Micro Letters (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Machine Learning Tailored Anodes for Efficient Hydrogen Energy Generation in Proton-Conducting Solid Oxide Electrolysis Cells In the global trend of vigorously developing hydrogen energy, proton-conducting solid oxide electrolysis cells (P-SOECs) have attracted significant attention due to their advantages of high efficiency and not requiring precious metals. However, the application of P-SOECs faces challenges, particularly in developing high-performance anodes possessing both high catalytic activity and ionic conductivity. In this study, La<sub>0.9</sub>Ba<sub>0.1</sub>Co<sub>0.7</sub>Ni<sub>0.3</sub>O<sub>3-δ</sub> (LBCN9173) and La<sub>0.9</sub>Ca<sub>0.1</sub>Co<sub>0.7</sub>Ni<sub>0.3</sub>O<sub>3-δ</sub> (LCCN9173) oxides are tailored as promising anodes by machine learning model, achieving the synergistic enhancement of water oxidation reaction kinetics and proton conduction, which is confirmed by comprehensively analyzing experiment and density functional theory calculation results. Furthermore, the anodic reaction mechanisms for P-SOECs with these anodes are elucidated by analyzing distribution of relaxation time spectra and Gibbs energy of water oxidation reaction, manifesting that the dissociation of H<sub>2</sub>O is facilitated on LBCN9173 anode. As a result, P-SOEC with LBCN9173 anode demonstrates a top-rank current density of 2.45 A cm<sup>-2</sup> at 1.3 V and an extremely low polarization resistance of 0.05 Ω cm<sup>2</sup> at 650 °C. This multi-scale, multi-faceted research approach not only discovered a high-performance anode but also proved the robust framework for the machine learning-assisted design of anodes for P-SOECs. Anode ; Oxide ; Electrolysis ; Hydrogen ; Materials science ; Polarization (electrochemistry) ; Gibbs free energy ; Hydrogen production ; Chemical engineering ; Chemistry ; Thermodynamics ; Physical chemistry ; Electrode ; Electrolyte ; Physics ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Advanced battery technologies research
Authors: Yi-Fan Hou | Cheng Wang | Pavlo O. Dral
Source: The Journal of Physical Chemistry A (article, 2025-04-14)
Field: Chemistry — Analytical Chemistry

Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models Infrared (IR) spectroscopy is a potent tool for identifying molecular structures and studying the chemical properties of compounds, and hence, various theoretical approaches have been developed to simulate and predict the IR spectra. However, the theoretical approaches based on quantum chemical calculations suffer from high computational cost (e.g., density functional theory, DFT) or insufficient accuracy (e.g., semiempirical methods orders of magnitude faster than DFT). Here, we introduce a new approach, based on the universal machine learning (ML) models of the AIQM series targeting CCSD(T)/CBS level, that can deliver molecular IR spectra with accuracy close to DFT (compared to the experiment) and the speed close to a semiempirical GFN2-xTB method. This approach is based on the harmonic oscillator approximation with the frequency scaling factors fitted to experimental data. While the benchmarks reported here are focused on harmonic IR spectra, our implementation supports anharmonic spectra simulations via molecular dynamics and VPT2. These implementations are available in MLatom as described in https://github.com/dralgroup/mlatom and can be performed online via a web browser. Infrared ; Infrared spectroscopy ; Spectral line ; Chemistry ; Materials science ; Computational chemistry ; Physics ; Organic chemistry ; Optics ; Astronomy Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Linzheng Wang | Nicolas Tricard | Zi Chen | Sili Deng
Source: Nanoscale (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Progress in computational methods and mechanistic insights on the growth of carbon nanotubes Carbon nanotubes (CNTs), as a promising nanomaterial with broad applications across various fields, are continuously attracting significant research attention. Despite substantial progress in understanding their growth mechanisms, synthesis methods, and post-processing techniques, two major goals remain challenging: achieving property-targeted growth and efficient mass production. Recent advancements in computational methods driven by increased computational resources, the development of platforms, and the refinement of theoretical models, have significantly deepened our understanding of the mechanisms underlying CNT growth. This review aims to comprehensively examine the latest computational techniques that shed light on various aspects of CNT synthesis. The first part of this review focuses on progress in computational methods. Beginning with atomistic simulation approaches, we introduce the fundamentals and advancements in density functional theory (DFT), molecular dynamics (MD) simulations, and kinetic Monte Carlo (kMC) simulations. We discuss the applicability and limitations of each method in studying mechanisms of CNT growth. Then, the focus shifts to multiscale modeling approaches, where we demonstrate the coupling of atomic-scale simulations with reactor-scale multiphase flow models. Given that CNT growth inherently spans multiple temporal and spatial scales, the development and application of multiscale modeling techniques are poised to become a central focus of future computational research in this field. Furthermore, this review emphasizes the growing role played by machine learning in CNT growth research. Compared with traditional physics-based simulation methods, data-driven machine learning approaches have rapidly emerged in recent years, revolutionizing research paradigms from molecular simulation to experimental design. In the second part of this review, we highlight the latest advancements in CNT growth mechanisms and synthesis methods achieved through computational techniques. These include novel findings across fundamental growth stages, <i>i.e.</i>, from nucleation to elongation and ultimately termination. We also examine the dynamic behaviors of catalyst nanoparticles and chirality-controlled growth processes, emphasizing how these insights contribute to advancing the field. Finally, in the concluding section, we propose future directions for advancements of computational approaches toward deeper understanding of CNT growth mechanisms and better support of CNT manufacturing. Carbon nanotube ; Nanotechnology ; Materials science ; Biochemical engineering ; Engineering Carbon Nanotubes in Composites ; Graphene research and applications ; Fullerene Chemistry and Applications
Authors: Abrar U. Hassan | Mamduh J. Aljaafreh
Source: Advanced Theory and Simulations (article, 2025-05-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Machine Learning Study to Explore the Structural Basis of Non‐Conjugated Compounds for Their Optical Activity Features Abstract Various investigations have been aimed to elucidate that why certain molecules exhibit an enhanced nonlinear optical ( NLO ) response than others. Such kind of knowledge is advantageous to design of new NLO switches where their ON / OFF states can display their NLO behavior. Based on the significance of nonconjugated crystal systems, the current study aims at identifying the driving forces of the polarizability/first hyperpolarizability through machine learning ( ML ) analysis by growing crystal of 2,4‐diaminotrotriazole. The study shows that how input of a Simplified Molecular Line Entry System ( SMILES ) of compound into a large polarizability/hyperpolarizability data can display its NLO response. The results show a good correlation between their polarizability and the HOMO–LUMO energy gaps. Their SHapley Additive exPlanations ( SHAP ) analysis reveals that the transition dipole moment () between the ground and first excited state is one of the primary contributors for such molecular systems. It is also illustrated that, besides various non‐conjugated related redox states, the ML model can adequately characterize these NLO responses. Hyperpolarizability ; Polarizability ; Dipole ; Conjugated system ; Excited state ; Moment (physics) ; Chemical physics ; Computational chemistry ; Molecule ; Chemistry ; Materials science ; Physics ; Atomic physics ; Quantum mechanics ; Organic chemistry Nonlinear Optical Materials Research ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity
Authors: Yingfei Hui | Hao Guo | Mingyue Wang | Liping Peng | Henglong Ren | Borong Ren | Wuqiang Yang
Source: Sensors and Actuators B Chemical (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

A boronic acid functionalized fully conjugated covalent organic framework for significantly improved affinity and fluorescence detection for sialic acid Boronic acid ; Sialic acid ; Conjugated system ; Covalent bond ; Chemistry ; Fluorescence ; Combinatorial chemistry ; Organic chemistry ; Biochemistry ; Polymer Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection
Authors: Bingqin Wang | Xuequn Cheng | Luntao Wang | Zhong Li | Chao Liu | Dawei Zhang | Xiaogang Li
Source: Journal of Material Science and Technology (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Big data mining of corrosion for weathering steel in marine atmospheric environments: Discovery and mechanism of critical temperature influencing corrosion resistance Weathering steel ; Corrosion ; Rust (programming language) ; Weathering ; Layer (electronics) ; Mechanism (biology) Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Offshore Engineering and Technologies
Authors: Shijie Pan | Wenhao Wang | Jun Zhu
Source: The Journal of Physical Chemistry A (article, 2025-08-05)
Field: Chemistry — Organic Chemistry

Antiaromaticity-Promoted Radical Stability in Boryl Heterocyclics and Its Application to Dinitrogen Activation: A Combined DFT and Machine Learning Study Aromaticity is one of the fundamental concepts in chemistry and generally brings additional thermodynamic stability to a compound. On the other hand, boron radicals have attracted increasing interest from both theoretical and experimental chemists due to their various applications. Here, we carry out density functional theory (DFT) calculations to explore the relationship between the (anti)aromaticity and stability of boron-centered radicals. It is found that stronger antiaromaticity of the parent heterocyclics leads to higher thermodynamic stability of the corresponding radicals, which is in sharp contrast to the general knowledge that antiaromaticity brings compounds' thermodynamic instabilities. In addition, the boryl radicals derived from aromatic precursors are predicated to be favorable both thermodynamically and kinetically for dinitrogen activation. Furthermore, through principal component analysis (PCA), one of the most commonly used unsupervised machine learning algorithms, we have identified that aromaticity, the HOMO-LUMO gap, and spin density collectively contribute to the reaction barrier of dinitrogen activation. Antiaromaticity ; Chemistry ; Stability (learning theory) ; Computational chemistry ; Medicinal chemistry ; Combinatorial chemistry ; Computer science ; Organic chemistry ; Machine learning ; Aromaticity Synthesis and Properties of Aromatic Compounds ; Organic Light-Emitting Diodes Research ; Radical Photochemical Reactions
Authors: Christina Soyoung Song | Yaling An | Wenjie Zhao | Yang Huang | Lijie Zhang | Ling Li | Zijun Tang | Zhenwei Li | Xiao‐Kang Liu | Dongping Zhang | De‐an Guo
Source: Microchemical Journal (article, 2025-01-04)
Field: Chemistry — Analytical Chemistry

A chemometric and machine learning scheme for classification of 37 kinds of aerial parts of medicinal herbs based on ATR-FTIR Medicinal herbs ; Fourier transform infrared spectroscopy ; Scheme (mathematics) ; Classification scheme ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Traditional medicine ; Machine learning ; Chemistry ; Mathematics ; Engineering ; Chemical engineering ; Medicine Spectroscopy and Chemometric Analyses ; Flavonoids in Medical Research ; Phytochemistry and Biological Activities
Authors: Artem Zholus | Maksim Kuznetsov | Roman Schutski | Rim Shayakhmetov | Daniil Polykovskiy | Sarath Chandar | Alex Zhavoronkov
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding of the complex physical interactions between the molecule and its environment. This paper presents a novel generative model, BindGPT, which uses a conceptually simple but powerful approach to create 3D molecules within the protein's binding site. Our model produces molecular graphs and conformations jointly, eliminating the need for an extra graph reconstruction step. We pre-train BindGPT on a large-scale dataset and fine-tune it with reinforcement learning using scores from external simulation software. We demonstrate how a single pre-trained language model can serve at the same time as a 3D molecular generative model, a conformer generator conditioned on the molecular graph, and a pocket-conditioned 3D molecule generator. Notably, the model does not make any representational equivariance assumptions about the domain of generation. We show how such a simple conceptual approach combined with pre-training and scaling can perform on par or better than the current best-specialized diffusion models, language models, and graph neural networks while being two orders of magnitude cheaper to sample. Reinforcement learning ; Computer science ; Scalability ; Reinforcement ; Human–computer interaction ; Cognitive science ; Natural language processing ; Artificial intelligence ; Psychology ; Social psychology Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Long Liu | Bin Wang | Xiaoxuan Xu | Jing Xu
Source: Infrared Physics & Technology (article, 2025-01-05)
Field: Chemistry — Analytical Chemistry

