jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aikaterini Vriza | Angelos B. Canaj | Rebecca Vismara | Laurence J. Kershaw Cook | Troy D. Manning | Michael W. Gaultois | P.A. Wood | Vitaliy Kurlin | Neil G. Berry | Matthew S. Dyer | Matthew J. Rosseinsky
Source: Chemical Science (article, 2020-12-10)
Field: Materials Science — Materials Chemistry

One class classification as a practical approach for accelerating π–π co-crystal discovery The implementation of machine learning models has brought major changes in the decision-making process for materials design. One matter of concern for the data-driven approaches is the lack of negative data from unsuccessful synthetic attempts, which might generate inherently imbalanced datasets. We propose the application of the one-class classification methodology as an effective tool for tackling these limitations on the materials design problems. This is a concept of learning based only on a well-defined class without counter examples. An extensive study on the different one-class classification algorithms is performed until the most appropriate workflow is identified for guiding the discovery of emerging materials belonging to a relatively small class, that being the weakly bound polyaromatic hydrocarbon co-crystals. The two-step approach presented in this study first trains the model using all the known molecular combinations that form this class of co-crystals extracted from the Cambridge Structural Database (1722 molecular combinations), followed by scoring possible yet unknown pairs from the ZINC15 database (21 736 possible molecular combinations). Focusing on the highest-ranking pairs predicted to have higher probability of forming co-crystals, materials discovery can be accelerated by reducing the vast molecular space and directing the synthetic efforts of chemists. Further on, using interpretability techniques a more detailed understanding of the molecular properties causing co-crystallization is sought after. The applicability of the current methodology is demonstrated with the discovery of two novel co-crystals, namely pyrene-6<i>H</i>-benzo[<i>c</i>]chromen-6-one (<b>1</b>) and pyrene-9,10-dicyanoanthracene (<b>2</b>). Interpretability ; Class (philosophy) ; Chemical space ; Computer science ; Ranking (information retrieval) ; Workflow ; Drug discovery ; Machine learning ; Data mining ; Artificial intelligence ; Chemistry ; Database Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Chaochao Yan | Qianggang Ding | Peilin Zhao | Shuangjia Zheng | Jinyu Yang | Yang Yu | Junzhou Huang
Source: ChemRxiv (preprint, 2020-11-03)
Field: Materials Science — Materials Chemistry

RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate the retrosynthesis analysis, many retrosynthesis prediction methods have been proposed.However, most of them are cumbersome and lack interpretability about their predictions.In this paper, we devise a novel template-free algorithm, RetroXpert, for automatic retrosynthetic expansion by automating the procedure that chemists used to do.Our method disassembles retrosynthesis into two steps: i) we identify the potential reaction center within the target molecule through a graph neural network and generate intermediate synthons; and ii) we predict the associated reactants based on the obtained synthons via a reactant generation model. While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation. Retrosynthetic analysis ; Interpretability ; Synthon ; Computer science ; Artificial intelligence ; Process (computing) ; Stepping stone ; Margin (machine learning) ; Interpretation (philosophy) ; Machine learning ; Engineering ; Chemistry ; Programming language Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Rui Wang | Fei Liang | Zheshuai Lin
Source: Scientific Reports (article, 2020-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-driven prediction of diamond-like infrared nonlinear optical crystals with targeting performances Combining high-throughput screening and machine learning models is a rapidly developed direction for the exploration of novel optoelectronic functional materials. Here, we employ random forests regression (RFR) model to investigate the second harmonic generation (SHG) coefficients of nonlinear optical crystals with distinct diamond-like (DL) structures. 61 DL structures in Inorganic Crystallographic Structure Database (ICSD) are selected, and four distinctive descriptors, including band gap, electronegativity, group volume and bond flexibility, are used to model and predict second-order nonlinearity. It is demonstrated that the RFR model has reached the first-principles calculation accuracy, and gives validated predictions for a variety of representative DL crystals. Additionally, this model shows promising applications to explore new crystal materials of quaternary DL system with superior mid-IR NLO performances. Two new potential NLO crystals, Li<sub>2</sub>CuPS<sub>4</sub> with ultrawide bandgap and Cu<sub>2</sub>CdSnTe<sub>4</sub> with giant SHG response, are identified by this model. Electronegativity ; Nonlinear system ; Materials science ; Band gap ; Second-harmonic generation ; Diamond ; Nonlinear optical ; Flexibility (engineering) ; Infrared ; Optoelectronics ; Computer science ; Artificial intelligence ; Optics ; Mathematics ; Chemistry ; Physics Crystal Structures and Properties ; Chalcogenide Semiconductor Thin Films ; Nonlinear Optical Materials Research
Authors: Jith Sarker | Scott Broderick | A F M Anhar Uddin Bhuiyan | Zixuan Feng | Hongping Zhao | Baishakhi Mazumder
Source: Applied Physics Letters (article, 2020-04-13)
Field: Materials Science — Materials Chemistry

