jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thakur Singh | Jatinder Kumar | Joy Prakash Misra
Source: Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (article, 2022-02-23)
Field: Materials Science — Materials Chemistry

Process Modelling and Optimization using ANN and RSM during WSEM of Ni<sub>51.59</sub>Ti<sub>48.41</sub> shape memory alloy Shape memory Ti-Ni alloys have fascinated significantly in recent years since these types of material are intelligent, shape memory and functional materials. These materials find many applications in the engineering and medical fields. In the current study, ‘Ni-Ti’ shape memory alloy (SMA) has been processed by wire spark erosion machining (WSEM). This research article explores the effects of controllable machining variables such as spark on-time, spark off-time, wire feed and servo voltage on productivity, i.e., material removal rate (MRR) and surface integrity. To assess the impact of WSEM controllable factor, a statistical analysis is done. Mathematical modelling and ANOVA study have been conducted after performing 29 experiments based on Box-Behnken design (BBD) of response surface methodology (RSM) technique. Artificial neural network (ANN) modelling technique is used for further prediction of the response. The predicted values obtained from the ANN model shows excellent agreement with experimental outcomes. In this study, the correlation coefficient (R) value is 0.9919, which is very close to unity. It is clearly showing that the prediction accuracy of ANN model is high. Furthermore, scanning electron microscope (SEM) is utilized to assess the microstructure and composition of the WSEMed surfaces. After a critical study, it has been noticed that machined surfaces contain debris, cracks, craters and spherical droplets. In addition, energy dispersive spectroscopy (EDS) analysis has been performed to check the elemental composition on the machined surface. Shape-memory alloy ; Response surface methodology ; Materials science ; Machining ; Scanning electron microscope ; SPARK (programming language) ; Artificial neural network ; Electrical discharge machining ; Microstructure ; Mechanical engineering ; Composite material ; Metallurgy ; Computer science ; Engineering ; Machine learning Shape Memory Alloy Transformations ; Innovations in Concrete and Construction Materials ; Advanced Machining and Optimization Techniques
Authors: Xiaolan Tian | Xiujuan Qi | Yi Wang | Junnan Wu | Siwei Song | Qinghua Zhang
Source: Propellants Explosives Pyrotechnics (article, 2022-11-21)
Field: Materials Science — Materials Chemistry

Predicting Enthalpy of Formation of Energetic Compounds by Machine Learning: Comparison of Featurization Methods and Algorithms Abstract Machine learning (ML) is an emerging approach for predicting molecular properties. The prediction of the properties of energetic molecules by ML is still in its infancy. In order to improve the accuracy of ML‐based predictions, it is important to pay attention to aspects such as data preparation, model selection, and hyperparameter tuning. In this work, we focused on the influence of different featurization methods and algorithms on predicting the enthalpy of formation (EOF) of energetic compounds. We manually extracted a dataset consisting of 649 EOF values of energetic materials from the literature and compared different combinations of featurization methods and algorithms. The experimental results confirmed that ML can effectively map the relationship between molecular structure and EOF. Custom descriptor sets were found to perform best in featurization with a mean absolute error of 90.10 kJ mol −1 , after training by kernel ridge regression algorithm. Algorithm ; Enthalpy ; Hyperparameter ; Kernel (algebra) ; Mean squared prediction error ; Machine learning ; Computer science ; Ridge ; Regression ; Artificial intelligence ; Mathematics ; Thermodynamics ; Statistics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Energetic Materials and Combustion
Authors: Lingjun Wu | Zhenming Xu | Zixuan Wang | Zijian Chen | Zhichao Huang | Chao Peng | Xiangdong Pei | Xiangguo Li | Jonathan P. Mailoa | Chang‐Yu Hsieh | Tao Wu | Xue‐Feng Yu | Haitao Zhao
Source: Science China Technological Sciences (article, 2022-09-20)
Field: Materials Science — Materials Chemistry

Machine learning accelerated carbon neutrality research using big data—from predictive models to interatomic potentials Carbon neutrality ; Neutrality ; Big data ; Scale (ratio) ; Computer science ; Moment (physics) ; Atomic units ; Carbon fibers ; Data science ; Nanotechnology ; Statistical physics ; Materials science ; Physics ; Engineering ; Algorithm ; Data mining ; Political science ; Renewable energy ; Law ; Electrical engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Hybrid Renewable Energy Systems
Authors: Suman Timilsina | Ho Geun Shin | Kee‐Sun Sohn | Ji Sik Kim
Source: Advanced Intelligent Systems (article, 2022-04-29)
Field: Chemistry — Organic Chemistry

Dark‐Mode Human–Machine Communication Realized by Persistent Luminescence and Deep Learning Increasing ubiquitous collaborative intelligence between humans and machines requires human–machine communication (HMC) that is more human and less machine‐like to accomplish given tasks. Although speech signals are considered the best modes of communication in HMC, background noise often interferes with these signals. Therefore, research focused on integrating lip‐reading technology into HMC has gained significant attention. However, lip‐reading functions effectively only in well‐lit environments. In contrast, HMC may occur daily in dark environments owing to potential energy shortages, increased exploration in darkness, nighttime emergencies, etc. Herein, a possible method for HMC in the dark mode is presented, which is realized based on deep learning motion patterns of persistent luminescence (PL) of the skin surrounding the lips. An ultrasoft PL–polymer composite patch is used to record the motion pattern of the skin during speech in the dark. It is found that visual geometric group network (VGGNET‐5) and residual neural network (ResNet‐34) could predict spoken words in darkness with test accuracies of 98.5% and 98.75%, respectively. Furthermore, these models could effectively distinguish similar‐sounding words such as “around” and “ground.” Dark‐mode communication can allow a wide range of people, including disabled people with limited dexterity and voice tremors, to communicate with artificial intelligence machines. Computer science ; Artificial intelligence ; Mode (computer interface) ; Reading (process) ; Motion (physics) ; Speech recognition ; Human–computer interaction Polydiacetylene-based materials and applications ; Speech and Audio Processing ; Random lasers and scattering media
Authors: Yutao Ma | Rohan Kapoor | Bineet Sharma | Allen P. Liu | Andrew L. Ferguson
Source: Molecular Systems Design & Engineering (article, 2022-09-22)
Field: Materials Science — Biomaterials

Computational design of self-assembling peptide chassis materials for synthetic cells High-throughput screening based on molecular simulation, machine learning and black-box optimization identifies promising candidates for forming self-assembled peptide vesicles. Chassis ; Peptide ; Nanotechnology ; Throughput ; Computer science ; Synthetic biology ; Self-assembling peptide ; Engineering ; Artificial intelligence ; Computer architecture ; Computational biology ; Materials science ; Chemistry ; Biology ; Biochemistry ; Mechanical engineering Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Molecular Sensors and Ion Detection
Authors: Biao Wang | Haijie Ren | Wenxin Cao | Yanwei Hu | Yurong He | Jiecai Han | Jiaqi Zhu
Source: Zhongguo kexue. Wulixue Lixue Tianwenxue (article, 2022-03-14)
Field: Materials Science — Materials Chemistry

Prediction of thermal conductivity of diamond film by neural network based on first principles <p indent="0mm">As a high thermal conductivity material, diamond is significant for microelectronic cooling and other applications. Multiscale analysis of diamond’s thermal conductivity is beneficial to designing micro-nano electronic components and thermal management. The Boltzmann transport equation is an ideal tool to describe the phenomenon of multiscale phonon transport. However, due to its high dimensionality, it is incredibly challenging to use it to calculate and predict the thermal conductivity of diamonds at different scales. Based on the first principle, physical information neural network and Boltzmann transport equation, this paper effectively predicts the multiscale thermal transport of phonons in diamond and makes related analysis. After the phonon dispersion information, the diamond’s group velocity and scattering rate were calculated by the first principles. Relevant results were input into the physical information neural network as training samples. To introduce the Boltzmann transport equation as physical information in physical information neural network, we embedded related control equations and boundary conditions in the loss function of the neural network. When the loss function of the physical information neural network drops to a set standard, the trained model can accurately predict the phonon energy distribution. In this paper, physical information neural network was used to predict the transport law of phonons in a one-dimensional diamond film at different temperatures and scales. The solution results of the physical information neural network and linearized Boltzmann transport equation have high consistency. It could be deduced that the thermal conductivity of diamond had a size effect on the millimeter scale. At the same temperature, the smaller the scale, the lower the normalized thermal conductivity of the diamond, and the lower the temperature, the more pronounced the size effect is on the thermal conductivity. Under the same scale, the normalized thermal conductivity was lower, and the change law of the phonon thermal conductivity of each mode of the diamond was the same. Thermal conductivity ; Diamond ; Artificial neural network ; Materials science ; Thermal ; Conductivity ; Artificial intelligence ; Computer science ; Engineering physics ; Composite material ; Thermodynamics ; Engineering ; Physics ; Chemistry ; Physical chemistry Thermal properties of materials ; Nanofluid Flow and Heat Transfer ; Model Reduction and Neural Networks
Authors: Shunsuke Daimon | Kakeru Tsunekawa | S. Kawakami | Takashi Kikkawa | R. Ramos | Koichi Oyanagi | Tomi Ohtsuki | Eiji Saitoh
Source: Nature Communications (article, 2022-06-08)
Field: Materials Science — Materials Chemistry

Deciphering quantum fingerprints in electric conductance When the electric conductance of a nano-sized metal is measured at low temperatures, it often exhibits complex but reproducible patterns as a function of external magnetic fields called quantum fingerprints in electric conductance. Such complex patterns are due to quantum-mechanical interference of conduction electrons; when thermal disturbance is feeble and coherence of the electrons extends all over the sample, the quantum interference pattern reflects microscopic structures, such as crystalline defects and the shape of the sample, giving rise to complicated interference. Although the interference pattern carries such microscopic information, it looks so random that it has not been analysed. Here we show that machine learning allows us to decipher quantum fingerprints; fingerprint patterns in magneto-conductance are shown to be transcribed into spatial images of electron wave function intensities (WIs) in a sample by using generative machine learning. The output WIs reveal quantum interference states of conduction electrons, as well as sample shapes. The present result augments the human ability to identify quantum states, and it should allow microscopy of quantum nanostructures in materials by making use of quantum fingerprints. Quantum ; Electron ; Physics ; Thermal conduction ; Interference (communication) ; Conductance ; Condensed matter physics ; Materials science ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Neural Networks and Reservoir Computing
Authors: Chuanrui Qin | Mengtao Dang | Yifei Meng | Dongfeng Zhao
Source: Process Safety Progress (article, 2022-07-29)
Field: Materials Science — Materials Chemistry

Thermal risk classification optimization of flammable aromatic nitro compounds: Experiments and <scp>QSPR</scp> models Abstract In view of the frequent occurrence of major fires and explosions in the production, transportation, and storage of aromatic nitro compounds, the reactive thermal hazards of these compounds have attracted widespread attention in the chemical industry. Thermal hazard assessment is of great significance for the standardized storage of reactive chemicals. Based on adiabatic calorimetry and quantitative structure–property relationship (QSPR) prediction, a thermal hazard assessment and classification method dominated by the molecular structure of nitro compounds were proposed. While obtaining thermodynamic parameters, stable configuration and molecular descriptors, the prediction models were constructed by multiple linear regression (MLR) and artificial neural network (ANN), respectively, including self‐accelerating decomposition temperature (SADT), maximum power density (MPD), and the apparent activation energy ( E a ). On this basis, SADT, H 50 , and E a were selected as the possible characterization parameters of thermal risk of nitro compounds, while MPD and ΔT ad were selected as the characterization parameters of consequence severity. When combined with weight and reference TNT, this method comprehensively evaluated the thermal hazards of nitro compounds and divided them into four types of thermal hazard levels. By comparing the method with the previous classification standards, it is found that the evaluation method is expected to provide theoretical support for the safe standardized storage of nitro compounds in chemical enterprises. Quantitative structure–activity relationship ; Nitro ; Chemistry ; Hazard ; Thermal decomposition ; Organic chemistry ; Stereochemistry Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Risk and Safety Analysis
Authors: Anna W. Sobańska
Source: Environmental Science and Pollution Research (article, 2022-08-22)
Field: Chemistry — Spectroscopy

Immobilized artificial membrane-chromatographic and computational descriptors in studies of soil-water partition of environmentally relevant compounds Chromatographic retention factor log k<sub>IAM</sub> obtained from immobilized artificial membrane (IAM) HPLC with buffered, aqueous mobile phases and calculated molecular descriptors (molecular weight - log M<sub>W</sub>; molar volume - V<sub>M</sub>; polar surface area - PSA; total count of nitrogen and oxygen atoms -(N + O); count of freely rotable bonds - FRB; H-bond donor count - HD; H-bond acceptor count - HA; energy of the highest occupied molecular orbital - E<sub>HOMO</sub>; energy of the lowest unoccupied orbital - E<sub>LUMO</sub>; dipole moment - DM; polarizability - α) obtained for a group of 175 structurally unrelated compounds were tested in order to generate useful models of solutes' soil-water partition coefficient normalized to organic carbon log K<sub>oc</sub>. It was established that log k<sub>IAM</sub> obtained in the conditions described in this study is not sufficient as a sole predictor of the soil-water partition coefficient. Simple, potentially useful models based on log k<sub>IAM</sub> and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR) and artificial neural networks (ANN). The models proposed in the study were tested on a group of 50 compounds with known experimental log K<sub>oc</sub> values by plotting the calculated vs. experimental values. There is a good close similarity between the calculated and experimental data for both MLR and ANN models for compounds from different chemical families (R<sup>2</sup> ≥ 0.80, n = 50) which proves the models' reliability. Ecotoxicology ; Partition (number theory) ; Partition coefficient ; Membrane ; Chemistry ; Soil Pollutants ; Environmentally friendly ; Chromatography ; Environmental chemistry ; Soil contamination ; Environmental science ; Soil water ; Mathematics ; Soil science ; Ecology ; Biology Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Pesticide Residue Analysis and Safety
Authors: J. Zhu | Yingce Xia | Lijun Wu | Shufang Xie | Tao Qin | Wengang Zhou | Houqiang Li | Tie‐Yan Liu
Source: arXiv (Cornell University) (preprint, 2022-07-14)
Field: Materials Science — Materials Chemistry

Unified 2D and 3D Pre-Training of Molecular Representations Molecular representation learning has attracted much attention recently. A molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds, and can also be represented by a 3D conformation with 3-dimensional coordinates of all atoms. We note that most previous work handles 2D and 3D information separately, while jointly leveraging these two sources may foster a more informative representation. In this work, we explore this appealing idea and propose a new representation learning method based on a unified 2D and 3D pre-training. Atom coordinates and interatomic distances are encoded and then fused with atomic representations through graph neural networks. The model is pre-trained on three tasks: reconstruction of masked atoms and coordinates, 3D conformation generation conditioned on 2D graph, and 2D graph generation conditioned on 3D conformation. We evaluate our method on 11 downstream molecular property prediction tasks: 7 with 2D information only and 4 with both 2D and 3D information. Our method achieves state-of-the-art results on 10 tasks, and the average improvement on 2D-only tasks is 8.3%. Our method also achieves significant improvement on two 3D conformation generation tasks. Graph ; Computer science ; Representation (politics) ; Atom (system on chip) ; Training set ; Theoretical computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Milad Mohseni | Ahmed Alkhayyat | P. Balaji Srikaanth | Ali Jawad Alrubaie | Arnold C. Alguno | Rey Y. Capangpangan | Bhupesh Kumar Singh
Source: Journal of Nanomaterials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Analyzing Characteristics for Two‐Step SET Operation Scheme for Improving Write Time in Nanoscale Phase‐Change Memory (PCM) PCM (phase‐change memory) is a memory innovation that has gained prominence as a capacity‐class memory for computer systems. It is made up of a tiny functional amount of phase‐change material that is located in the middle of two electrodes. In PCM, data is kept by utilizing the difference in electrical resistance between a crystalline phase, which has a high resistance, and its amorphous phase, which has a low resistance. Using electrical pulses, the phase‐change material would be shifted from a high to the low conductive region and conversely. However, the device’s material science concerned with PCM has been generally studied, and questions remain regarding their electrical, warm, and fundamental aspects since its publication in the 1960s. One major downside of PCM is its low heat conductivity, which causes delays in the energy charging/discharging procedure and hardware efficiency. As a result of this, one of the primary focuses of PCM studies has been the improvement of PCM’s heat conductivity through the utilization of nanotechnology and nanomaterials. Nanotechnology has been developing ultrasmall nanoparticles to improve traditional PCMs’ thermophysical characteristics. These nanomaterials, such as metal, metal oxide, and carbon, will significantly boost PCM’s thermal properties, including supercooling, viscosity, and heat capacity. An overview of PCM devices is presented in this article, which underlies perusing and composition activities. Consequently, we offered novel PCM devices and materials. Therefore, the total study is qualitative, and no machine learning approach is used. Because of this, we cannot say that the data is quantitative. Our work includes both test representations of the specific features observed in nanoscale PCM devices and material science demonstrations. In the end, we provide a point of view on some remaining open inquiries and possible future exploration directions. Phase-change memory ; Materials science ; Nanomaterials ; Supercooling ; Phase-change material ; Thermal conductivity ; Nanotechnology ; Phase (matter) ; Work (physics) ; Engineering physics ; Phase change ; Mechanical engineering ; Composite material ; Thermodynamics Phase-change materials and chalcogenides ; Machine Learning and ELM ; Advanced Memory and Neural Computing
Authors: Meenakshi Ahirwar | B.K. Behera
Source: Journal of Natural Fibers (article, 2022-08-03)
Field: Materials Science — Polymers and Plastics

Objective Hand Evaluation of Stretch Fabrics Using Artificial Neural Network and Computational Model Stretch woven textiles are widely employed because of their excellent elongation and recovery properties. The stretch fabrics studied in this research can be used as power stretch and action stretch sportswear fabrics. The purpose of this paper is to investigate an approach to predict the total hand value by translating the senses into numbers. Computational and artificial neural network models were developed. Five primary hand attributes softness, smoothness, fullness, stiffness, and stretchability were shortlisted that influence the fabric handle. Computational methods were used to generate primary and total hand equations based on basic mechanical parameters. To forecast primary hand values, stretch percent was used with low stress mechanical properties. The association between subjective, computational, and artificial neural network total hand values was investigated using a statistical technique. The subjective and computational hand values have a high correlation of 0.84. The subjective and artificial neural network hand values were shown to have a 0.82 correlation. The accuracy of both models’ prediction of fabric hand was found to be very high. The study finds that both models can forecast the total hand value of stretch materials with a tolerable level of accuracy. Artificial neural network ; Artificial intelligence ; Computational model ; Computer science ; Stiffness ; Structural engineering ; Mathematics ; Engineering Textile materials and evaluations ; Lower Extremity Biomechanics and Pathologies ; Ergonomics and Musculoskeletal Disorders
Authors: Yanming Liu | Shu-Jian Chen | Kwesi Sagoe‐Crentsil | Wenhui Duan
Source: Acta Materialia (article, 2022-03-19)
Field: Materials Science — Materials Chemistry

Large set microstructure reconstruction mimicking quantum computing approach via deep learning Probabilistic logic ; Computer science ; Computational science ; Process optimization ; Deep learning ; Set (abstract data type) ; Process (computing) ; Microstructure ; Quantum computer ; Materials science ; Algorithm ; Quantum ; Artificial intelligence ; Computer engineering ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis