jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xin Chen | Lifang Wang | Xingyu Gao | Ya‐Fan Zhao | De-Ye Lin | Weidong Chu | Haifeng Song
Source: Computer Physics Communications (article, 2021-08-09)
Field: Materials Science — Materials Chemistry

Machine learning enhanced empirical potentials for metals and alloys Computer science ; Interatomic potential ; Embedded atom model ; Python (programming language) ; Atom (system on chip) ; Artificial intelligence ; Algorithm ; Machine learning ; Potential energy surface ; Computational science ; Statistical physics ; Molecular dynamics ; Physics ; Ab initio ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Y. Mishin
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine-Learning Interatomic Potentials for Materials Science Interatomic potential ; Computer science ; Materials science ; Statistical physics ; Nanotechnology ; Chemistry ; Physics ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Hiroaki Matsumoto | Tadokoro Daiki | Irvin Séchepée
Source: ISIJ International (article, 2021-11-15)
Field: Materials Science — Materials Chemistry

Strength and Ductility Balance of a Ti-5Al-2Sn-2Zr-4Cr-4Mo (Ti-17) Alloy with Various Microstructures: Experiment and Machine Learning This work examined the tensile properties and hardness of a Ti-5Al-2Sn-2Zr-4Cr-4Mo (Ti-17) alloy having various morphology of an equiaxed, a (α+β) bimodal, and a (α+β) lamellar (with or without secondary α precipitates) microstructures in terms of an experimental analysis and a machine learning approach. Among the various morphology, a good balance on strength and ductility is obtained in the bimodal morphology. On the strength, higher strength is exhibited in the microstructures having secondary lamellar α precipitates. Here, we experimentally find that the primary α phase of an equiaxed grain or a lamellae acts as only the strengthening according to the rule-of-mixtures, on the other hand, the secondary lamellar α precipitates act as precipitation strengthening dominated by cutting mechanism. On the ductility, it is strongly affected by the difference in morphology and the formation of secondary lamellar α precipitates; high ductility is obtained in an equiaxed grained morphology and in the microstructure with a decreasing fraction of secondary α precipitates. A machine learning approach adapting neural network and clustering (the Ward’s method) algorithms successfully predicts the linking between the microstructural factors and the tensile properties of strength, elongation to fracture and reduction of area; a similar dominant mode to the experimental results is estimated from a machine learning approach. Additionally, a quantitative analysis on the effect of each microstructural factor is successfully estimated according to the sensitivity of variable’s importance in terms of the neural network algorithm. Equiaxed crystals ; Materials science ; Ductility (Earth science) ; Lamellar structure ; Microstructure ; Ultimate tensile strength ; Alloy ; Titanium alloy ; Precipitation ; Metallurgy ; Composite material ; Creep Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties ; Advanced materials and composites
Authors: Shi-Ping Peng | Xinyu Yang | Yi Zhao
Source: International Journal of Molecular Sciences (article, 2021-08-23)
Field: Materials Science — Materials Chemistry

Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning The proposition of non-fullerene acceptors (NFAs) in organic solar cells has made great progress in the raise of power conversion efficiency, and it also broadens the ways for searching and designing new acceptor molecules. In this work, the design of novel NFAs with required properties is performed with the conditional generative model constructed from a convolutional neural network (CNN). The temporal CNN is firstly trained to be a good string-based molecular conditional generative model to directly generate the desired molecules. The reliability of generated molecular properties is then demonstrated by a graph-based prediction model and evaluated with quantum chemical calculations. Specifically, the global attention mechanism is incorporated in the prediction model to pool the extracted information of molecular structures and provide interpretability. By combining the generative and prediction models, thousands of NFAs with required frontier molecular orbital energies are generated. The generated new molecules essentially explore the chemical space and enrich the database of transformation rules for molecular design. The conditional generation model can also be trained to generate the molecules from molecular fragments, and the contribution of molecular fragments to the properties is subsequently predicted by the prediction model. Chemical space ; Interpretability ; Computer science ; Generative model ; Molecular graph ; Artificial intelligence ; Molecular descriptor ; Generative grammar ; Biological system ; Graph ; Chemistry ; Theoretical computer science ; Machine learning ; Quantitative structure–activity relationship ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Nathan L.D. Sarmento | José Marques Basílio | Maxsuel Ferreira Cunha | Cícero da Rocha Souto | Andreas Ríes
Source: Applied Artificial Intelligence (article, 2021-12-20)
Field: Materials Science — Materials Chemistry