A tea classification method based on near infrared spectroscopy (NIRS) and transfer learning Spectroscopy ; Infrared ; Near-infrared spectroscopy ; Infrared spectroscopy ; Materials science ; Computer science ; Remote sensing ; Optics ; Chemistry ; Physics ; Geology ; Astronomy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Mosaab A. Elbager | Mohammad Al-Suwaiyan | Tawfik A. Saleh | Qusai M. Karrar | Nadir Mohammed Osman | Maimuna U. Zarewa
Source: Journal of Molecular Liquids (article, 2025-09-27)
Field: Chemistry — Analytical Chemistry

Development of methacrylamide/methylmethacrylate copolymer modified biomass-carbon for superior Congo red adsorption: Leveraging RSM and machine learning for optimization and mechanistic insights Adsorption ; Congo red ; Response surface methodology ; Central composite design ; Zeta potential ; Methacrylamide ; Endothermic process ; Freundlich equation ; Copolymer Dye analysis and toxicity ; Water Quality Monitoring and Analysis
Authors: Boris A. Panchenko | E. V. Fomin | Alexander E. Mayer
Source: Computational Materials Science (article, 2025-04-06)
Field: Chemistry — Physical and Theoretical Chemistry

Tensor equation of state for copper and aluminum Copper ; Tensor (intrinsic definition) ; Aluminium ; State (computer science) ; Equation of state ; Materials science ; Mathematical physics ; Chemistry ; Metallurgy ; Physics ; Thermodynamics ; Mathematics ; Pure mathematics Advanced Physical and Chemical Molecular Interactions ; Surface and Thin Film Phenomena ; High-pressure geophysics and materials
Authors: Da Xu | William C. Thomas | Audrey A. Burnim | Nozomi Ando
Source: Nature Communications (article, 2025-03-12)
Field: Chemistry — Inorganic Chemistry

Conformational landscapes of a class I ribonucleotide reductase complex during turnover reveal intrinsic dynamics and asymmetry Understanding the structural dynamics associated with enzymatic catalysis has been a long-standing goal of biochemistry. With the advent of modern cryo-electron microscopy (cryo-EM), it has become conceivable to redefine a protein's structure as the continuum of all conformations and their distributions. However, capturing and interpreting this information remains challenging. Here, we use classification and deep-learning-based analyses to characterize the conformational heterogeneity of a class I ribonucleotide reductase (RNR) during turnover. By converting the resulting information into physically interpretable 2D conformational landscapes, we demonstrate that RNR continuously samples a wide range of motions while maintaining surprising asymmetry to regulate the two halves of its turnover cycle. Remarkably, we directly observe the appearance of highly transient conformations needed for catalysis, as well as the interaction of RNR with its endogenous reductant thioredoxin also contributing to the asymmetry and dynamics of the enzyme complex. Overall, this work highlights the role of conformational dynamics in regulating key steps in enzyme mechanisms. Ribonucleotide reductase ; Asymmetry ; Dynamics (music) ; Biophysics ; Chemistry ; Biology ; Cell biology ; Biochemistry ; Physics ; Gene ; Particle physics Metal-Catalyzed Oxygenation Mechanisms ; Advanced Electron Microscopy Techniques and Applications ; Photosynthetic Processes and Mechanisms
Authors: Shuaiwu Tong | Shuaijun Zhang | Chong Chen | Tao Jiang | Liming Peng | Shizhong Wei
Source: Journal of Materials Research and Technology (article, 2025-04-15)
Field: Materials Science — Materials Chemistry

Building a composition-microstructure-performance model for C–V–Cr–Mo wear-resistant steel via the thermodynamic calculations and machine learning synergy To better understand the complex relationships between composition, microstructure, and performance in C–V–Cr–Mo wear-resistant steel, and to facilitate the rapid and efficient development of high-alloy steels for extreme working conditions, this study proposes a collaborative approach that integrates thermodynamic calculations and machine learning. Based on systematic composition design and friction-wear experimental data, microstructure data for the corresponding components were first derived through thermodynamic calculations. Subsequently, the strong coupling between microstructure composition and performance was explored using machine learning techniques. The results indicated that the relative content of vanadium (V), chromium (Cr), and carbon (C) directly influenced the content and distribution of carbides within the steel. By using phase content and experimental parameters as input features, the Gradient Boosted Tree model and Support Vector Regression model demonstrated strong applicability in predicting frictional performance and wear, respectively. Furthermore, feature importance analysis, conducted using the Random Forest algorithm, revealed significant differences in the factors affecting sliding friction and abrasive wear. Dry sliding friction was primarily governed by surface modification behaviors, while abrasive wear resistance was more dependent on the material's ability to resist plastic deformation. These findings contribute to the development of a composition-microstructure-performance model for C–V–Cr–Mo wear-resistant steel, supporting the rapid design and performance optimization of multi-component alloy steels, with significant potential for industrial application. Materials science ; Microstructure ; Metallurgy ; Composition (language) Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Advanced materials and composites
Authors: Zhexu Xi
Source: RSC Advances (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Revisiting the Marcus inverted regime: modulation strategies for photogenerated ultrafast carrier transfer from semiconducting quantum dots to metal oxides Ultrafast charge transfer at quantum-dot/metal-oxide (QD-MO) heterojunctions governs the performance ceiling of emerging solar-energy and optoelectronic technologies. This review distills three decades of progress, covering the evolution from classical Marcus theory to modern multi-state, many-body models; the rise of exascale non-adiabatic simulations; and femtosecond spectroscopies that track electron motion in real time. Despite converging evidence for activation-less transfer under strong coupling, direct observation of the Marcus inverted regime remains scarce, largely due to Auger pathways, continuum acceptor states and interfacial defect complexity. We spotlight current strategies-outer-sphere dielectric engineering, single-charge pump-probe designs, and data-driven interface optimization-that are poised to reveal or harness inverted-region kinetics. Looking ahead, integrating low-<i>λ</i> materials, suppressing multi-carrier losses, and uniting <i>operando</i> probes with machine learning could shift QD-MO systems from kinetically limited to thermodynamically dictated performance, inspiring advances in solar fuels, infrared photodetectors and solid-state lighting. Ultrashort pulse ; Metal ; Quantum dot ; Materials science ; Modulation (music) ; Marcus theory ; Optoelectronics ; Quantum ; Physics ; Quantum mechanics Quantum Dots Synthesis And Properties ; Molecular Junctions and Nanostructures ; Semiconductor Quantum Structures and Devices
Authors: Matthew C. Kuner | Aaron D. Kaplan | Kristin A. Persson | Mark Asta | D. C. Chrzan
Source: npj Computational Materials (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

MP-ALOE: an r2SCAN dataset for universal machine learning interatomic potentials Abstract We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r 2 SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium structures. We benchmark a machine learning interatomic potential trained on MP-ALOE, and evaluate its performance on a series of benchmarks, including predicting the thermochemical properties of equilibrium structures; predicting forces of far-from-equilibrium structures; maintaining physical soundness under static extreme deformations; and molecular dynamic stability under extreme temperatures and pressures. MP-ALOE shows strong performance on all of these benchmarks and is made public for the broader community to utilize. Stability (learning theory) ; Soundness ; Benchmark (surveying) ; Interatomic potential ; Series (stratigraphy) ; Extreme learning machine Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Crystallography and molecular interactions