jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maohua Li | Ping Gong | Zhenbang Zhang | Linhong Li | Yapeng Chen | Yue Qin | Yingying Guo | Rongjie Yang | Jianxiang Zhang | Yiwei Zhou | Kang Xu | Yandong Wang | Hainam Do | Xiaofei Jia | Tao Cai | Cheng‐Te Lin | Nan Jiang | Jinhong Yu
Source: Communications Materials (article, 2024-02-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electric-field-aligned liquid crystal polymer for doubling anisotropic thermal conductivity Abstract High thermal conductive polymers have become more important because equipment requires high performance, high-energy density, and high integration. There are different strategies to make high thermal conductive polymers, among which is the synthesis of polymers in the liquid crystal phase. However, the thermal conductivity of such material is rarely beyond 1 W m −1 K −1 because of the disordered molecular directionality. The disordered directionality between crystal zones limits the thermal conductivity in a specific direction. Here, we show a method for unifying the direction of crystal zones by applying an external electric field on the liquid crystal monomers. Meanwhile, by exposing the transparent equipment and specially designed photopolymerisable monomer in UV light, the liquid crystal monomer is in situ polymerised into a liquid crystal polymer with a high intrinsic thermal conductivity of 1.02 W m −1 K −1 . The molecular alignment was characterised and resulted in the resultant high conductivity. Thermal conductivity ; Electric field ; Liquid crystal ; Anisotropy ; Materials science ; Field (mathematics) ; Condensed matter physics ; Polymer ; Thermal ; Physics ; Optics ; Optoelectronics ; Thermodynamics ; Composite material ; Mathematics ; Quantum mechanics Liquid Crystal Research Advancements ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Sandeep Kumar | Raeesh Muhammad | Sunghyun Kim | Jungwon Yi | Kwanghyo Son | Hyunchul Oh
Source: Advanced Functional Materials (article, 2024-05-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring Magnetocaloric Materials for Sustainable Refrigeration near Hydrogen Gas Liquefaction Temperature Abstract Magnetocaloric materials have the ability to undergo temperature changes when subjected to varying magnetic fields. These materials are of interest due to their potential for innovative cooling applications. This review article summarizes materials that exhibit magnetic ordering within the temperature range required for gas liquefaction and explores their potential applications through the magnetocaloric effect (MCE). The gas liquefaction temperature range is typically assumed to be 20–77 K, however, this study specifically summarizes materials that have a transition temperature near to the hydrogen liquefaction temperature (≈20K). This review article aims to showcase ongoing research on magnetic materials for hydrogen liquefaction. Driven by the depletion of natural resources and environmental concerns, the search for environmentally sustainable fuels has intensified, making hydrogen a promising alternative. However, the liquefaction of hydrogen is highly energy‐intensive. The investigation focuses on identifying and understanding these materials and assessing their suitability for environmentally friendly and sustainable cooling technologies. By harnessing the magnetocaloric effect, these materials exhibit temperature changes in response to an applied magnetic field, offering advantages over traditional cooling methods that are 20–50% more efficient. The review aims to furnish researchers with essential information that can help modify magnetocaloric effect (MCE) materials, enabling them to achieve the desired magnetic ordering temperature conducive to the liquefaction of hydrogen. Magnetic refrigeration ; Materials science ; Liquefaction ; Refrigeration ; Refrigerant ; Hydrogen ; Chemical engineering ; Thermodynamics ; Organic chemistry ; Gas compressor ; Magnetization Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Thermal Expansion and Ionic Conductivity
Authors: Yunlai Fu | Xuxi Zhou | Yiwan Yu | Jiawang Chen | Shuming Wang | Shining Zhu | Zhenlin Wang
Source: Nanophotonics (review, 2024-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unleashing the potential: AI empowered advanced metasurface research In recent years, metasurface, as a representative of micro- and nano-optics, have demonstrated a powerful ability to manipulate light, which can modulate a variety of physical parameters, such as wavelength, phase, and amplitude, to achieve various functions and substantially improve the performance of conventional optical components and systems. Artificial Intelligence (AI) is an emerging strong and effective computational tool that has been rapidly integrated into the study of physical sciences over the decades and has played an important role in the study of metasurface. This review starts with a brief introduction to the basics and then describes cases where AI and metasurface research have converged: from AI-assisted design of metasurface elements up to advanced optical systems based on metasurface. We demonstrate the advanced computational power of AI, as well as its ability to extract and analyze a wide range of optical information, and analyze the limitations of the available research resources. Finally conclude by presenting the challenges posed by the convergence of disciplines. Nanomaterials ; Nanotechnology ; Metamaterial ; Computer science ; Materials science ; Optoelectronics Metamaterials and Metasurfaces Applications ; Advanced Memory and Neural Computing ; Molecular Communication and Nanonetworks
Authors: Chao Ye | Hao Zhang | Yunhao Yang | Yicheng Shan | Junhao Fu | Wenli Gao | Jing Ren | Leitao Cao | Shengjie Ling
Source: Advanced Materials (article, 2024-12-08)
Field: Materials Science — Biomaterials