Force Control of a Shape Memory Alloy Spring Actuator Based on Internal Electric Resistance Feedback and Artificial Neural Networks This paper presents a study of the resistive behavior of a Shape Memory Alloy spring, with a focus on the application of electrical resistance feedback in control systems. Artificial Neural Networks of different topologies were designed to learn the relation between spring electrical resistance and the force exerted. The feedback between layers in Neural Networks is demonstrated to be a key parameter in learning the non-linear and hysteretic behavior of Shape Memory Alloys. Experiments with closed-loop systems showed that shape memory alloy springs generated forces that converged satisfactorily to the desired reference values. The scientific contribution of this work is the use of electrical resistance variation as feedback for controlling the spring force, eliminating the use of an external force sensor. Neural networks were used for both, the sensing process and the system control; in that way the nonlinear and hysterical behavior of the shape memory alloy actuator was well considered. Shape-memory alloy ; Actuator ; Artificial neural network ; Computer science ; Spring (device) ; Control theory (sociology) ; Nonlinear system ; Work (physics) ; Focus (optics) ; Artificial intelligence ; Control (management) ; Mechanical engineering ; Physics ; Engineering Shape Memory Alloy Transformations
Authors: M. Hamed Mozaffari | Li‐Lin Tay
Source:  (article, 2021-12-06)
Field: Chemistry — Analytical Chemistry

Convolutional Neural Networks for Raman Spectral Analysis of Chemical Mixtures In the spectroscopy domain, one-dimensional Convolutional Neural Networks (1D CNN) assist researchers in recognizing one pure chemical compound and distinguishing it from unknown substances. The novelty of this approach is that a trained CNN operates automatically with almost no pre-or post-processing of data. However, the application of 1-D CNNs has typically been restricted to a binary classification of pure chemical substances. This study highlights a new approach in spectral recognition and quantification of components in chemical mixtures. Two 1-D CNN models, RaMixNet I and II, have been developed for this purpose as two multi-label classifiers. Depending on data availability, there is no limit to the number of compounds in an unknown mixture to recognize by RaMixNet models. We trained RaMixNet models using generated Raman spectra utilizing a novel data augmentation technique that adds random noise and different baselines to each spectrum as well as random wavenumber shifts for Raman peaks. The experimental results over hundreds of generated synthetic test mixtures revealed that the classification accuracy of RaMixNet I and II is 100%; at the same time, the RaMixNet II model could reach an average means square error rate of 0.06 and R2 score of 0.76 for the quantification of each component. In a comparison study, RaMixNet models could distinguish components of six actual chemical mixtures better than well-established distance-based techniques in the literature. Convolutional neural network ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Raman spectroscopy ; Binary classification ; Binary number ; Random forest ; Principal component analysis ; Noise (video) ; Biological system ; Mathematics ; Support vector machine ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: C. Scherdel | Eddi Miller | Gudrun Reichenauer | Jan Schmitt
Source: Processes (article, 2021-04-11)
Field: Materials Science — Materials Chemistry

Advances in the Development of Sol-Gel Materials Combining Small-Angle X-ray Scattering (SAXS) and Machine Learning (ML) The requirements for new materials are increasing with each new application, which, in most cases, means an enhancement in the complexity of the development process. Nanoporous sol-gel-based materials, especially aerogels, are promising candidates for thermal superinsulation, electrodes for energy conversion and storage or high-end adsorbers. Their synthesis and processing route is complex, and the relationship between the material/processing parameters and the resulting structural and physical properties is not straightforward. Using small-angle X-ray scattering (SAXS) allows for fast structural characterization of both the gel and the resulting aerogel; combining these results with the respective physical properties of the aerogels and using these data as inputs for machine learning (ML) algorithms provide an approach to predict physical properties on the basis of a structural dataset. This data-driven strategy may be a feasible approach to speed up the development process. Thus, the study aimed to provide a proof of concept of ML-based model derivation from material, process and SAXS data to predict physical properties such as the solid-phase thermal conductivity (λs) of silica aerogels from a structural dataset. Here, we used different data subsets as predictors according to different states of synthesis (wet and dry) to evaluate the model performance. Small-angle X-ray scattering ; Aerogel ; Nanoporous ; Materials science ; Characterization (materials science) ; Sol-gel ; Scattering ; Process (computing) ; Computer science ; Process engineering ; Nanotechnology ; Physics ; Optics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Zhiyong Wei | Cheng Zhang | Yajing Kan | Yan Zhang | Yunfei Chen
Source: Computational Materials Science (article, 2021-11-03)
Field: Materials Science — Materials Chemistry

Developing machine learning potential for classical molecular dynamics simulation with superior phonon properties Anharmonicity ; Phonon ; Molecular dynamics ; Laser linewidth ; Statistical physics ; Work (physics) ; Gaussian ; Potential energy ; Interatomic potential ; Density functional theory ; Dispersion (optics) ; Ab initio ; Coherent potential approximation ; Physics ; Condensed matter physics ; Electronic structure ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Zhongyu Wan | Quan‐De Wang | Dongchang Liu | Jinhu Liang
Source: International Journal of Quantum Chemistry (article, 2021-06-07)
Field: Materials Science — Materials Chemistry

Machine learning prediction of the optimal carrier concentration and band gap of quaternary thermoelectric materials via element feature descriptors Abstract While the thermoelectric (TE) materials have attracted significant attention in recent years, the design and discovery of new TE materials with optimal carrier concentration and band gap remains a great challenge. Herein, we report the development of machine learning (ML) methods to predict TE materials with introducing physically meaningful simple descriptors. Specifically, we use the number of electrons, Pauling electronegativity and relative atomic mass as the basic physical variables and compute 242 descriptors in 64 categories to characterize the molecular information of a TE material. Multiple stepwise regression is employed to reduce the dimensionality in the developed ML models, and 5 and 4 important features for the band gap and carrier concentration is selected, respectively. The important features are used as input of a total number of 19 ML methods to select the optimal ML models for the prediction of band gap and carrier concentration, respectively. It is shown that the least square support vector machines method is the best model for the prediction of the band gap, while the back propagating artificial neutral network model exhibits the best performance in predicting the carrier concentration values. This work provides novel theoretical guidance for the rapid prediction properties of TE materials. The simple descriptors we defined can accurately predict the band gap and carrier concentration of quaternary TE materials. Electronegativity ; Band gap ; Curse of dimensionality ; Artificial intelligence ; Machine learning ; Simple (philosophy) ; Materials science ; Computer science ; Electron ; Feature (linguistics) ; Biological system ; Chemistry ; Optoelectronics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Saba Ayub | Beh Hoe Guan | Faiz Ahmad | Muhammad Faisal Javed | Amir Mosavi | Imre Felde
Source: Preprints.org (preprint, 2021-07-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Preparation methods for Graphene, Metal and Polymer-based Composites for EMI Shielding Materials: State of the Art of Conventional and Machine Learning Methods The electromagnetic inference is an issue from decades, where working for a better shielding material is still on-going. The purpose of this study is to review the existing methods in the formation of graphene, metal and polymer-based composites. Study indicates that in graphene and metal-based composites, the utilization of alternating deposition method showed the highest shielding effectiveness, whereas, in polymer-based composite, the utilization of chemical vapor deposition method showed highest shielding effectiveness. However, this review reveals that still there is a gap in the literature in terms of the selection of the method. Although there are various available methods which researchers adopt as per their convenience, none of the studies makes a comparison of the methods to form a similar composite. Therefore, as a future gap researcher needs to adopt various methods to form a single composite and then make a comparison of shielding effectiveness. This act will be useful for future researchers to select the appropriate method. Electromagnetic shielding ; Graphene ; Materials science ; EMI ; Composite material ; Composite number ; Polymer ; Electromagnetic interference ; Chemical vapor deposition ; Computer science ; Nanotechnology ; Telecommunications Electromagnetic wave absorption materials ; Graphite, nuclear technology, radiation studies ; Transportation Systems and Safety
Authors: Mohammad Asaduzzaman Chowdhury | Nayem Hossain | Md. Bengir Ahmed Shuvho | Mohammad Fotouhi | Md Sakibul Islam | Md. Ramjan Ali | Mohammod Abul Kashem
Source: Computational Condensed Matter (review, 2021-09-20)
Field: Materials Science — Materials Chemistry

Recent machine learning guided material research - A review Aerospace ; Computer science ; Automotive industry ; Characterization (materials science) ; Manufacturing engineering ; Systems engineering ; Biochemical engineering ; Process engineering ; Engineering ; Nanotechnology ; Materials science ; Aerospace engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Mandana Saebi | Bozhao Nan | John E. Herr | Jessica Wahlers | Zhichun Guo | A. Zuranski | Thierry Kogej | Per‐Ola Norrby | Abigail G. Doyle | Olaf Wiest | Nitesh V. Chawla
Source:  (preprint, 2021-09-27)
Field: Materials Science — Materials Chemistry

On the Use of Real-World Datasets for Reaction Yield Prediction The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as good or better than the best previous models on two HTE datasets for the Suzuki and Buchwald-Hartwig reactions. However, training of the AGNN on the ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions. Bottleneck ; Computer science ; Key (lock) ; Machine learning ; Yield (engineering) ; Task (project management) ; Context (archaeology) ; Artificial intelligence ; Graph ; Training set ; Data mining ; Data science ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Jia Fang Fang | Linyuan Hu | Jianfeng Dong | Haowei Li | Hui Wang | Huafen Zhao | Yao Zhang | Min Liu
Source: Scientific Reports (article, 2021-09-21)
Field: Materials Science — Materials Chemistry

Predicting scalar coupling constants by graph angle-attention neural network Scalar coupling constant (SCC), directly measured by nuclear magnetic resonance (NMR) spectroscopy, is a key parameter for molecular structure analysis, and widely used to predict unknown molecular structure. Restricted by the high cost of NMR experiments, it is impossible to measure the SCC of unknown molecules on a large scale. Using density functional theory (DFT) to theoretically calculate the SCC of molecules is incredibly challenging, due to the cost of substantial computational time and space. Graph neural networks (GNN) of artificial intelligence (AI) have great potential in constructing molecul ar-like topology models, which endows them the ability to rapidly predict SCC through data-driven machine learning methods, and avoiding time-consuming quantum chemical calculations. With a priori knowledge of angles, we propose a graph angle-attention neural network (GAANN) model to predict SCC by means of some easily accessible related information. GAANN, with a multilayer message-passing network and a self-attention mechanism, can accurately simulate the molecular-like topological structure and predict molecular properties. Our simulations show that the prediction accuracy by GAANN, with the log(MAE) = -2.52, is close to that by DFT calculations. Different from conventional AI methods, GAANN combining the AI method with quantum chemistry theory (Karplus equation) has a strong physicochemical interpretability about angles. From an AI perspective, we find that bond angle has the highest correlation with the SCC among all angle features (dihedral angle, bond angle, geometric angles) about multiple coupling types in the small molecule datasets. Dihedral angle ; Artificial neural network ; Interpretability ; Computer science ; Scalar (mathematics) ; Density functional theory ; Topology (electrical circuits) ; Artificial intelligence ; Molecular geometry ; Molecular descriptor ; Machine learning ; Algorithm ; Molecule ; Statistical physics ; Computational chemistry ; Quantitative structure–activity relationship ; Physics ; Chemistry ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Qiyuan Zhao | Nicolae C. Iovanac | Brett M. Savoie
Source: Journal of Chemical Information and Modeling (article, 2021-05-25)
Field: Materials Science — Materials Chemistry

Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds Computational predictions of the thermodynamic properties of molecules and materials play a central role in contemporary reaction prediction and kinetic modeling. Due to the lack of experimental data and computational cost of high-level quantum chemistry methods, approximate methods based on additivity schemes and more recently machine learning are currently the only approaches capable of supplying the chemical coverage and throughput necessary for such applications. For both approaches, ring-containing molecules pose a challenge to transferability due to the nonlocal interactions associated with conjugation and strain that significantly impact thermodynamic properties. Here, we report the development of a self-consistent approach for parameterizing transferable ring corrections based on high-level quantum chemistry. The method is benchmarked against both the Pedley-Naylor-Kline experimental dataset for C-, H-, O-, N-, S-, and halogen-containing cyclic molecules and a dataset of Gaussian-4 quantum chemistry calculations. The prescribed approach is demonstrated to be superior to existing ring corrections while maintaining extensibility to arbitrary chemistries. We have also compared this ring-correction scheme against a novel machine learning approach and demonstrate that the latter is capable of exceeding the performance of physics-based ring corrections. Transferability ; Ring (chemistry) ; Quantum chemistry ; Molecule ; Quantum chemical ; Additive function ; Computer science ; Quantum ; Gaussian ; Computational chemistry ; Enthalpy ; Statistical physics ; Chemistry ; Thermodynamics ; Physics ; Mathematics ; Machine learning ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies