jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yupeng Diao | Luchun Yan | Kewei Gao
Source: Materials & Design (article, 2020-11-12)
Field: Materials Science — Materials Chemistry

Improvement of the machine learning-based corrosion rate prediction model through the optimization of input features The corrosion resistance of low-alloy steel seriously influences its performance, particularly as a class of materials widely used in marine environments. In this study, we collected the marine corrosion data of low-alloy steels and established corrosion rate prediction models with machine learning algorithms. Both the chemical composition of low-alloy steel and environmental factors were used as input features, and the random forest algorithm was selected as the modeling algorithm. Feature reduction methods, including the gradient boosting decision tree, Kendall correlation analysis and principal component analysis, were first applied to select the dominating factors on the corrosion rate. Then, we proposed two feature creation methods to convert the chemical composition features into a set of atomic and physical property features. As a result, the feature creation method crafted a model no longer limited to materials with specific chemical compositions. The machine learning-based corrosion rate prediction model also showed good prediction accuracy of the corrosion rate. This study improved the generalization ability of the corrosion rate prediction model and proved the feasibility of machine learning in corrosion resistance evaluation. Corrosion ; Random forest ; Materials science ; Machine learning ; Decision tree ; Predictive modelling ; Principal component analysis ; Artificial intelligence ; Gradient boosting ; Generalization ; Computer science ; Metallurgy ; Mathematics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses
Authors: Nasim Sabahi | Wenliang Chen | Chunhui Wang | Jamie J. Kruzic | Xiaopeng Li
Source: JOM (review, 2020-01-23)
Field: Materials Science — Polymers and Plastics

A Review on Additive Manufacturing of Shape-Memory Materials for Biomedical Applications Shape-memory alloy ; Nanotechnology ; Shape-memory polymer ; Computer science ; Materials science ; Artificial intelligence Polymer composites and self-healing ; Shape Memory Alloy Transformations ; Additive Manufacturing and 3D Printing Technologies
Authors: Zhiming Cheng | Erkin Kuru | Amit Sachdeva | Marc Vendrell
Source: Nature Reviews Chemistry (review, 2020-05-13)
Field: Chemistry — Organic Chemistry

Fluorescent amino acids as versatile building blocks for chemical biology Amino acid ; Fluorescence ; Bioorthogonal chemistry ; Fluorescent protein ; Chemical biology ; Synthetic biology ; Protein engineering ; Nanotechnology ; Peptide ; Computational biology ; Chemistry ; Combinatorial chemistry ; Biochemistry ; Green fluorescent protein ; Biology ; Materials science ; Click chemistry ; Gene ; Enzyme Click Chemistry and Applications ; Advanced biosensing and bioanalysis techniques ; bioluminescence and chemiluminescence research
Authors: Haining Pan | Fengcheng Wu | Sankar Das Sarma
Source: Physical Review Research (article, 2020-07-16)
Field: Materials Science — Materials Chemistry

Band topology, Hubbard model, Heisenberg model, and Dzyaloshinskii-Moriya interaction in twisted bilayer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>WSe</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> We present a theoretical study of single-particle and many-body properties of twisted bilayer WSe 2 . For single-particle physics, we calculate the band topological phase diagram and electron local density of states (LDOS), which are found to be correlated. By comparing our theoretical LDOS with those measured by scanning tunneling microscopy, we comment on the possible topological nature of the first moir valence band. For many-body physics, we construct a generalized Hubbard model on a triangular lattice based on the calculated single-particle moir bands. We show that a layer potential difference, arising, for example, from an applied electric field, can drastically change the noninteracting moir bands, tune the spin-orbit coupling in the Hubbard model, control the charge excitation gap of the Mott insulator at half-filling, and generate an effective Dzyaloshinskii-Moriya interaction in the effective Heisenberg model for the Mott insulator. Our theoretical results agree with transport experiments on the same system in several key aspects, and establish twisted bilayer WSe 2 as a highly tunable system for studying and simulating strongly correlated phenomena in the Hubbard model. Hubbard model ; Phase diagram ; Mott insulator ; Lattice (music) ; Bilayer ; Heisenberg model ; Quantum tunnelling ; Band gap ; Excitation 2D Materials and Applications ; Topological Materials and Phenomena ; Electronic and Structural Properties of Oxides
Authors: Juhwan Noh | Geun Ho Gu | Sungwon Kim | Yousung Jung
Source: Chemical Science (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine-enabled inverse design of inorganic solid materials: promises and challenges Developing high-performance advanced materials requires a deeper insight and search into the chemical space. Until recently, exploration of materials space using chemical intuitions built upon existing materials has been the general strategy, but this direct design approach is often time and resource consuming and poses a significant bottleneck to solve the materials challenges of future sustainability in a timely manner. To accelerate this conventional design process, inverse design, which outputs materials with pre-defined target properties, has emerged as a significant materials informatics platform in recent years by leveraging hidden knowledge obtained from materials data. Here, we summarize the latest progress in machine-enabled inverse materials design categorized into three strategies: high-throughput virtual screening, global optimization, and generative models. We analyze challenges for each approach and discuss gaps to be bridged for further accelerated and rational data-driven materials design. Inverse ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Biochemical engineering ; Engineering ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Xiujuan Zou | Gaige Zheng | Quan Yuan | Wenbo Zang | Run Chen | Tianyue Li | Lin Li | Shuming Wang | Zhenlin Wang | Shining Zhu
Source: PhotoniX (article, 2020-03-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Imaging based on metalenses Abstract Metalens, a prominent application of two-dimensional metasurfaces, has demonstrated powerful abilities even beyond traditional optical lenses. By manipulating the phase distribution of metalens composed of appropriately arranged nanoscale building blocks, the wavefront of incident wave can be controlled based on Huygens principle, thus achieving the desired reflected and transmitted wave for many different purposes. Metalenses will lead a revolution in optical imaging due to its flat nature and compact size, multispectral acquisition and even off-axis focusing. Here, we review the recent progress of metalenses presenting excellent properties, with a focus on the imaging application using these metalenses. We firstly discuss the mechanism for achieving metalenses with high efficiency, large numerical aperture, controlling the chromatic dispersion or monochromatic aberrations and large area fabrication. Then, we review several important imaging applications including wide-band focusing imaging, polarization dependent imaging, light field imaging and some other significant imaging systems in different areas. Finally, we make a conclusion with an outlook on the future development and challenges of this developing research field. Wavefront ; Optics ; Monochromatic color ; Polarization (electrochemistry) ; Multispectral image ; Computer science ; Physics ; Computer vision Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Johannes Klicpera | Janek Groß | Stephan Günnemann
Source: arXiv (Cornell University) (article, 2020-03-06)
Field: Materials Science — Materials Chemistry

Directional Message Passing for Molecular Graphs Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes) and not the spatial direction from one atom to another. However, directional information plays a central role in empirical potentials for molecules, e.g. in angular potentials. To alleviate this limitation we propose directional message passing, in which we embed the messages passed between atoms instead of the atoms themselves. Each message is associated with a direction in coordinate space. These directional message embeddings are rotationally equivariant since the associated directions rotate with the molecule. We propose a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them. Additionally, we use spherical Bessel functions to construct a theoretically well-founded, orthogonal radial basis that achieves better performance than the currently prevalent Gaussian radial basis functions while using more than 4x fewer parameters. We leverage these innovations to construct the directional message passing neural network (DimeNet). DimeNet outperforms previous GNNs on average by 77% on MD17 and by 41% on QM9. Message passing ; Computer science ; Leverage (statistics) ; Gaussian ; Theoretical computer science ; Graph ; Belief propagation ; Topology (electrical circuits) ; Algorithm ; Mathematics ; Physics ; Decoding methods ; Artificial intelligence ; Combinatorics ; Distributed computing ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sean S. Nguyen | Jennifer A. Prescher
Source: Nature Reviews Chemistry (review, 2020-07-21)
Field: Chemistry — Organic Chemistry

Developing bioorthogonal probes to span a spectrum of reactivities Bioorthogonal chemistry ; Biocompatible material ; Nanotechnology ; Biomolecule ; Computer science ; Living systems ; Variety (cybernetics) ; Chemistry ; Broad spectrum ; Biochemical engineering ; Combinatorial chemistry ; Click chemistry ; Materials science ; Engineering ; Artificial intelligence Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Monoclonal and Polyclonal Antibodies Research
Authors: Sicong Ma | Zhi‐Pan Liu
Source: ACS Catalysis (article, 2020-10-30)
Field: Materials Science — Materials Chemistry

Machine Learning for Atomic Simulation and Activity Prediction in Heterogeneous Catalysis: Current Status and Future Heterogeneous catalysis, for its industrial importance and great complexity in structure, has long been the testing ground of new characterization techniques. Machine learning (ML) as a starring tool in data science brings new opportunities for chemists to interpret, simulate, and predict complex reactions in heterogeneous catalysis. Here we review the current status of ML methods and applications in heterogeneous catalysis by following two main streams: the top-down approach by learning experiment data and the bottom-up approach for making predictions from first-principles, which differ in the data source. We focus more on the latter, where ML interacts intimately with first-principles calculations for predicting the key properties (e.g., molecular adsorption energy) and evaluating potential energy surface (PES) to expedite the atomic simulation. The ML-based PES exploration represents the top gear that can largely replace the traditional roles of first-principles calculations for structure determination and activity evaluation but requires efficient methods for data set generation, sensitive structure descriptors to discriminate structures, and iterative self-learning to refine the ML potential. We illustrate these key ingredients of ML-based atomic simulation using the SSW-NN method developed by our group as the example. Three cases of SSW-NN application are presented to elaborate how ML can expedite the material and reaction simulation and lead to new findings on catalyst structure and reaction channels. The future directions of ML-based applications in heterogeneous catalysis are also discussed. Computer science ; Set (abstract data type) ; Catalysis ; Characterization (materials science) ; Biochemical engineering ; Key (lock) ; Heterogeneous catalysis ; Chemistry ; Machine learning ; Nanotechnology ; Artificial intelligence ; Materials science ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Lihua Chen | Chiho Kim | Rohit Batra | Jordan P. Lightstone | Chao Wu | Zongze Li | Ajinkya A. Deshmukh | Yifei Wang | Tran Doan Huan | Priya Vashishta | Gregory A. Sotzing | Yang Cao | Rampi Ramprasad
Source: npj Computational Materials (article, 2020-05-21)
Field: Materials Science — Materials Chemistry

Frequency-dependent dielectric constant prediction of polymers using machine learning Abstract The dielectric constant ( ϵ ) is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors, microelectronic devices, and high-voltage insulations. However, agile discovery of polymer dielectrics with desirable ϵ remains a challenge, especially for high-energy, high-temperature applications. To aid accelerated polymer dielectrics discovery, we have developed a machine-learning (ML)-based model to instantly and accurately predict the frequency-dependent ϵ of polymers with the frequency range spanning 15 orders of magnitude. Our model is trained using a dataset of 1210 experimentally measured ϵ values at different frequencies, an advanced polymer fingerprinting scheme and the Gaussian process regression algorithm. The developed ML model is utilized to predict the ϵ of synthesizable 11,000 candidate polymers across the frequency range 60–10 15 Hz, with the correct inverse ϵ vs. frequency trend recovered throughout. Furthermore, using ϵ and another previously studied key design property (glass transition temperature, T g ) as screening criteria, we propose five representative polymers with desired ϵ and T g for capacitors and microelectronic applications. This work demonstrates the use of surrogate ML models to successfully and rapidly discover polymers satisfying single or multiple property requirements for specific applications. Microelectronics ; Dielectric ; Capacitor ; Materials science ; Polymer ; Range (aeronautics) ; Computer science ; Inverse ; Electronic engineering ; Voltage ; Optoelectronics ; Electrical engineering ; Mathematics ; Composite material ; Engineering Machine Learning in Materials Science ; Dielectric materials and actuators ; Fuel Cells and Related Materials
Authors: Jinhai Mao | Slaviša P. Milovanović | Miša Anđelković | Xinyuan Lai | Yang Cao | Kenji Watanabe | Takashi Taniguchi | Lucian Covaci | Francois M. Peeters | Andre K. Geim | Yuhang Jiang | Eva Y. Andrei
Source: Nature (article, 2020-08-12)
Field: Materials Science — Materials Chemistry

Evidence of flat bands and correlated states in buckled graphene superlattices Superlattice ; Graphene ; Bilayer graphene ; Monolayer ; Fermi energy ; Quantum tunnelling ; Scanning tunneling microscope ; Substrate (aquarium) Graphene research and applications ; Topological Materials and Phenomena ; Surface and Thin Film Phenomena
Authors: Alain C. Vaucher | Federico Zipoli | Joppe Geluykens | Vishnu H Nair | Philippe Schwaller | Teodoro Laino
Source: Nature Communications (article, 2020-07-17)
Field: Materials Science — Materials Chemistry

Automated extraction of chemical synthesis actions from experimental procedures Experimental procedures for chemical synthesis are commonly reported in prose in patents or in the scientific literature. The extraction of the details necessary to reproduce and validate a synthesis in a chemical laboratory is often a tedious task requiring extensive human intervention. We present a method to convert unstructured experimental procedures written in English to structured synthetic steps (action sequences) reflecting all the operations needed to successfully conduct the corresponding chemical reactions. To achieve this, we design a set of synthesis actions with predefined properties and a deep-learning sequence to sequence model based on the transformer architecture to convert experimental procedures to action sequences. The model is pretrained on vast amounts of data generated automatically with a custom rule-based natural language processing approach and refined on manually annotated samples. Predictions on our test set result in a perfect (100%) match of the action sequence for 60.8% of sentences, a 90% match for 71.3% of sentences, and a 75% match for 82.4% of sentences. Computer science ; Transformer ; Sequence (biology) ; Natural language processing ; Set (abstract data type) ; Artificial intelligence ; Task (project management) ; Test set ; Sequence labeling ; Programming language ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Ryota Shimizu | Shigeru Kobayashi | Yuki Watanabe | Yasunobu Ando | Taro Hitosugi
Source: APL Materials (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Autonomous materials synthesis by machine learning and robotics Future materials-science research will involve autonomous synthesis and characterization, requiring an approach that combines machine learning, robotics, and big data. In this paper, we highlight our recent experiments in autonomous synthesis and resistance minimization of Nb-doped TiO2 thin films. Combining Bayesian optimization with robotics, these experiments illustrate how the required speed and volume of future big-data collection in materials science will be achieved and demonstrate the tremendous potential of this combined approach. We briefly discuss the outlook and significance of these results and advances. Robotics ; Artificial intelligence ; Big data ; Bayesian optimization ; Machine learning ; Computer science ; Data science ; Nanotechnology ; Materials science ; Robot ; Data mining Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: JongCheol Pyo | Seok Min Hong | Yong Sung Kwon | Moon S. Kim | Kyung Hwa Cho
Source: The Science of The Total Environment (article, 2020-06-18)
Field: Chemistry — Analytical Chemistry

Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil Autoencoder ; Convolutional neural network ; Principal component analysis ; Dimensionality reduction ; Artificial intelligence ; Random forest ; Partial least squares regression ; Diffuse reflectance infrared fourier transform ; Pattern recognition (psychology) ; Deep learning ; Environmental science ; Computer science ; Chemistry ; Machine learning Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Soil Geostatistics and Mapping
Authors: Dmitrii V. Semenok | Ivan A. Kruglov | Igor A. Savkin | Alexander G. Kvashnin | Artem R. Oganov
Source: Current Opinion in Solid State and Materials Science (article, 2020-04-01)
Field: Materials Science — Materials Chemistry

On Distribution of Superconductivity in Metal Hydrides Superconductivity ; Metal ; Distribution (mathematics) ; Materials science ; Condensed matter physics ; Metallurgy ; Physics ; Mathematics Hydrogen Storage and Materials ; Quantum, superfluid, helium dynamics ; Rare-earth and actinide compounds
Authors: Jon Paul Janet | Sahasrajit Ramesh | Chenru Duan | Heather J. Kulik
Source: ACS Central Science (article, 2020-03-11)
Field: Materials Science — Materials Chemistry

Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization The accelerated discovery of materials for real world applications requires the achievement of multiple design objectives. The multidimensional nature of the search necessitates exploration of multimillion compound libraries over which even density functional theory (DFT) screening is intractable. Machine learning (e.g., artificial neural network, ANN, or Gaussian process, GP) models for this task are limited by training data availability and predictive uncertainty quantification (UQ). We overcome such limitations by using efficient global optimization (EGO) with the multidimensional expected improvement (EI) criterion. EGO balances exploitation of a trained model with acquisition of new DFT data at the Pareto front, the region of chemical space that contains the optimal trade-off between multiple design criteria. We demonstrate this approach for the simultaneous optimization of redox potential and solubility in candidate M(II)/M(III) redox couples for redox flow batteries from a space of 2.8 M transition metal complexes designed for stability in practical redox flow battery (RFB) applications. We show that a multitask ANN with latent-distance-based UQ surpasses the generalization performance of a GP in this space. With this approach, ANN prediction and EI scoring of the full space are achieved in minutes. Starting from ca. 100 representative points, EGO improves both properties by over 3 standard deviations in only five generations. Analysis of lookahead errors confirms rapid ANN model improvement during the EGO process, achieving suitable accuracy for predictive design in the space of transition metal complexes. The ANN-driven EI approach achieves at least 500-fold acceleration over random search, identifying a Pareto-optimal design in around 5 weeks instead of 50 years. Chemical space ; Computer science ; Artificial neural network ; Surrogate model ; Artificial intelligence ; Machine learning ; Mathematical optimization ; Mathematics ; Chemistry Machine Learning in Materials Science ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Bing He | Shuting Chi | Anjiang Ye | Penghui Mi | Liwen Zhang | Bowei Pu | Zheyi Zou | Yunbing Ran | Qian Zhao | Da Wang | Wenqing Zhang | Jing‐Tai Zhao | Stefan Adams | Maxim Avdeev | Siqi Shi
Source: Scientific Data (article, 2020-05-21)
Field: Materials Science — Materials Chemistry

High-throughput screening platform for solid electrolytes combining hierarchical ion-transport prediction algorithms The combination of a materials database with high-throughput ion-transport calculations is an effective approach to screen for promising solid electrolytes. However, automating the complicated preprocessing involved in currently widely used ion-transport characterization algorithms, such as the first-principles nudged elastic band (FP-NEB) method, remains challenging. Here, we report on high-throughput screening platform for solid electrolytes (SPSE) that integrates a materials database with hierarchical ion-transport calculations realized by implementing empirical algorithms to assist in FP-NEB completing automatic calculation. We first preliminarily screen candidates and determine the approximate ion-transport paths using empirical both geometric analysis and the bond valence site energy method. A chain of images are then automatically generated along these paths for accurate FP-NEB calculation. In addition, an open web interface is actualized to enable access to the SPSE database, thereby facilitating machine learning. This interactive platform provides a workflow toward high-throughput screening for future discovery and design of promising solid electrolytes and the SPSE database is based on the FAIR principles for the benefit of the broad research community. Computer science ; Workflow ; Algorithm ; Preprocessor ; Throughput ; Database ; Artificial intelligence ; Wireless Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Zeolite Catalysis and Synthesis