jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Longbing Qu | Peiyao Wang | Benyamin Motevalli | Qinghua Liang | Kangyan Wang | Wenjie Jiang | Jefferson Zhe Liu | Dan Li
Source: Advanced Materials (article, 2024-06-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

New Engineering Science Insights into the Electrode Materials Pairing of Electrochemical Energy Storage Devices Abstract Pairing the positive and negative electrodes with their individual dynamic characteristics at a realistic cell level is essential to the practical optimal design of electrochemical energy storage devices. However, the complex relationship between the performance data measured for individual electrodes and the two‐electrode cells used in practice often makes an optimal pairing experimentally challenging. Taking advantage of the developed tunable graphene‐based electrodes with controllable structure, experiments with machine learning are successfully united to generate a large pool of capacitance data for graphene‐based electrode materials with varied slit pore sizes, thicknesses, and charging rates and numerically pair them into different combinations for two‐electrode cells. The results show that the optimal pairing parameters of positive and negative electrodes vary considerably with the operation rate of the cells and are even influenced by the thickness of inactive components. The best‐performing individual electrode does not necessarily result in optimal cell‐level performance. The machine learning‐assisted pairing approach presents much higher efficiency compared with the traditional trial‐and‐error approach for the optimal design of supercapacitors. The new engineering science insights observed in this work enable the adoption of artificial intelligence techniques to efficiently translate well‐developed high‐performance individual electrode materials into real energy storage devices. Pairing ; Electrode ; Materials science ; Supercapacitor ; Energy storage ; Capacitance ; Graphene ; Nanotechnology ; Electrochemical energy storage ; Electrochemistry ; Work (physics) ; Computer science ; Optoelectronics ; Mechanical engineering ; Engineering ; Physics ; Thermodynamics Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advancements in Battery Materials
Authors: Tunca Karasu | Fatih Çalışır | Sena Pişkin | Erdoğan Özgür | Canan Armutçu | M. Emin Çorman | Lokman Uzun
Source: Journal of Pharmaceutical and Biomedical Analysis Open (article, 2024-09-06)
Field: Chemistry — Analytical Chemistry

An intriguing future is approaching: Artificial intelligence meets molecularly imprinted polymers Artificial intelligence (AI) is developing and expanding rapidly beyond the expectations of engineers who develop it as well. It is evaluated that it has/will have great potential not only in terms of software but also in terms of material science, data analysis, and decision-making. On the other hand; molecularly imprinted polymers (MIPs) still attract researchers’ attention with their superior features in terms of wide range of design options and application fields. In this review, the combination of AI and MIP applications and their potential contributions to the future were compiled. In this context, firstly, a brief introduction to MIPs were given in a combination with recent sensor design approaches. Subsequently, the subject of artificial intelligence, in other commonly used words, machine learning, was discussed while summarizing the commonly used algorithms. In the last section, pioneering studies involving the combination of AI and MIPs are highlighted. This review will be the first article compiling AI and MIPs as well as it is thought to be an important main resource for researchers and enthusiasts. • AI is developing and expanding rapidly beyond the expectations of engineers. • MIPs still attract researchers’ attention with their superior features. • The combination of AI and MIP applications and their potential contributions to the future. • The dependency of biosensors on AI will increase day-by-day. • AI will metamorphose healthcare, environmental monitoring, and food safety Molecularly imprinted polymer ; Computer science ; Nanotechnology ; Artificial intelligence ; Materials science ; Chemistry ; Organic chemistry Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Anders S. Larsen | Toms Rekis | Anders Ø. Madsen
Source: Science (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

