jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tianyu Zhang | Yuxiang Ren | Chengbin Hou | Hairong Lv | Xuegong Zhang
Source:  (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small Models Molecular property prediction is a crucial foundation for drug discovery. In recent years, pre-trained deep learning models have been widely applied to this task. Some approaches that incorporate prior biological domain knowledge into the pre-training framework have achieved impressive results. However, these methods heavily rely on biochemical experts, and retrieving and summarizing vast amounts of domain knowledge literature is both time-consuming and expensive. Large Language Models (LLMs) have demonstrated remarkable performance in understanding and efficiently providing general knowledge. Nevertheless, they occasionally exhibit hallucinations and lack precision in generating domain-specific knowledge. Conversely, Domain-specific Small Models (DSMs) possess rich domain knowledge and can accurately calculate molecular domain-related metrics. However, due to their limited model size and singular functionality, they lack the breadth of knowledge necessary for comprehensive representation learning. To leverage the advantages of both approaches in molecular property prediction, we propose a novel Molecular Graph representation learning framework that integrates Large language models and Domain-specific small models (MolGraph-LarDo). Technically, we design a two-stage prompt strategy where DSMs are introduced to calibrate the knowledge provided by LLMs, enhancing the accuracy of domain-specific information and thus enabling LLMs to generate more precise textual descriptions for molecular samples. Subsequently, we employ a multi-modal alignment method to coordinate various modalities, including molecular graphs and their corresponding descriptive texts, to guide the pre-training of molecular representations. Extensive experiments demonstrate the effectiveness of the proposed method. Computer science ; Graph ; Representation (politics) ; Theoretical computer science ; Domain (mathematical analysis) ; Artificial intelligence ; Natural language processing ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Genetics, Bioinformatics, and Biomedical Research
Authors: Yoyo Hinuma
Source: Computational Materials Science (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Effect of very slow O diffusion at high temperature on very fast H diffusion in the hydride ion conductor LaH2.75O0.125 Diffusion ; Hydride ; Ion ; Conductor ; Chemistry ; Materials science ; Analytical Chemistry (journal) ; Inorganic chemistry ; Thermodynamics ; Hydrogen ; Composite material ; Physics ; Organic chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Hydrogen Storage and Materials
Authors: Hadiyawarman Tukiman | Demas Aji | Gerald Ensang Timuda | Nono Darsono | Sri Rahayu | Abdul Hamid Budiman | Deni Shidqi Khaerudini
Source: Clean Technologies and Environmental Policy (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Bibliometric review of carbonaceous-based sorbent for hydrogen storage Sorbent ; Industrial and production engineering ; Hydrogen storage ; Sustainable development ; Environmental science ; Waste management ; Hydrogen ; Chemistry ; Chemical engineering ; Engineering ; Organic chemistry ; Adsorption ; Political science ; Mechanical engineering Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advanced Battery Materials and Technologies
Authors: Jeffrey S. S. K. Formen | James R. Howard | Eric V. Anslyn | Christian Wolf
Source: Angewandte Chemie (article, 2024-02-29)
Field: Chemistry — Spectroscopy

Circular Dichroism Sensing: Strategies and Applications Abstract The analysis of the absolute configuration, enantiomeric composition, and concentration of chiral compounds are frequently encountered tasks across the chemical and health sciences. Chiroptical sensing methods can streamline this work and allow high‐throughput screening with remarkable reduction of operational time and cost. During the last few years, significant methodological advances with innovative chirality sensing systems, the use of computer‐generated calibration curves, machine learning assistance, and chemometric data processing, to name a few, have emerged and are now matched with commercially available multi‐well plate CD readers. These developments have reframed the chirality sensing space and provide new opportunities that are of interest to a large group of chemists. This review will discuss chirality sensing strategies and applications with representative small‐molecule CD sensors. Emphasis will be given to important milestones and recent advances that accelerate chiral compound analysis by outperforming traditional methods, conquer new directions, and pioneering efforts that lie at the forefront of chiroptical high‐throughput screening developments. The goal is to provide the reader with a thorough understanding of the current state and a perspective of future directions of this rapidly emerging field. Chirality (physics) ; Computer science ; Nanotechnology ; Perspective (graphical) ; Circular dichroism ; Enantiomer ; Throughput ; Chemistry ; Artificial intelligence ; Physics ; Materials science ; Telecommunications ; Organic chemistry Molecular spectroscopy and chirality ; Molecular Sensors and Ion Detection ; Analytical Chemistry and Chromatography
Authors: Juhyung Lee | Woonghee Lee
Source: Journal of Raman Spectroscopy (article, 2024-01-08)
Field: Chemistry — Analytical Chemistry

Peak‐aware adaptive denoising for Raman spectroscopy based on machine learning approach Abstract Raman spectroscopy can be effectively used for detection and analysis of chemical agents that are serious threats in modern warfare, but the detection and analysis performance is prone to deterioration due to noise. The existing denoising technique has limitations that there is no criterion for selecting the window length and that the filtering distorts the peaks, key features for Raman spectral data analysis. To overcome such limitations, in this paper, we propose the peak‐aware adaptive denoising for Raman spectroscopy based on machine learning approach. The proposed technique utilizes the information of detected peaks to eliminate noise effectively using different window values optimal for each region in the Raman spectrum while preserving the shape of peaks. We conducted the various analyses and experiments, and the proposed technique showed a 28% lower Euclidean distance and a 48% lower Fréchet inception distance compared to the existing technique, meaning the proposed technique outperformed the existing one. Raman spectroscopy ; Noise reduction ; Noise (video) ; Euclidean distance ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Feature (linguistics) ; Biological system ; Optics ; Physics ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Rundong Liao | Ke Xu | Yifan Liu | Z. Gao | Shuo Jin | Linyun Liang | Guang-Hong Lü
Source: Nuclear Materials and Energy (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

Fast prediction of irradiation-induced cascade defects using denoising diffusion probabilistic model • A machine learning method was proposed to rapidly and accurately predict the spatial coordinates of point defects at given primary knock atom (PKA) energies in W. • The computational scheme has the capability to generate the cascade collision defects across a broad spectrum of PKA energies, spanning from 0 to 100.0 keV. • The trained DDPM only needs 0.06 s to generate the cascade collision point defects at a specific PKA energy, which is at least five orders of magnitude faster than the MD simulation time. Irradiation-induced cascade collisions produce numerous point defects within materials, which can severely deteriorate their thermo-mechanical properties and overall performance. We propose a computational scheme that combines molecular dynamic (MD) simulations with a denoising diffusion probabilistic model (DDPM) to rapidly and accurately predict the spatial coordinates of point defects at any given primary knock atom (PKA) energy, ranging from 0 to 100.0 keV. Importantly, this capability extends to PKA energies that are exclusive from the training data set, demonstrating the robustness and generalizability of the model. The proposed scheme has been thoroughly validated by several designed indicators, including the Fréchet inception distance, the number of point defects, the distance from vacancies and self-interstitial atoms (SIAs) to their respective centroids, the inter-centroid distance between the vacancies and SIAs, the probability density of clustered defect sizes, and the sub-cascade number. Compared to MD simulations, the DDPM can generate point defects at a specific PKA energy at least ten thousand times faster. By offering a rapid and reliable means to model defect distributions across various energy levels, the proposed scheme benefits the comprehension of the cascade process and provides a valuable database for both experimental investigations and large-scale simulations. Cascade ; Irradiation ; Diffusion ; Probabilistic logic ; Materials science ; Nuclear engineering ; Statistical physics ; Radiochemistry ; Biological system ; Physics ; Computer science ; Thermodynamics ; Nuclear physics ; Chemistry ; Artificial intelligence ; Engineering ; Chromatography Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear reactor physics and engineering
Authors: Zhanpeng Gong | Jefferson Zhe Liu | Xiangdong Ding | Jun Sun | Junkai Deng
Source: Physical review. B./Physical review. B (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Strain-induced two-dimensional relaxor ferroelectrics in Se-doped PbTe Triggered by external fields, typical ferroelectrics (FEs) undergo a sharp phase transition from the nonpolarized paraelectric (PE) phase, which induces the emergence of spontaneous electric polarization. It is widely accepted that by introducing a large number of point defects, conventional FEs can be transformed into relaxor FEs, which exhibit unique physical properties, such as polar nanoregions (PNRs) and a corresponding gradual phase transition. In recent years, the discovery of two-dimensional (2D) FEs has attracted significant interest driven by the increasing demand for miniaturized nanoelectronics. Despite the identification of various 2D FE materials, there has been no report on 2D relaxor FEs so far. In this study, we explore the strain-induced FE phase transitions in a Se-doped PbTe monolayer through molecular dynamics simulations utilizing a developed deep-learning potential. Our findings reveal the gradual emergence of PNRs during the strain-induced FE phase transition of $\mathrm{P}{\mathrm{b}}_{50}\mathrm{T}{\mathrm{e}}_{50\ensuremath{-}x}\mathrm{S}{\mathrm{e}}_{x}$ ($x\ensuremath{\ge}4$). Additionally, we showcase the dynamic nature of the PNRs through a comparison between the instantaneous and time-averaged dynamic pair distribution functions. Furthermore, we demonstrate that the evolution of the PNRs' size and percolation strength with increasing strain can be well fitted by power laws derived from the percolation theory, which is a key characteristic of the relaxor-to-FE phase transition. Finally, a strain-temperature PE-relaxor-FE phase diagram for $\mathrm{P}{\mathrm{b}}_{50}\mathrm{T}{\mathrm{e}}_{46}\mathrm{S}{\mathrm{e}}_{4}$ is presented, which provides valuable insights for potential applications involving 2D Se-doped PbTe. Condensed matter physics ; Materials science ; Phase diagram ; Percolation (cognitive psychology) ; Phase transition ; Phase (matter) ; Physics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Multiferroics and related materials
Authors: Shengluo Ma | Yongchao Rao | Xiang Huang | Shenghong Ju
Source: Materials Today Physics (article, 2024-05-09)
Field: Materials Science — Materials Chemistry

High-throughput discovery of metal oxides with high thermoelectric performance via interpretable feature engineering on small data Materials science ; Throughput ; Feature (linguistics) ; Thermoelectric effect ; Nanotechnology ; Computer science Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Machine Learning and ELM
Authors: Nisha Kumari | Saroj Kumar Sarangi
Source: Physica Scripta (article, 2024-09-27)
Field: Materials Science — Materials Chemistry

Efficient prediction of mechanical properties of Boron Nitride Nanosheets using artificial neural networks Abstract This paper aims to evaluate the mechanical properties of Boron Nitride Nanosheets (BNNS) and their vital use in nano-electromechanical systems (NEMS). By employing molecular Dynamics (MD) simulation, modelling of the atomic structure was done. The mechanical response of BNNS under various parameters (strain rate, temperature, chirality and dimension) enabled the generation of a comprehensive data set that accurately represents their elastic properties. The dataset obtained from MD simulation was subsequently utilized to construct an artificial neural network (ANN) model, tailored to predict the Young’s modulus of BNNS accurately. This work aimed to improve the model’s efficiency by refining the design of ANN, which significantly reduces the computational time while maintaining higher accuracy predictions. The findings demonstrate precise and rapid prediction for developing components based on BNNS in NEMS. This paper establishes an analogy between in-depth atomistic simulations and real-world engineering applications presenting a new approach for precisely predicting the attributes of nanomaterials. Boron nitride ; Materials science ; Artificial neural network ; Boron ; Biological system ; Nanotechnology ; Artificial intelligence ; Computer science ; Physics Boron and Carbon Nanomaterials Research ; Diamond and Carbon-based Materials Research ; Machine Learning in Materials Science
Authors: Ibrahim Bel-Hadj | Mohammed Guerboub | Achille Lambrecht | Guido Ori | Carlo Massobrio | Évelyne Martin
Source: Journal of Physics D Applied Physics (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Thermal conductivity of crystalline Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub>: lattice contribution and size effects in the cubic phase quantified by approach-to-equilibrium molecular dynamics Abstract Approach-to-equilibrium molecular dynamics simulations are carried out for the cubic phase of crystalline Ge 2 Sb 2 Te 5 , using interatomic forces derived from a machine learning interatomic potential (MLIP) trained with ab initio calculations. The use of this MLIP potential significantly reduces the computational burden, allowing for the study of systems over 70 nm in length. Above 20 nm, the thermal conductivity of the lattice plateaus at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mn>0.37</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.01</mml:mn> </mml:mrow> </mml:math> W K −1 m −1 in agreement with measurements reported in the literature. However, below 20 nm, size effects lead to a reduction in thermal conductivity which is systematically calculated. Interatomic potential ; Thermal conductivity ; Molecular dynamics ; Lattice (music) ; Materials science ; Ab initio quantum chemistry methods ; Ab initio ; Conductivity ; Thermodynamics ; Condensed matter physics ; Molecular physics ; Chemical physics ; Chemistry ; Computational chemistry ; Physical chemistry ; Molecule ; Physics Phase-change materials and chalcogenides ; Advanced Thermoelectric Materials and Devices ; thermodynamics and calorimetric analyses
Authors: Matthew Frichtl | Sreeramamurthy Ankem
Source: Materials Today Communications (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

An improved method for the quantification of microstructures via optical microscopy Materials science ; Optical microscope ; Microstructure ; Sample (material) ; Segmentation ; Thresholding ; Biological system ; Phase (matter) ; Microscopy ; Artificial intelligence ; Computer science ; Optics ; Image (mathematics) ; Scanning electron microscope ; Composite material ; Chromatography Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Rohan Yuri Sanspeur | John R. Kitchin
Source: Machine Learning Science and Technology (article, 2024-01-30)
Field: Materials Science — Materials Chemistry

Circumventing data imbalance in magnetic ground state data for magnetic moment predictions Abstract Magnetic materials play a crucial role in the transition to more sustainable forms of energy and electric vehicles. There is an anticipated shortage in magnetic materials in the future, and as a result there is an urgent need to discover and design new magnetic materials. Computational magnetic material design using density functional theory is daunting because of the challenge in identifying magnetic ground states from a combinatorially large set of possibilities. Machine learning offers a path forward by enabling efficient surrogate models that can more readily enumerate these states, but there is a dearth of training data available, and what is available tends to be imbalanced with too much non-magnetic data. In this work we show that the discrete and previously tackled data imbalance that exists at the level of the magnetic ordering leads to an imbalanced continuous distribution with many zeros when the data is unraveled at the atomic magnetic moment level, which subsequently leads to models with low accuracy for magnetic properties. We mitigate this by using a two-part model framework. Our scheme is able to classify atoms into magnetic and non-magnetic with an F1 score and Matthew’s correlation coefficient (MCC) of ~91% and then to provide an implicit embedding representation that maps directly onto the magnitude of the magnetic moment with a mean absolute error of 0.1 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>μ</mml:mi> <mml:mrow> <mml:mtext>B</mml:mtext> </mml:mrow> </mml:msub> </mml:math> . Beyond screening for new magnetic materials, we demonstrate an additional practical use case of our scheme: the provision of good initial guesses for magnetic moments in first-principles electronic relaxations. Such initialization is shown to lead to faster convergence to configurations that lie closer to the ground state. Magnetic moment ; Computer science ; Algorithm ; Moment (physics) ; Ground state ; Artificial intelligence ; Statistical physics ; Physics ; Machine learning ; Condensed matter physics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Yonglin Xia | X. Zhang | Ao Wang | Yufei Sheng | Han Xie | Hua Bao
Source: Journal of Physics Condensed Matter (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

Critical factors influencing electron and phonon thermal conductivity in metallic materials using first-principles calculations Understanding the thermal transport of various metals is crucial for many energy-transfer applications. However, due to the complex transport mechanisms varying among different metals, current research on metallic thermal transport has been focusing on case studies of specific types of metallic materials. A general understanding of the transport mechanisms across a broad spectrum of metallic materials is still lacking. In this work, we perform first-principles calculations to determine the thermal conductivity of 40 representative metallic materials, within a range of 8-456 W mK<sup>-1</sup>. Our predicted values of electrical and thermal conductivity are in good agreement with available experimental results. Based on the data of separated electron and phonon thermal conductivity, we employ a statistical approach to examine nine factors derived from previous understandings and identify the critical factors determining these properties. For electrons, although a high electron density of states around the Fermi level implies more conductive electrons, we find it counterintuitively correlates with low electron thermal conductivity. This is attributed to the enlarged electron-phonon scattering channels induced by substantial electrons around the Fermi level. Regarding phonons, we demonstrate that among all the studied factors, Debye temperature plays the most significant role in determining the phonon thermal conductivity, despite the phonon-electron scattering being non-negligible in some transition metals. Correlation analysis suggests that Debye temperature has the highest positive correlation coefficient with phonon thermal conductivity, as it corresponds to a large phonon group velocity. Additionally, Young's modulus is found to be closely correlated with high phonon thermal conductivity and contribution. Our findings of simple factors that closely correlate with the electron and phonon thermal conductivity provide a general understanding of various metallic materials. They may facilitate the discovery of novel materials with extremely high or low thermal conductivity, or be used as descriptors in machine learning to accurately predict the thermal conductivity of metals in the future. Thermal conductivity ; Phonon ; Condensed matter physics ; Debye model ; Phonon scattering ; Electron ; Thermal conduction ; Scattering ; Materials science ; Debye ; Fermi level ; Physics Thermal properties of materials ; Machine Learning in Materials Science ; Surface and Thin Film Phenomena