A combined approach of atom probe tomography and unsupervised machine learning to understand phase transformation in (AlxGa1−x)2O3 In this paper, we investigated the evolution of microstructural chemistry of metal organic chemical vapor deposition grown (010) (AlxGa1−x)2O3 films with varying Al contents, x = 0.10–1.0, using atom probe tomography (APT). At a low Al content (x ≤ 0.25), the films are homogeneous, where layer inhomogeneity appears at a high Al content (x &amp;gt; 0.25). Further increasing the Al content up to x ≥ 0.60 results in a homogeneous (AlxGa1−x)2O3 layer. This change in microstructural features was linked to the phase transformation of (AlxGa1−x)2O3 using a manifold learning approach to capture the governing features hidden in the data dimensionality. Combining APT to unsupervised machine learning enables APT to be an independent material characterization tool to investigate the microstructure, chemical composition, and phase related information. Atom probe ; Homogeneous ; Phase (matter) ; Microstructure ; Chemical vapor deposition ; Materials science ; Transformation (genetics) ; Characterization (materials science) ; Atom (system on chip) ; Deposition (geology) ; Layer (electronics) ; Artificial intelligence ; Analytical Chemistry (journal) ; Machine learning ; Chemistry ; Nanotechnology ; Computer science ; Metallurgy ; Thermodynamics ; Physics ; Organic chemistry ; Geology Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques ; Semiconductor materials and devices
Authors: Yu-Chuan Hsu | Chi-Hua Yu | Markus J. Buehler
Source: Advanced Engineering Materials (article, 2020-12-19)
Field: Materials Science — Materials Chemistry

Tuning Mechanical Properties in Polycrystalline Solids Using a Deep Generative Framework A framework for inverse design of tuning mechanical properties of polycrystalline brittle materials is presented using artificial intelligence (AI). Crystalline solids, which often exhibit distinct mechanical properties at different orientations, can be used as building blocks for polycrystalline composites. However, the design space of geometry and crystal misorientations is typically intractable, and all possible solutions cannot be discovered using experiment or numerical simulation. Herein, a framework using deep learning (DL) alongside a genetic algorithm (GA) is adopted to generate composite polycrystalline materials, whereas the raw material is brittle and sensitive to crystalline orientation, to achieve distinct mechanical properties in various combinatorial designs. The DL model, trained by full‐atomistic simulations of crystals with different orientations, evolves autonomously to yield a desirable range of target toughness values, exemplified in maximizing and minimizing toughness, which are validated by molecular dynamics (MD) simulations. It is found that higher crystal misorientations are preferred for high toughness, as opposed to lower overall misorientations for low‐toughness designs. Notably, this shows that a mechanism can be extracted from the AI algorithm. This materiomics method may ultimately change the way nanomaterials are designed, and can be applied to de novo biomaterial design, architected materials, and bioinspired structural materials. Crystallite ; Materials science ; Toughness ; Brittleness ; Fracture toughness ; Composite material ; Material Design ; Nanomaterials ; Crystal (programming language) ; Mechanical engineering ; Nanotechnology ; Computer science ; Metallurgy ; Engineering Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Calcium Carbonate Crystallization and Inhibition
Authors: Soumya Chatterjee | Sayanjit Singha Roy | Kaniska Samanta | Sudip Modak
Source: IEEE Sensors Letters (article, 2020-06-17)
Field: Materials Science — Materials Chemistry

Sensing Wettability Condition of Insulation Surface Employing Convolutional Neural Network Wettability of polymeric insulators is a prime indicator of the insulator surface condition. New polymeric insulator surfaces are hydrophobic in nature, where discrete water droplets are formed on the insulator surface. But as the insulation becomes aged, the surface loses its hydrophobicity, leading to the formation of continuous water channels, which subsequently leads to dry band arcing and even flashover, thereby affecting the long-term performance of the insulators. Therefore, accurate sensing of wettability of polymeric insulators is important for reliable insulator diagnostics. Considering the above fact, in this letter, we propose a deep learning framework for accurate sensing of wettability class (WC) of insulators. We captured the images of water droplets with varying WCs on the surface of an 11 kV silicone rubber suspension insulator. After suitable preprocessing, the captured images were fed to a pretrained deep convolutional neural network model AlexNet for the purpose of wettability classification. We also observed that our proposed method is capable of sensing different WC with a high degree of accuracy, which can be practically implemented for real-life monitoring of insulators. Wetting ; Insulator (electricity) ; Materials science ; Convolutional neural network ; Silicone rubber ; Arc flash ; Composite material ; Contact angle ; Immersion (mathematics) ; Computer science ; Optoelectronics ; Artificial intelligence High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Advanced Neural Network Applications
Authors: Giovanni Drera | Chahan M. Kropf | L. Sangaletti
Source: BOA (University of Milano-Bicocca) (article, 2020-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Deep neural network for x-ray photoelectron spectroscopy data analysis In this work, we characterize the performance of a deep convolutional neural network designed to detect and quantify chemical elements in experimental x-ray photoelectron spectroscopy data. Given the lack of a reliable database in literature, in order to train the neural network we computed a large (&lt;100 k) dataset of synthetic spectra, based on randomly generated materials covered with a layer of adventitious carbon. The trained net performs as well as standard methods on a test set of~500 well characterized experimental x-ray photoelectron spectra. Fine details about the net layout, the choice of the loss function and the quality assessment strategies are presented and discussed. Given the synthetic nature of the training set, this approach could be applied to the automatization of any photoelectron spectroscopy system, without the need of experimental reference spectra and with a low computational effort. X-ray photoelectron spectroscopy ; Artificial neural network ; Convolutional neural network ; Computer science ; Spectral line ; Set (abstract data type) ; Data set ; Function (biology) ; Pattern recognition (psychology) ; Artificial intelligence ; Training set ; Biological system ; Data mining ; Physics ; Nuclear magnetic resonance Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Ting Xue | Peng Liu | Jie Zhang | Jingkun Xu | Ge Zhang | Peicong Zhou | Yingying Li | Yifu Zhu | Xinyu Lu | Yangping Wen
Source: ACS Omega (article, 2020-10-27)
Field: Materials Science — Polymers and Plastics

Multiwalled Carbon Nanotube-N-Doped Graphene/Poly(3,4-ethylenedioxythiophene):Poly(styrenesulfonate) Nanohybrid for Electrochemical Application in Intelligent Sensors and Supercapacitors In this study, we reported the preparation of a conducting polymeric/inorganic nanohybrid consisting of multiwalled carbon nanotubes (MWCNT), N-doped graphene (NGr), and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), and its electrochemical application in intelligent sensors and supercapacitors. The multilayer thin film of the PEDOT:PSS-supported MWCNT-NGr nanohybrid was prepared by a facile layer-by-layer assembly strategy. The obtained conducting polymeric/inorganic nanohybrid modified electrode displayed superior electron transfer ability and a high specific surface area, which was used for electrochemical applications in intelligent sensors and supercapacitors. Remarkably, the fabricated amaranth sensor exhibited a broad linear range of 0.05-10 μM with a limit of detection of 0.015 μM under the optimal conditions. With the help of the response surface methodology, multivariate optimization was used as a substitute for the traditional single variable optimization to reflect the complete real effects of multivariate optimization in a sensing platform. Machine learning implemented by hybrid genetic algorithm-artificial neural network was used as an intelligent analysis model to replace the traditional regression analysis model for realizing intelligent analysis and output of sensing system. The MWCNT-NGr/PEDOT:PSS modified electrode exhibited a considerable specific capacitance of 6.5 mF cm<sup>-2</sup> at a current density of 2.0 mA cm<sup>-2</sup>. The proposed results provided a new thought for a nanosensing platform equipped with a supercapacitor as a self-powered electrochemical energy storage system and machine learning as an intelligent analysis and output system in the near future. PEDOT:PSS ; Materials science ; Supercapacitor ; Graphene ; Conductive polymer ; Poly(3,4-ethylenedioxythiophene) ; Nanotechnology ; Carbon nanotube ; Capacitance ; Electrode ; Nanotube ; Chemical engineering ; Layer (electronics) ; Polymer ; Composite material ; Chemistry Conducting polymers and applications ; Supercapacitor Materials and Fabrication ; Electrochemical sensors and biosensors
Authors: Silvia Amabilino | Lars Andersen Bratholm | Simon J. Bennie | Michael B. O’Connor | David R. Glowacki
Source: The Journal of Chemical Physics (article, 2020-10-15)
Field: Materials Science — Materials Chemistry

Training atomic neural networks using fragment-based data generated in virtual reality The ability to understand and engineer molecular structures relies on having accurate descriptions of the energy as a function of atomic coordinates. Here, we outline a new paradigm for deriving energy functions of hyperdimensional molecular systems, which involves generating data for low-dimensional systems in virtual reality (VR) to then efficiently train atomic neural networks (ANNs). This generates high-quality data for specific areas of interest within the hyperdimensional space that characterizes a molecule's potential energy surface (PES). We demonstrate the utility of this approach by gathering data within VR to train ANNs on chemical reactions involving fewer than eight heavy atoms. This strategy enables us to predict the energies of much higher-dimensional systems, e.g., containing nearly 100 atoms. Training on datasets containing only 15k geometries, this approach generates mean absolute errors around 2 kcal mol<sup>-1</sup>. This represents one of the first times that an ANN-PES for a large reactive radical has been generated using such a small dataset. Our results suggest that VR enables the intelligent curation of high-quality data, which accelerates the learning process. Virtual reality ; Artificial neural network ; Computer science ; Process (computing) ; Function (biology) ; Fragment (logic) ; Chemical space ; Energy (signal processing) ; Quality (philosophy) ; Virtual screening ; Artificial intelligence ; Machine learning ; Molecular dynamics ; Physics ; Algorithm ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: E Weinan | Jiequn Han | Linfeng Zhang
Source: arXiv (Cornell University) (preprint, 2020-06-04)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning with Physics-Based Modeling Machine learning is poised as a very powerful tool that can drastically improve our ability to carry out scientific research. However, many issues need to be addressed before this becomes a reality. This article focuses on one particular issue of broad interest: How can we integrate machine learning with physics-based modeling to develop new interpretable and truly reliable physical models? After introducing the general guidelines, we discuss the two most important issues for developing machine learning-based physical models: Imposing physical constraints and obtaining optimal datasets. We also provide a simple and intuitive explanation for the fundamental reasons behind the success of modern machine learning, as well as an introduction to the concurrent machine learning framework needed for integrating machine learning with physics-based modeling. Molecular dynamics and moment closure of kinetic equations are used as examples to illustrate the main issues discussed. We end with a general discussion on where this integration will lead us to, and where the new frontier will be after machine learning is successfully integrated into scientific modeling. Artificial intelligence ; Machine learning ; Computer science ; Computational learning theory ; Simple (philosophy) ; Active learning (machine learning) ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jonathan R. Thompson | Joshua A. Burrow | Piyush Shah | Jonathan E. Slagle | Eric S. Harper | Andre Van Rynbach | Imad Agha | Matthew S. Mills
Source: Optics Express (article, 2020-07-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial neural network discovery of a switchable metasurface reflector Optical materials engineered to dynamically and selectively manipulate electromagnetic waves are essential to the future of modern optical systems. In this paper, we simulate various metasurface configurations consisting of periodic 1D bars or 2D pillars made of the ternary phase change material Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> (GST). Dynamic switching behavior in reflectance is exploited due to a drastic refractive index change between the crystalline and amorphous states of GST. Selectivity in the reflection and transmission spectra is manipulated by tailoring the geometrical parameters of the metasurface. Due to the immense number of possible metasurface configurations, we train deep neural networks capable of exploring all possible designs within the working parameter space. The data requirements, predictive accuracy, and robustness of these neural networks are benchmarked against a ground truth by varying quality and quantity of training data. After ensuring trustworthy neural network advisory, we identify and validate optimal GST metasurface configurations best suited as dynamic switchable mirrors depending on selected light and manufacturing constraints. Artificial neural network ; Robustness (evolution) ; Computer science ; Optics ; Refractive index ; Reflection (computer programming) ; Ternary operation ; Transmission (telecommunications) ; Materials science ; Artificial intelligence ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Phase-change materials and chalcogenides ; Photonic Crystals and Applications
Authors: Shin Kiyohara | Masashi Tsubaki | Teruyasu Mizoguchi
Source: npj Computational Materials (article, 2020-06-03)
Field: Materials Science — Materials Chemistry

Learning excited states from ground states by using an artificial neural network Abstract Excited states are different quantum states from their ground states, and spectroscopy methods that can assess excited states are widely used in materials characterization. Understanding the spectra reflecting excited states is thus of great importance for materials science. However, understanding such spectra remains difficult because excited states have usually different atomic or electronic configurations from their corresponding ground states. If excited states could be predicted from ground states, the knowledge of the excited states would be improved. Here, we used an artificial neural network to predict the excited states of the core-electron absorption spectra from their ground states. Consequently, our model correctly learned and predicted the excited states from their ground states, providing several thousand times computational efficiency. Furthermore, it showed excellent transferability to other materials. Also, we found two physical insights about excited states: core-hole effects of amorphous silicon oxides are stronger than those of crystalline silicon oxides, and the excited-ground states relationships of some metal oxides are similar to those of the silicon oxides, which could not be obtained by conventional spectral simulation nor found until using machine leaning. Excited state ; Spectral line ; Atomic physics ; Spectroscopy ; Materials science ; Ground state ; Physics ; Quantum mechanics Electronic and Structural Properties of Oxides ; Semiconductor materials and devices ; Machine Learning in Materials Science