PhAI: A deep-learning approach to solve the crystallographic phase problem X-ray crystallography provides a distinctive view on the three-dimensional structure of crystals. To reconstruct the electron density map, the complex structure factors [Formula: see text] of a sufficiently large number of diffracted reflections must be known. In a conventional experiment, only the amplitudes [Formula: see text] are obtained, and the phases ϕ are lost. This is the crystallographic phase problem. In this work, we show that a neural network, trained on millions of artificial structure data, can solve the phase problem at a resolution of only 2 angstroms, using only 10 to 20% of the data needed for direct methods. The network works in common space groups and for modest unit-cell dimensions and suggests that neural networks could be used to solve the phase problem in the general case for weakly scattering crystals. Phase problem ; Artificial neural network ; Phase (matter) ; Angstrom ; Diffraction ; Phaser ; Resolution (logic) ; Amplitude ; Computer science ; Space (punctuation) ; Crystal structure ; Electron density ; Crystallography ; Physics ; Algorithm ; Artificial intelligence ; Electron ; Optics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Zuhong Lin | Hui Cai | Hongjia Peng | Ying Fang | Pengxu Pan | Haipu Li | Ying Yang | Jingjing Yao
Source: Chemical Engineering Journal (article, 2024-08-22)
Field: Chemistry — Inorganic Chemistry

Enhancing arsenate removal through interpretable machine learning guiding the modular design of metal–organic frameworks Modular design ; Arsenate ; Metal-organic framework ; Computer science ; Process engineering ; Systems engineering ; Artificial intelligence ; Biochemical engineering ; Engineering ; Chemistry ; Organic chemistry ; Adsorption ; Programming language ; Arsenic Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization ; Polyoxometalates: Synthesis and Applications
Authors: Weida Zhang | Yusi Peng | Chenglong Lin | Meimei Xu | Shuai Zhao | Dan Li | Yingchun Yang | Yong Yang
Source: Chemical Engineering Journal (article, 2024-11-23)
Field: Materials Science — Materials Chemistry

A novel ultra-sensitive semiconductor SERS substrate V5S4 nanopompons for the specific detection of antibiotics with AI technology Substrate (aquarium) ; Nanotechnology ; Semiconductor ; Materials science ; Antibiotics ; Optoelectronics ; Chemistry ; Biology Advanced Nanomaterials in Catalysis ; Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Pinchen Xie | Roberto Car | E Weinan
Source: Proceedings of the National Academy of Sciences (article, 2024-03-29)
Field: Materials Science — Materials Chemistry

Ab initio generalized Langevin equation We introduce a machine learning-based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori-Zwanzig formalism, under the constraint of the fluctuation-dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency. Statistical physics ; Ab initio ; Physics ; Langevin dynamics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Theoretical and Computational Physics
Authors: Liyi Bai | Changming Ke | Zhongshen Luo | Tianyuan Zhu | Lü You | Shi Liu
Source: ACS Nano (article, 2024-09-15)
Field: Materials Science — Materials Chemistry

Intrinsic Ferroelectric Switching in Two-Dimensional α-In<sub>2</sub>Se<sub>3</sub> Two-dimensional (2D) ferroelectric semiconductors present opportunities for integrating ferroelectrics into high-density ultrathin nanoelectronics. Among the few synthesized 2D ferroelectrics, α-In<sub>2</sub>Se<sub>3</sub>, known for its electrically addressable vertical polarization, has attracted significant interest. However, the understanding of many fundamental characteristics of this material, such as the existence of spontaneous in-plane polarization and switching mechanisms, remains controversial, marked by conflicting experimental and theoretical results. Here, our combined experimental characterizations with piezoresponse force microscope and symmetry analysis conclusively dismiss previous claims of in-plane ferroelectricity in single-domain α-In<sub>2</sub>Se<sub>3</sub>. The processes of vertical polarization switching in monolayer α-In<sub>2</sub>Se<sub>3</sub> are explored with deep-learning-assisted large-scale molecular dynamics simulations, revealing atomistic mechanisms fundamentally different from those of bulk ferroelectrics. Despite lacking in-plane effective polarization, 1D domain walls can be moved by both out-of-plane and in-plane fields, exhibiting avalanche dynamics characterized by abrupt, intermittent moving patterns. The propagating velocity at various temperatures, field orientations, and strengths can be statistically described with a universal creep equation, featuring a dynamical exponent of 2 that is distinct from all known values for elastic interfaces moving in disordered media. This work rectifies a long-held misunderstanding regarding the in-plane ferroelectricity of α-In<sub>2</sub>Se<sub>3</sub>, and the quantitative characterizations of domain wall velocity will hold broad implications for both the fundamental understanding and technological applications of 2D ferroelectrics. Ferroelectricity ; Nanoelectronics ; Materials science ; Semiconductor ; Condensed matter physics ; Nanotechnology ; Optoelectronics ; Engineering physics ; Physics ; Dielectric 2D Materials and Applications ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Elizabeth R. Guest | Jonathan Tennyson | S. N. Yurchenko
Source: Journal of Molecular Spectroscopy (article, 2024-03-01)
Field: Chemistry — Spectroscopy

Predicting the rotational dependence of line broadening using machine learning Correct pressure broadening is essential for modelling radiative transfer in atmospheres, however data are lacking for the many exotic molecules expected in exoplanetary atmospheres. Here we explore modern machine learning methods to mass produce pressure broadening parameters for a large number of molecules in the ExoMol data base. To this end, state-of-the-art machine learning models are used to fit to existing, empirical air-broadening data from the HITRAN database. A computationally cheap method for large-scale production of pressure broadening parameters is developed, which is shown to be reasonably (69%) accurate for unseen active molecules. This method has been used to augment the previously insufficient ExoMol line broadening diet, providing air-broadening data for all ExoMol molecules, so that the ExoMol database has a full and more accurate treatment of line broadening. Suggestions are made for improved air-broadening parameters for species present in atmospheric databases. HITRAN ; Homogeneous broadening ; Radiative transfer ; Line (geometry) ; Atmospheric pressure ; Doppler broadening ; Computer science ; Computational physics ; Materials science ; Physics ; Spectral line ; Meteorology ; Optics ; Quantum mechanics ; Mathematics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Atmospheric and Environmental Gas Dynamics
Authors: Dustin Broderick | John M. Herbert
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Delocalization error poisons the density-functional many-body expansion The many-body expansion is a fragment-based approach to large-scale quantum chemistry that partitions a single monolithic calculation into manageable subsystems. This technique is increasingly being used as a basis for fitting classical force fields to electronic structure data, especially for water and aqueous ions, and for machine learning. Here, we show that the many-body expansion based on semilocal density functional theory affords wild oscillations and runaway error accumulation for ion-water interactions, typified by F<sup>-</sup>(H<sub>2</sub>O) <sub><i>N</i></sub> with <i>N</i> ≳ 15. We attribute these oscillations to self-interaction error in the density-functional approximation. The effect is minor or negligible in small water clusters, explaining why it has not been noticed previously, but grows to catastrophic proportion in clusters that are only moderately larger. This behavior can be counteracted with hybrid functionals but only if the fraction of exact exchange is ≳50%, whereas modern meta-generalized gradient approximations including ωB97X-V, SCAN, and SCAN0 are insufficient to eliminate divergent behavior. Other mitigation strategies including counterpoise correction, density correction (<i>i.e.</i>, exchange-correlation functionals evaluated atop Hartree-Fock densities), and dielectric continuum boundary conditions do little to curtail the problematic oscillations. In contrast, energy-based screening to cull unimportant subsystems can successfully forestall divergent behavior. These results suggest that extreme caution is warranted when the many-body expansion is combined with density functional theory. Density functional theory ; Delocalized electron ; Statistical physics ; Computer science ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Theoretical and Computational Physics
Authors: Binshan Huang | Songhao Li | Teng Long | Shudai Bai | Jing Zhao | Haitao Xu | Yubin Lan | Houcheng Liu | Yongbing Long
Source: Microchemical Journal (article, 2024-06-29)
Field: Chemistry — Analytical Chemistry

Research on predicting photosynthetic pigments in tomato seedling leaves based on near-infrared hyperspectral imaging and machine learning Hyperspectral imaging ; Seedling ; Photosynthesis ; Infrared ; Carotenoid ; Near-infrared spectroscopy ; Pigment ; Remote sensing ; Chemistry ; Botany ; Environmental science ; Artificial intelligence ; Computer science ; Biology ; Physics ; Geology ; Optics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Leaf Properties and Growth Measurement
Authors: Huili Liang | Xiaoyan Tang | Hang Shao | Rui Zhu | Sheng Deng | Xiaozhi Zhan | Zhu Tao | Jiwei Wang | Jihua Zhang | Guangyu Zhang | Zengxia Mei
Source: Advanced Science (article, 2024-11-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Retina‐Inspired X‐Ray Optoelectronic Synapse Using Amorphous Ga<sub>2</sub>O<sub>3</sub> Thin Film Machine vision techniques are widely applied for object identification in daily life and industrial production, where images are captured and processed by sensors, memories, and processing units sequentially. Neuromorphic optoelectronic synapses, as a preferable option to promote the efficiency of image recognition, are hotly pursued in non-ionizing radiation range, but rarely in ionizing radiation including X-rays. Here, the study proposes an X-ray optoelectronic synapse using amorphous Ga<sub>2</sub>O<sub>3</sub> (a-Ga<sub>2</sub>O<sub>3</sub>) thin film. Boosted by the interfacial V<sub>O</sub> <sup>2+</sup> defects and its slow neutralization rate, the enhanced electron tunneling process at metal/a-Ga<sub>2</sub>O<sub>3</sub> interface produces remarkable X-ray-induced post-synaptic current, contributing to a sensitivity of 20.5, 64.3, 164.1 µC mGy<sup>-1</sup> cm<sup>-2</sup> for the 1st, 5th, and 10th excitation periods, respectively. Further, a 64 × 64 imaging sensor is constructed on a commercial amorphous Si (a-Si) thin film transistor (TFT) array. The image contrast can be apparently improved under a series of X-ray pulses due to an outstanding long-term plasticity of the single pixel, which is beneficial to the subsequent image recognition and classification based on artificial neural network. The merits of large-scale production ability and good compatibility with modern microelectronic techniques belonging to amorphous oxide semiconductors may promote the development of neuro-inspired X-ray imagers and corresponding machine vision systems. Materials science ; Amorphous solid ; Optoelectronics ; Neuromorphic engineering ; Microelectronics ; Thin-film transistor ; Thin film ; Transistor ; Computer science ; Artificial intelligence ; Nanotechnology ; Artificial neural network ; Electrical engineering ; Chemistry Ga2O3 and related materials ; Advanced Memory and Neural Computing ; CCD and CMOS Imaging Sensors
Authors: Yongheng Li | Xiang Li | Bin Wei | Juanjuan Liu | Feihao Pan | Hongliang Wang | Peng Cheng | Hongxia Zhang | Daye Xu | Wei Bao | Jinchen Wang | Lijie Hao | Guochu Deng | Guodong Zhang | Jiawang Hong
Source: Advanced Functional Materials (article, 2024-08-22)
Field: Materials Science — Materials Chemistry

Phonon Coherence in Bismuth‐Halide Perovskite Cs<sub>3</sub>Bi<sub>2</sub>Br<sub>9</sub> With Ultralow Thermal Conductivity Abstract Halide perovskites emerge as promising candidates for thermoelectrics due to their ultralow thermal conductivity. The conventional theory based on the phonon gas model, which treats thermal transport as particle‐like behavior, shows limitations to describe the unusual thermal transport property in some halide perovskites with strong anharmonicity. Here, the significance of phonon coherence effect on thermal transport of bismuth‐halide perovskite Cs 3 Bi 2 Br 9 is reported by inelastic neutron scattering and simulations including density functional theory and machine‐learning potential based molecular dynamics. This study shows that the restrictive low‐energy acoustic phonons lead to the limited particle‐like thermal conductivity, which seriously underestimates the lattice thermal conductivity of Cs 3 Bi 2 Br 9 . The significant contribution of wave‐like optical phonon modes, driven by the coherence effect, accounts for an additional ≈50% wave‐like thermal conductivity. Besides, the experimental weak temperature dependence of thermal conductivity along z direction ( κ ≈ T −0.35 ) is well reproduced by calculation ( κ ≈ T −0.37 ) when including phonon coherence. This work highlights the critical role of phonon coherence in Cs 3 Bi 2 Br 9 and enhances understanding on the unusual thermal transport properties in halide perovskites and other related materials with strong anharmonicity. Anharmonicity ; Phonon ; Thermal conductivity ; Materials science ; Bismuth ; Condensed matter physics ; Thermoelectric materials ; Perovskite (structure) ; Halide ; Phonon scattering ; Scattering ; Coherence length ; Physics ; Inorganic chemistry ; Optics ; Chemistry ; Crystallography Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Optical properties and cooling technologies in crystalline materials
Authors: Yu Chen | Guobin Zhao | Sunghyun Yoon | Parsa Habibi | Chang Seop Hong | Song Li | Othonas A. Moultos | Poulumi Dey | Thijs J. H. Vlugt | Yongchul G. Chung
Source: ACS Applied Materials & Interfaces (article, 2024-10-30)
Field: Chemistry — Inorganic Chemistry

Computational Exploration of Adsorption-Based Hydrogen Storage in Mg-Alkoxide Functionalized Covalent-Organic Frameworks (COFs): Force-Field and Machine Learning Models Hydrogen is a clean-burning fuel that can be converted to other forms. of energy without generating any greenhouse gases. Currently, hydrogen is stored either by compression to high pressure (>700 bar) or cryogenic cooling to liquid form (<23 K). Therefore, it is essential to develop safe, reliable, and energy-efficient storage technology that can store hydrogen at lower pressures and temperatures. In this work, we systematically designed 2902 Mg-alkoxide-functionalized covalent-organic frameworks (COFs) and performed high-throughput (HT) computational screening for hydrogen storage applications at 111, 231, and 296 K. To accurately model the interaction between Mg-alkoxide sites and molecular hydrogen, we performed MP2 calculations to compute the hydrogen binding energy for different types of functionalized models, and the data were subsequently used to fit modified-Morse force field (FF) parameters. Using the developed FF models, we conducted HT grand canonical Monte Carlo (GCMC) simulations to compute hydrogen uptakes for both original and functionalized COFs. The generated data were subsequently used to evaluate the materials' gravimetric and volumetric storage performance at various temperatures (111, 231, and 296 K). Finally, we developed machine learning (ML) models to predict the hydrogen storage performance of functionalized structures based on the features of the original structures. The developed model showed excellent performance with a mean absolute error (MAE) of 0.061 wt % and 0.456 g/L for predicting the gravimetric and volumetric deliverable capacities, enabling a quick evaluation of structures in a hypothetical COF database. The screening results demonstrated that the Mg-alkoxide functionalization yields greater improvements in volumetric H<sub>2</sub> storage capacities for COFs with smaller pores compared to those with larger (mesoporous) pores. Materials science ; Alkoxide ; Adsorption ; Covalent bond ; Hydrogen storage ; Field (mathematics) ; Nanotechnology ; Chemical engineering ; Organic chemistry ; Composite material ; Engineering ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Enhanced Oil Recovery Techniques
Authors: Ying Sun | Binbin Hu | Yiting Zhang | Xilong Song | Jiaqing Feng | Xu Yong | Hong Tao | Daji Ergu
Source: Journal of Alloys and Compounds (article, 2024-07-19)
Field: Materials Science — Materials Chemistry

Machine learning-assisted accelerated research on piezoelectric response prediction of KNN-based ceramics Piezoelectricity ; Ceramic ; Materials science ; Artificial intelligence ; Machine learning ; Computer science ; Composite material Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies