jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenfei Wang | Lulu Zhang | Chengzhi Cai | Shuhua Li | Hong Liang | Yufang Wu | Zheng He | Zhiwei Qiao
Source: Separation and Purification Technology (article, 2023-07-20)
Field: Chemistry — Inorganic Chemistry

Machine learning assisted high-throughput computational screening of MOFs for the capture of chemical warfare agents from the air Adsorption ; Nanoporous ; Trimesic acid ; Metal-organic framework ; Porosity ; Hydrogen bond ; Hydrogen sulfide ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Molecule ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; MXene and MAX Phase Materials
Authors: Kai Pan | Hui Liu | Zhijun Wang | Wenjing Ji | Jianhai Wang | Rui Huang | Ze Wei | Dong Ye | Chang Xu | Haining Wang
Source: Safety (article, 2023-07-21)
Field: Materials Science — Polymers and Plastics

Insights into Ionic Liquids for Flame Retardant: A Study Based on Bibliometric Mapping Fire is a typical disaster in the processing industry. Ionic liquids, as a type of green flame retardant, play an important role in process safety. In order to grasp the current research status, hotspots, and frontiers in the field of ionic liquids in flame retardancy, the bibliometric mapping method is applied to study the relevant literature in Web of Science datasets from 2000–2022 in this paper. The results show that the research on ionic liquids in flame retardancy is multidisciplinary and involves some disciplines such as energy science, material science, and environmental protection. Journal of Power Sources, Polymer Degradation and Stability, ACS Applied Materials and Interfaces, and Chemical Engineering Journal are the core journals in the field. The results of keyword co-occurrence indicate that the hotspots of research can be divided into five components: the improvement and application of pure ionic liquids electrolytes, the research of gel polymer electrolytes, applying ionic liquids to enhance the polymer materials’ flame retardancy properties, utilizing ionic liquids and inorganic materials to synergize flame retardant polymers, and using ionic liquids flame retardant to improve material’s multiple properties. The burst terms and time zone diagram’s results point out the combination of computational quantum chemistry to study the flame retardancy mechanism of ionic liquids, the study of fluorinated electrolytes, ionic liquids for smoke suppression, phosphorus-containing ionic liquids for flame retardant, and machine learning-assisted design of ILs flame retardants are the research frontiers and future research trends. Ionic liquid ; Fire retardant ; Materials science ; Ionic bonding ; Electrolyte ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material ; Ion ; Engineering ; Physical chemistry Flame retardant materials and properties
Authors: Hassan Masood | Tharmakulasingam Sirojan | Cui Ying Toe | Priyank V. Kumar | Yousof Haghshenas | Patrick H.‐L. Sit | Rose Amal | Vidhyasaharan Sethu | Wey Yang Teoh
Source: Cell Reports Physical Science (article, 2023-08-25)
Field: Materials Science — Materials Chemistry

Enhancing prediction accuracy of physical band gaps in semiconductor materials Accurate band-gap prediction is essential for designing and discovering new materials with desired properties. However, current methods for calculating band gaps based on local and semilocal functionals lead to significant underestimation, hindering the effectiveness of in silico and high-throughput screening of materials. We present a machine learning model with domain adaptation to rapidly yield accurate band-gap prediction of semiconductors (oxides, chalcogenides, nitrides, phosphides, etc.). The approach circumvents the prerequisite for a large amount of physically measured band-gap data, which is notoriously scarce. It instead sources knowledge from a large dataset with underestimated band gaps and subsequently transfers knowledge to train a crystal graph convolution neural network (CGCNN) using a small dataset of accurate, physically measured band gaps. The prediction model shows a low mean absolute error (MAE) of 0.23 eV, outperforming those using Perdew-Burke-Ernzerhof (PBE) functionals (MAE = 0.87 eV). Visualization of the learned crystal graph using the t-distributed stochastic neighbor embedding (t-SNE) algorithm revealed that the crystal structure and composition have a strong influence on the material band gaps. Band gap ; Semiconductor ; Convolution (computer science) ; Computer science ; Graph ; Crystal (programming language) ; Machine learning ; Algorithm ; Materials science ; Artificial neural network ; Optoelectronics ; Theoretical computer science Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Electronic and Structural Properties of Oxides
Authors: David Buterez | Jon Paul Janet | Steven J. Kiddle | Dino Oglić | Píetro Lió
Source: Communications Chemistry (article, 2023-11-29)
Field: Materials Science — Materials Chemistry

Modelling local and general quantum mechanical properties with attention-based pooling Atom-centred neural networks represent the state-of-the-art for approximating the quantum chemical properties of molecules, such as internal energies. While the design of machine learning architectures that respect chemical principles has continued to advance, the final atom pooling operation that is necessary to convert from atomic to molecular representations in most models remains relatively undeveloped. The most common choices, sum and average pooling, compute molecular representations that are naturally a good fit for many physical properties, while satisfying properties such as permutation invariance which are desirable from a geometric deep learning perspective. However, there are growing concerns that such simplistic functions might have limited representational power, while also being suboptimal for physical properties that are highly localised or intensive. Based on recent advances in graph representation learning, we investigate the use of a learnable pooling function that leverages an attention mechanism to model interactions between atom representations. The proposed pooling operation is a drop-in replacement requiring no changes to any of the other architectural components. Using SchNet and DimeNet++ as starting models, we demonstrate consistent uplifts in performance compared to sum and mean pooling and a recent physics-aware pooling operation designed specifically for orbital energies, on several datasets, properties, and levels of theory, with up to 85% improvements depending on the specific task. Pooling ; Computer science ; Representation (politics) ; Function (biology) ; Artificial intelligence ; Theoretical computer science ; Atom (system on chip) ; Quantum ; Machine learning ; Physics ; Quantum mechanics ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Erfan Maleki | Okan Ünal | Seyed Mahmoud Seyedi Sahebari | Kazem Reza Kashyzadeh
Source: Materials (article, 2023-06-29)
Field: Materials Science — Materials Chemistry

A Novel Approach for Analyzing the Effects of Almen Intensity on the Residual Stress and Hardness of Shot-Peened (TiB + TiC)/Ti–6Al–4V Composite: Deep Learning In the present study, the experimental data of a shot-peened (TiB + TiC)/Ti-6Al-4V composite with two volume fractions of 5 and 8% for TiB + TiC reinforcements were used to develop a neural network based on the deep learning technique. In this regard, the distributions of hardness and residual stresses through the depth of the materials as the properties affected by shot peening (SP) treatment were modeled via the deep neural network. The values of the TiB + TiC content, Almen intensity, and depth from the surface were considered as the inputs, and the corresponding measured values of the residual stresses and hardness were regarded as the outputs. In addition, the surface coverage parameter was assumed to be constant in all samples, and only changes in the Almen intensity were considered as the SP process parameter. Using the presented deep neural network (DNN) model, the distributions of hardness and residual stress from the top surface to the core material were continuously evaluated for different combinations of input parameters, including the Almen intensity of the SP process and the volume fractions of the composite reinforcements. Residual stress ; Materials science ; Peening ; Composite number ; Shot peening ; Shot (pellet) ; Composite material ; Intensity (physics) ; Indentation hardness ; Metallurgy ; Microstructure ; Optics Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Surface Treatment and Residual Stress
Authors: Zeyu Xiang | Yu Pang | Xin Qian | Ronggui Yang
Source: Applied Physics Letters (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Machine learning reconstruction of depth-dependent thermal conductivity profile from pump–probe thermoreflectance signals Characterizing spatially varying thermal conductivities is significant to unveil the structure–property relation for a wide range of thermal functional materials such as chemical-vapor-deposited (CVD) diamonds, ion-irradiated materials, nuclear materials under radiation, and battery electrode materials. Although the development of thermal property microscopy based on time/frequency-domain thermoreflectance (TDTR/FDTR) enabled in-plane scanning of a thermal conductivity profile, measuring depth-dependent profiles remains challenging. This work proposed a machine-learning-based reconstruction method for extracting depth-dependent thermal conductivity K ( z ) directly from pump–probe phase signals. We demonstrated that the simple supervised-learning algorithm kernel ridge regression can reconstruct K ( z ) without requiring pre-knowledge about the functional form of the profile. The reconstruction method can not only accurately reproduce typical K ( z ) distributions such as the exponential profile of CVD diamonds and the Gaussian profile of ion-irradiated materials but also complex profiles artificially constructed by superimposing Gaussian, exponential, polynomial, and logarithmic functions. In addition to FDTR, the method also shows excellent performance of reconstructing K ( z ) of ion-irradiated semiconductors from TDTR signals. This work demonstrates that combining machine learning with pump–probe thermoreflectance is an effective way for depth-dependent thermal property mapping. Materials science ; Thermal conductivity ; Gaussian ; Thermal ; Irradiation ; Gaussian function ; Optoelectronics ; Optics ; Composite material ; Chemistry ; Physics ; Thermodynamics Thermal properties of materials ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Deting Xu | Wenyan Yin | Jie Zhou | Liyuan Wu | Haodong Yao | Minghui Sun | Ping Chen | Xiangwen Deng | Lina Zhao
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Rational design of MoS<sub>2</sub>-supported Cu single-atom catalysts by machine learning potential for enhanced peroxidase-like activity Two-dimensional molybdenum disulfide (2D-MoS<sub>2</sub>)-supported single atom nanomaterials with enhanced enzyme-like activities are potential substitutes for natural enzymes due to their huge specific surface areas, ease of decoration, high catalytic activity and high catalytic stability. However, their catalytic mechanism remains unclear, making the rational design of nanozymes difficult to achieve. Herein, the mechanisms have been explored to enhance the peroxidase-like activity of MoS<sub>2</sub> for H<sub>2</sub>O<sub>2</sub> decomposition. Global neutral network (G-NN) potentials were constructed to accurately and quickly illustrate the mechanisms of MoS<sub>2</sub> catalysts and their surface modifications. The high peroxidase-like activity of the MoS<sub>2</sub>-supported Cu single atom catalyst with sulfur vacancy (Cu@MoS<sub>2</sub>-Vs) in acidic conditions was systematically evaluated using the trained G-NN potential and density functional theory (DFT), as well as experimental validation. Further analysis of the geometric and electronic properties of pivotal stationary structures revealed the enhanced electron transfer process for high catalytic performance with the modulation of the Cu single atom loading, sulfur vacancy engineering and the surrounding acidic and alkaline environment regulation on the MoS<sub>2</sub> basal plane. The results also showed that Cu@MoS<sub>2</sub>-Vs in an acidic environment exhibited the highest peroxidase-like activity. This work is expected to provide broad implications for the rational design of substrate-supported single-atom catalysts with superior performance and lower cost by surface modification and acidic and alkaline environment regulation. Peroxidase ; Rational design ; Catalysis ; Atom (system on chip) ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Computational chemistry ; Materials science ; Computer science ; Enzyme ; Biochemistry ; Embedded system Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Nanocluster Synthesis and Applications
Authors: Yea‐Lee Lee | Hyungseok Lee | Seunghun Jang | Jungho Shin | Taeshik Kim | Sejin Byun | In Jae Chung | Jino Im | Hyunju Chang
Source: APL Materials (article, 2023-04-01)
Field: Materials Science — Materials Chemistry

TEXplorer.org: Thermoelectric material properties data platform for experimental and first-principles calculation results Scientific databases offer remarkable potential for solving complex questions in materials science, such as global optimization of materials and designing unknown materials for novel properties. ThermoElectric materials eXplorer (TEXplorer) is a web-based platform designed to collect and share all types of thermoelectric materials data, including synthesis information, materials characterization, transport measurements, and electronic structures obtained from experiments and computations. TEXplorer also provides valuable tools, such as an easy upload and download system, retrieval, automatic post-processing calculations, visualization of datasets, and toolkits for predicting thermoelectric properties through machine learning models. Using the platform, we collected and managed the thermoelectric dataset of SnSe and Bi2Te3 with various doping/alloying elements in this study in order to investigate the complex relationship between doping/alloying elements and the thermoelectric properties of host materials. The web-based interactive data platform enables efficient management and utilization of experimental and computational datasets, supporting the acceleration of data-driven materials research and autonomous material synthesis. Thermoelectric effect ; Thermoelectric materials ; Upload ; Materials science ; Visualization ; Computer science ; Characterization (materials science) ; Nanotechnology ; Data mining ; World Wide Web Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Aurelio Bifulco | Angelo Casciello | Claudio Imparato | Stanislao Forte | Sabyasachi Gaan | Antonio Aronne | Giulio Malucelli
Source: Polymer Testing (article, 2023-08-19)
Field: Materials Science — Polymers and Plastics

A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites In this work, the fire behavior of a sol-gel in-situ hybrid Mg(OH)2-epoxy nanocomposite was investigated and an artificial neural network-based system built on a fully connected feed-forward artificial neural network was developed to predict its heat release capacity. The nanocomposite containing only ∼5 wt% loading of Mg(OH)2 promoted a remarkable decrease in heat release capacity (∼34%) measured by pyrolysis combustion flow calorimetry and in peak of heat release rate (∼37%), and heat release rate (∼29%), as assessed by cone calorimetry, as well as a significant decrease of total smoke release and smoke extinction area about 22 and 5%, respectively, indicating the suitability of Mg(OH)2 as an effective smoke suppressant. The proposed machine learning approach may be used as a promising alternative for a cost- and time-saving prediction of the fire performances of novel flame retardant polymer-based nanocomposites. Fire retardant ; Materials science ; Nanocomposite ; Cone calorimeter ; Calorimetry ; Epoxy ; Smoke ; Chemical engineering ; Pyrolysis ; Composite material ; Heat capacity ; Organic chemistry ; Char ; Chemistry Flame retardant materials and properties ; Fire dynamics and safety research ; Polymer Nanocomposites and Properties
Authors: Jie Zhang | Chao Qian | Jieting Chen | Bei Wu | Hongsheng Chen
Source: Laser & Photonics Review (article, 2023-02-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Uncertainty Qualification for Metasurface Design with Amendatory Bayesian Network Abstract Having a prophetic ability to evaluate the uncertainty of deep learning is important to enable the critical reception of the output result. This is especially pronounced in the emerging domain of intelligent metasurfaces, due to the ubiquitous uncertainties from realistic fabrication and network modeling. Despite the great advancements that have mutated the design and working modality of metasurfaces, this enticing ability remains elusive. Here, a new paradigm to quantify the uncertainty in metasurface design is proposed by generalizing the Bayesian neural network. The uncertainty generally originates from the network model part and data part, the latter of which is imitated by the topologically‐distorted encoding method. The conventional Bayesian neural network is revised by embedding physical‐inspired elements to make it exclusive for metasurface design case. Taking a microwave metasurface as an example, such an approach is benchmarked by simultaneously yielding predicted results and specific uncertainty and also providing experimental reliability for different metasurface manufacturers. This work ushers in a fathomable tool to help users make better decisions for deep learning output, meriting other research domains of optics and materials science. Computer science ; Deep learning ; Bayesian network ; Artificial neural network ; Embedding ; Artificial intelligence ; Encoding (memory) ; Bayesian probability ; Reliability (semiconductor) ; Machine learning ; Physics ; Power (physics) Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Millimeter-Wave Propagation and Modeling
Authors: Eleonora Romeo | María Fernanda Lezana-Muralles | Francesc Illas | Federico Calle‐Vallejo
Source: ACS Applied Materials & Interfaces (article, 2023-04-25)
Field: Materials Science — Materials Chemistry

Extracting Features of Active Transition Metal Electrodes for NO Electroreduction with Catalytic Matrices Electrocatalytic reduction of oxidized nitrogen compounds (NO<sub><i>x</i></sub>) promises to help rebalance the nitrogen cycle. It is widely accepted that nitrate reduction to NH<sub>4</sub><sup>+</sup>/NH<sub>3</sub> involves NO as an intermediate, and NO hydrogenation is the potential-limiting step of NO reduction. Whether *NO hydrogenates to *NHO or *NOH is still a matter of debate, which makes it difficult to optimize catalysts for NO<sub><i>x</i></sub> electroreduction. Here, "<i>catalytic matrices</i>" are used to swiftly extract features of active transition metal catalysts for NO electroreduction. The matrices show that active catalysts statistically stabilize *NHO over *NOH and have undercoordinated sites. Besides, square-symmetry active sites with Cu and other elements may prove active for NO electroreduction. Finally, multivariate regressions are able to reproduce the main features found by the matrices, which opens the door for more sophisticated machine-learning studies. In sum, catalytic matrices may ease the analysis of complex electrocatalytic reactions on multifaceted materials. Materials science ; Transition metal ; Catalysis ; Electrode ; Electrocatalyst ; Nanotechnology ; Inorganic chemistry ; Electrochemistry ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Ammonia Synthesis and Nitrogen Reduction
Authors: Walter Malone | Abdelkader Kara
Source: Surface Science (article, 2023-01-25)
Field: Materials Science — Materials Chemistry

Predicting adsorption energies and the physical properties of H, N, and O adsorbed on transition metal surfaces: A machine learning study Adsorption ; Bimetallic strip ; Substrate (aquarium) ; Transition metal ; Materials science ; Artificial neural network ; Surface (topology) ; Metal ; Chemistry ; Physical chemistry ; Geometry ; Computer science ; Mathematics ; Metallurgy ; Catalysis ; Machine learning Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Toby Lewis‐Atwell | Daniel Beechey | Özgür Şimşek | Matthew N. Grayson
Source: ACS Catalysis (article, 2023-10-06)
Field: Materials Science — Materials Chemistry

Reformulating Reactivity Design for Data-Efficient Machine Learning Machine learning (ML) can deliver rapid and accurate reaction barrier predictions for use in rational reactivity design. However, model training requires large data sets of typically thousands or tens of thousands of barriers that are very expensive to obtain computationally or experimentally. Furthermore, bespoke data sets are required for each region of interest in reaction space as models typically struggle to generalize. We have therefore reformulated the ML barrier prediction problem toward a much more data-efficient process: finding a reaction from a prespecified set with a desired target value. Our reformulation enables the rapid selection of reactions with purpose-specific activation barriers, for example, in the design of reactivity and selectivity in synthesis, catalyst design, toxicology, and covalent drug discovery, requiring just tens of accurately measured barriers. Importantly, our reformulation does not require generalization beyond the domain of the data set at hand, and we show excellent results for the highly toxicologically and synthetically relevant data sets of aza-Michael addition and transition-metal-catalyzed dihydrogen activation, typically requiring less than 20 accurately measured density functional theory (DFT) barriers. Even for incomplete data sets of E2 and S<sub>N</sub>2 reactions, with high numbers of missing barriers (74% and 56% respectively), our chosen ML search method still requires significantly fewer data points than the hundreds or thousands needed for more conventional uses of ML to predict activation barriers. Finally, we include a case study in which we use our process to guide the optimization of the dihydrogen activation catalyst. Our approach was able to identify a reaction within 1 kcal mol<sup>-1</sup> of the target barrier by only having to run 12 DFT reaction barrier calculations, which illustrates the usage and real-world applicability of this reformulation for systems of high synthetic importance. Reactivity (psychology) ; Computer science ; Catalysis ; Artificial intelligence ; Chemistry ; Combinatorial chemistry ; Organic chemistry Machine Learning in Materials Science ; Radiation Effects in Electronics ; Advanced Memory and Neural Computing