Sustainable Silk Fibroin Ionic Touch Screens for Flexible Biodegradable Electronics with Integrated AI and IoT Functionality The increasing prevalence of electronic devices has led to a significant rise in electronic waste (e-waste), necessitating the development of sustainable materials for flexible electronics. In this study, silk fibroin ionic touch screen (SFITS) is introduced, a new platform integrating natural silk fibroin (SF) with ionic conductors to create highly elastic, environmentally stable, and multifunctional touch interfaces. Through a humidity-induced crystallization strategy, the molecular structure of SF is precisely controlled to achieve a balanced combination of mechanical strength, electrical conductivity, and biodegradability. The assembly and operational reliability of SFITS are demonstrated under various environmental conditions, along with their reusability through green recycling methods. Additionally, the intelligent design and application of SFITS are explored by incorporating Internet of Things (IoT) and artificial intelligence (AI) technologies. This integration enables real-time touch sensing, handwriting recognition, and advanced human-computer interactions. The versatility of SFITS is further exemplified through applications in remote control systems, molecular model generation, and virtual reality interfaces. The findings highlight the potential of sustainable ionic conductors in next-generation flexible electronics, offering a path toward greener and more intelligent device designs. Fibroin ; Electronics ; Materials science ; Nanotechnology ; Flexible electronics ; Ionic liquid ; Bioelectronics ; Computer science ; SILK ; Engineering ; Electrical engineering ; Composite material ; Biosensor Silk-based biomaterials and applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Qiangqiang Gu
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-11-25)
Field: Materials Science — Materials Chemistry

Neural network representation of electronic structure from <em>ab initio</em> molecular dynamics Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. Here we introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials. This predictive representation of ab initio electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling. When it is applied to a one-dimension charge-density wave material, carbyne, we are able to compute the spectral function and optical conductivity in the canonical ensemble. The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born–Oppenheimer limit. The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties, paving the way to previously inaccessible or challenging avenues in materials modeling. Representation (politics) ; Ab initio ; Electronic structure ; Artificial neural network ; Dynamics (music) ; SIESTA (computer program) ; Computational chemistry ; Computer science ; Statistical physics ; Ab initio quantum chemistry methods ; Cognitive science ; Artificial intelligence ; Physics ; Chemistry ; Psychology ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Souvik Manna | A. Das | Sandeep Das | Biswarup Pathak
Source: ACS Materials Letters (article, 2024-01-10)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Screening of MXene with Superior Anchoring Effect in Al–S Batteries Dissolution of polysulfide intermediates into electrolytes has been a major bottleneck in the development of the Al–S battery. MXenes can be promising anchoring materials, even though finding the most suitable candidates from a vast search space in a short span of time is challenging. Herein, a combined density functional theory and machine learning (ML) approach has been implemented to predict suitable M1M2XT2-type MXene materials that can optimally anchor the polysulfide intermediates. By employing various ML algorithms, the trained XGBR model is found to exhibit remarkable precision in predicting the anchoring effect of MXenes. 42 promising candidates have been identified to show optimum anchoring across the Al–S intermediates. The F and O terminal groups are found to majorly exhibit the optimum anchoring effect toward the possible polysulfide intermediates. This work provides crucial insights into the development of next-generation Al–S batteries accelerated by the ML approach, contributing to the advancement of energy storage technologies. Anchoring ; Polysulfide ; MXenes ; Bottleneck ; Battery (electricity) ; Electrolyte ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Engineering ; Embedded system MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Advanced Battery Materials and Technologies
Authors: Shingo Urata | Marco Bertani | Alfonso Pedone
Source: Journal of the American Ceramic Society (review, 2024-06-09)
Field: Materials Science — Materials Chemistry

Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review Abstract The emergence of artificial intelligence has provided efficient methodologies to pursue innovative findings in material science. Over the past two decades, machine‐learning potential (MLP) has emerged as an alternative technology to density functional theory (DFT) and classical molecular dynamics (CMD) simulations for computational modeling of materials and estimation of their properties. The MLP offers more efficient computation compared to DFT, while providing higher accuracy compared to CMD. This enables us to conduct more realistic simulations using models with more atoms and for longer simulation times. Indeed, the number of research studies utilizing MLPs has significantly increased since 2015, covering a broad range of materials and their structures, ranging from simple to complex, as well as various chemical and physical phenomena. As a result, there are high expectations for further applications of MLPs in the field of material science and industrial development. This review aims to summarize the applications, particularly in ceramics and glass science, and fundamental theories of MLPs to facilitate future progress and utilization. Finally, we provide a summary and discuss perspectives on the next challenges in the development and application of MLPs. Computer science ; Field (mathematics) ; Interatomic potential ; Range (aeronautics) ; Molecular dynamics ; Artificial intelligence ; Nanotechnology ; Machine learning ; Biochemical engineering ; Industrial engineering ; Materials science ; Chemistry ; Engineering ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Omri Abarbanel | Geoffrey Hutchison
Source: Journal of Chemical Theory and Computation (article, 2024-06-04)
Field: Materials Science — Materials Chemistry

QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-p <i>K</i> <sub>a</sub> Predictions Accurate prediction of micro-p<i>K</i><sub>a</sub> values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-p<i>K</i><sub>a</sub> prediction. QupKake outperforms state-of-the-art models on a variety of benchmark data sets, with root-mean-square errors between 0.5 and 0.8 p<i>K</i><sub>a</sub> units on five external test sets. Feature importance analysis reveals the crucial role of QM features in both the reaction site enumeration and micro-p<i>K</i><sub>a</sub> prediction models. QupKake represents a significant advancement in micro-p<i>K</i><sub>a</sub> prediction, offering a powerful tool for various applications in chemistry and beyond. Quantum chemistry ; Computer science ; Quantum chemical ; Quantum ; Chemistry ; Machine learning ; Physical chemistry ; Physics ; Quantum mechanics ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Tigany Zarrouk | Rina Ibragimova | Albert P. Bartók | Miguel A. Caro
Source: Journal of the American Chemical Society (preprint, 2024-05-15)
Field: Materials Science — Materials Chemistry

Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo structure optimization and selecting the one with the closest match to experiment. However, this inefficient process is not guaranteed to succeed. We introduce a general method to combine atomistic machine learning (ML) with experimental observables that produces atomistic structures compatible with experiment <i>by design</i>. We use this approach in combination with grand-canonical Monte Carlo within a modified Hamiltonian formalism, to generate configurations that agree with experimental data and are chemically sound (low in energy). We apply our approach to understand the atomistic structure of oxygenated amorphous carbon (a-CO<sub><i>x</i></sub>), an intriguing carbon-based material, to answer the question of how much oxygen can be added to carbon before it fully decomposes into CO and CO<sub>2</sub>. Utilizing an ML-based X-ray photoelectron spectroscopy (XPS) model trained from <i>GW</i> and density functional theory (DFT) data, in conjunction with an ML interatomic potential, we identify a-CO<sub><i>x</i></sub> structures compliant with experimental XPS predictions that are also energetically favorable with respect to DFT. Employing a network analysis, we accurately deconvolve the XPS spectrum into motif contributions, both revealing the inaccuracies inherent to experimental XPS interpretation and granting us atomistic insight into the structure of a-CO<sub><i>x</i></sub>. This method generalizes to multiple experimental observables and allows for the elucidation of the atomistic structure of materials directly from experimental data, thereby enabling experiment-driven materials modeling with a degree of realism previously out of reach. X-ray photoelectron spectroscopy ; Monte Carlo method ; Density functional theory ; Statistical physics ; Observable ; Electronic structure ; Molecular dynamics ; Materials science ; Amorphous solid ; Chemistry ; Computational chemistry ; Physics ; Crystallography ; Mathematics ; Nuclear magnetic resonance ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electron and X-Ray Spectroscopy Techniques
Authors: Marvin Alberts | Teodoro Laino | Alain C. Vaucher
Source: Communications Chemistry (article, 2024-11-16)
Field: Chemistry — Analytical Chemistry

Leveraging infrared spectroscopy for automated structure elucidation The application of machine learning models in chemistry has made remarkable strides in recent years. While analytical chemistry has received considerable interest from machine learning practitioners, its adoption into everyday use remains limited. Among the available analytical methods, Infrared (IR) spectroscopy stands out in terms of affordability, simplicity, and accessibility. However, its use has been limited to the identification of a selected few functional groups, as most peaks lie beyond human interpretation. We present a transformer model that enables chemists to leverage the complete information contained within an IR spectrum to directly predict the molecular structure. To cover a large chemical space, we pretrain the model using 634,585 simulated IR spectra and fine-tune it on 3,453 experimental spectra. Our approach achieves a top-1 accuracy of 44.4% and top-10 accuracy of 69.8% on compounds containing 6 to 13 heavy atoms. When solely predicting scaffolds, the model accurately predicts the top-1 scaffold in 84.5% and among the top-10 in 93.0% of cases. Infrared spectroscopy ; Infrared ; Spectroscopy ; Materials science ; Computer science ; Chemistry ; Physics ; Optics ; Organic chemistry ; Astronomy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Andrei Agafonov | Nayely Pineda-Romero | Matthew Witman | Vivian Nassif | G. Vaughan | Lei Lei | Sanliang Ling | David M. Grant | Martin Dornheim | Mark D. Allendorf | Vitalie Stavila | Claudia Zlotea
Source: Acta Materialia (article, 2024-06-07)
Field: Materials Science — Materials Chemistry

Destabilizing high-capacity high entropy hydrides via earth abundant substitutions: From predictions to experimental validation Hydrogen storage ; Intermetallic ; Materials science ; High entropy alloys ; Laves phase ; Alloy ; Hydride ; Density functional theory ; Thermodynamics ; Computational chemistry ; Metallurgy ; Chemistry ; Physics Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Metal and Thin Film Mechanics
Authors: Tong Liu | Zhuoyao Chen | Jingzhi Yang | Lingwei Ma | J.M.C. Mol | Dawei Zhang
Source: npj Materials Degradation (article, 2024-01-19)
Field: Materials Science — Materials Chemistry

Machine learning assisted discovery of high-efficiency self-healing epoxy coating for corrosion protection Abstract Machine learning is a powerful means for the rapid development of high-performance functional materials. In this study, we presented a machine learning workflow for predicting the corrosion resistance of a self-healing epoxy coating containing ZIF-8@Ca microfillers. The orthogonal Latin square method was used to investigate the effects of the molecular weight of the polyetheramine curing agent, molar ratio of polyetheramine to epoxy, molar content of the hydrogen bond unit (UPy-D400), and mass content of the solid microfillers (ZIF-8@Ca microfillers) on the low impedance modulus (lg| Z | 0.01Hz ) values of the scratched coatings, generating 32 initial datasets. The machine learning workflow was divided into two stages: In stage I, five models were compared and the random forest (RF) model was selected for the active learning. After 5 cycles of active learning, the RF model achieved good prediction accuracy: coefficient of determination ( R 2 ) = 0.709, mean absolute percentage error (MAPE) = 0.081, root mean square error (RMSE) = 0.685 (lg(Ω·cm 2 )). In stage II, the best coating formulation was identified by Bayesian optimization. Finally, the electrochemical impedance spectroscopy (EIS) results showed that compared with the intact coating ((4.63 ± 2.08) × 10 11 Ω·cm 2 ), the | Z | 0.01Hz value of the repaired coating was as high as (4.40 ± 2.04) × 10 11 Ω·cm 2 . Besides, the repaired coating showed minimal corrosion and 3.3% of adhesion loss after 60 days of neutral salt spray testing. Coating ; Epoxy ; Materials science ; Corrosion ; Dielectric spectroscopy ; Composite material ; Mean absolute percentage error ; Mean squared error ; Chemistry ; Electrochemistry ; Mathematics Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Fuel Cells and Related Materials
Authors: Yuanzhe Hu | Guowei Zhou | Myoung‐Gyu Lee | Peidong Wu | Dayong Li
Source: International Journal of Plasticity (article, 2024-06-07)
Field: Materials Science — Materials Chemistry

A temporal graph neural network for cross-scale modelling of polycrystals considering microstructure interaction Materials science ; Microstructure ; Scale (ratio) ; Artificial neural network ; Artificial intelligence ; Composite material ; Computer science Machine Learning in Materials Science ; Material Properties and Failure Mechanisms ; Natural Language Processing Techniques
Authors: Benjamin Schröder | Guntram Rauhut
Source: The Journal of Physical Chemistry Letters (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

From the Automated Calculation of Potential Energy Surfaces to Accurate Infrared Spectra Advances in the development of quantum chemical methods and progress in multicore architectures in computer science made the simulation of infrared spectra of isolated molecules competitive with respect to established experimental methods. Although it is mainly the multidimensional potential energy surface that controls the accuracy of these calculations, the subsequent vibrational structure calculations need to be carefully converged in order to yield accurate results. As both aspects need to be considered in a balanced way, we focus on approaches for molecules of up to 12-15 atoms with respect to both parts, which have been automated to some extent so that they can be employed in routine applications. Alternatives to machine learning will be discussed, which appear to be attractive, as long as local regions of the potential energy surface are sufficient. The automatization of these methods is still in its infancy, and the generalization to molecules with large amplitude motions or molecular clusters is far from trivial, but many systems relevant for astrophysical studies are already in reach. Infrared ; Spectral line ; Energy (signal processing) ; Infrared spectroscopy ; Potential energy ; Remote sensing ; Computer science ; Optics ; Physics ; Geology ; Atomic physics ; Astronomy ; Quantum mechanics Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Calibration and Measurement Techniques