jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Victorien Delannée | Marc C. Nicklaus
Source: ChemRxiv (preprint, 2020-04-08)
Field: Chemistry — Spectroscopy

ReactionCode: a new versatile format for searching, analysis, classification, transform, and encoding/decoding of reactions In the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts have been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we developed a new open-source format which allows to encode and decode a reaction into multi-layers machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language. Computer science ; Identifier ; Decoding methods ; ENCODE ; Context (archaeology) ; Encoding (memory) ; Representation (politics) ; Graph ; Information retrieval ; Theoretical computer science ; Artificial intelligence ; Programming language ; Algorithm ; Chemistry Analytical Chemistry and Chromatography ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Maoyi Zhang
Source: Journal of Physics Conference Series (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Main Components and Neuroanalysis of Automatic Steel Water Deoxygenation Alloy Ingredients Abstract At present, how to establish a mathematical model of deoxygenation alloying link through historical data, predict and optimize the type and quantity of alloy input online, and minimize the production cost of alloy steel while ensuring the quality of steel and water. It is an important issue for major steel companies to improve their competitiveness. Based on the main component and neural network and multivariate nonlinear regression theory, based on the existing alloy historical data, this paper optimizes the ingredients scheme of "deoxygenation and alloying" of steel and water, establishes the mathematical model, calculates the historical harvest rate of the main elements, and predicts the historical harvest rate of the main elements. Combined with the price of the alloy, the automatic ingredient scheme of the steel water deoxygenated alloy is given, and the cost of the steel water deoxygenated alloy is optimized. Deoxygenation ; Alloy ; Alloy steel ; Component (thermodynamics) ; Artificial neural network ; Multivariate statistics ; Computer science ; Materials science ; Metallurgy ; Artificial intelligence ; Machine learning ; Thermodynamics ; Chemistry Material Properties and Failure Mechanisms ; Metallurgical Processes and Thermodynamics
Authors: Wataru Hashimoto | Yuta Tsuji | Kazunari Yoshizawa
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Optimization of Work Function via Bayesian Machine Learning Combined with First-Principles Calculation Work function is one of the most fundamental and important physical quantities in surface science. Materials with either lower work function or higher work function would find various applications, such as electronic devices and high-performance catalysts. However, it would be challenging to find a material with the optimal work function exploiting the all-search or random approach, whether it is based on an experimental or theoretical method. In this paper, we use the Bayesian optimization (BO) approach, which is one of the most powerful machine-learning tools for optimization, in order to effectively explore a candidate material with a higher or lower work function value out of hundreds of thousands of materials registered in a material database. We introduce a quick measure of the work function based on the depth of the Fermi level calculated from the first-principles computation for the crystalline bulk structure of a material. We call this the approximate work function, treating it as the objective function of our BO scheme. Since we do not need any time-consuming surface calculation with the slab model for the evaluation of the approximate work function, a quick search of a material with the highest or the lowest work function is achieved. As input variables for our BO implementation, we employ some bulk-specific properties of materials, which can be fetched from the database. The demonstration of our BO-based exploration of the database shows that materials with both low and high limits of the approximate work function can be discovered more efficiently in BO than a random exploration. The top 10 lowest work function materials thus found are in line with our chemical intuition in that all of them include either alkali or alkaline earth metal. On the other hand, we found the top 10 highest work function materials with amazement because they also include either alkali or alkaline earth metal and a lanthanide element. Work function ; Function (biology) ; Work (physics) ; Bayesian optimization ; Measure (data warehouse) ; Computer science ; Mathematical optimization ; Chemistry ; Machine learning ; Mechanical engineering ; Database ; Mathematics ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Yoichi Kurumida | Keisuke Ikeda | Yusuke Nakamichi | Kaito Kobayashi | Yutaka Saitō | Tomoshi Kameda
Source: Biophysical Journal (article, 2020-02-01)
Field: Materials Science — Biomaterials

Evaluating Self-Assembly Propensity of Tetra-Peptide using MD and Machine Learning Peptide ; Tetra ; Molecular dynamics ; Chemistry ; Artificial intelligence ; Computer science ; Computational biology ; Machine learning ; Biological system ; Bioinformatics ; Biochemistry ; Biology ; Computational chemistry Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Machine Learning in Bioinformatics
Authors: S. V. Barabash
Source: MRS Advances (article, 2020-04-13)
Field: Materials Science — Materials Chemistry

Accelerating Development of Materials for Industrial and High-Tech Applications with Data-Driven Analysis and Simulations Identification (biology) ; Computer science ; Stability (learning theory) ; Stack (abstract data type) ; Development (topology) ; Materials science ; Nanotechnology ; Biochemical engineering ; Supply chain ; Systems engineering ; Process engineering ; Machine learning Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Jiadong Dan | Xiaoxu Zhao | Shoucong Ning | Jiong Lu | Kian Ping Loh | N. Duane Loh | Stephen J. Pennycook
Source: arXiv (Cornell University) (preprint, 2020-05-23)
Field: Materials Science — Materials Chemistry

A hierarchical active-learning framework for classifying structural motifs in atomic resolution microscopy Progress in functional materials discovery has been accelerated by advances in high throughput materials synthesis and by the development of high-throughput computation. However, a complementary robust and high throughput structural characterization framework is still lacking. New methods and tools in the field of machine learning suggest that a highly automated high-throughput structural characterization framework based on atomic-level imaging can establish the crucial statistical link between structure and macroscopic properties. Here we develop a machine learning framework towards this goal. Our framework captures local structural features in images with Zernike polynomials, which is demonstrably noise-robust, flexible, and accurate. These features are then classified into readily interpretable structural motifs with a hierarchical active learning scheme powered by a novel unsupervised two-stage relaxed clustering scheme. We have successfully demonstrated the accuracy and efficiency of the proposed methodology by mapping a full spectrum of structural defects, including point defects, line defects, and planar defects in scanning transmission electron microscopy (STEM) images of various 2D materials, with greatly improved separability over existing methods. Our techniques can be easily and flexibly applied to other types of microscopy data with complex features, providing a solid foundation for automatic, multiscale feature analysis with high veracity. Computer science ; Artificial intelligence ; Throughput ; Cluster analysis ; Characterization (materials science) ; Computation ; Visualization ; Feature (linguistics) ; Pattern recognition (psychology) ; Machine learning ; Nanotechnology ; Algorithm ; Materials science Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: JI Xiuyi, LI Jianhua
Source: SHILAP Revista de lepidopterología (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

Chemical Structure Image Recognition Based on Dual Attention Mechanism Most of the existing chemical structure image recognition methods based on traditional image processing techniques and pipeline methods usually rely on artificially designed features,resulting in a low recognition accuracy.To solve the problem,this paper proposes a chemical structure image recognition method based on spatial attention mechanism and channel attention mechanism.The method simplifies the recognition of chemical structure to a sequence generation task,and adopts a deep neural network model combining Convolutional Neural Network(CNN) and Long Short-Term Memory(LSTM) network to implement the transformation from chemical structure images to the SMILES sequence.The deep neural network model is composed of the encoder and the decoder.The encoder uses CNN to extract features of chemical structure images,and the decoder combines the two attention mechanisms with LSTM to generate SMILES sequences.Experimental results show that the proposed method improves the recognition accuracy to 81.63% and the BLEU-4 value to 0.937 under the condition that Beam Size equals 3,outperforming the chemical structure image recognition methods without attention mechanism or with a single attention mechanism. Pattern recognition (psychology) ; Artificial neural network ; Encoder ; Convolutional neural network ; Image (mathematics) ; Channel (broadcasting) ; Deep learning ; Pipeline (software) Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: Dakota Folmsbee | David Ryan Koes | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2020-11-10)
Field: Materials Science — Materials Chemistry

Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization While many machine learning methods, particularly deep neural networks have been trained for density functional and quantum chemical energies and properties, the vast majority of these methods focus on single-point energies. In principle, such ML methods, once trained, offer thermochemical accuracy on par with density functional and wave function methods but at speeds comparable to traditional force fields or approximate semiempirical methods. So far, most efforts have focused on optimized equilibrium single-point energies and properties. In this work, we evaluate the accuracy of several leading ML methods across a range of bond potential energy curves and torsional potentials. Methods were trained on the existing ANI-1 training set, calculated using the ωB97X / 6-31G(d) single points at non-equilibrium geometries. We find that across a range of small molecules, several methods offer both qualitative accuracy (e.g., correct minima, both repulsive and attractive bond regions, anharmonic shape, and single minima) and quantitative accuracy in terms of the mean absolute percent error near the minima. At the moment, ANI-2x, FCHL, and our new grid-based convolutional neural net show good performance. Maxima and minima ; Anharmonicity ; Artificial neural network ; Potential energy ; Convolutional neural network ; Range (aeronautics) ; Grid ; Work (physics) ; Hyperparameter optimization ; Density functional theory ; Quantum ; Energy minimization ; Focus (optics) ; Computer science ; Statistical physics ; Physics ; Artificial intelligence ; Mathematics ; Geometry ; Materials science ; Quantum mechanics ; Mathematical analysis ; Optics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Liangrui Pan | Pronthep Pipitsunthonsan | Chalongrat Daengngam | Mitchai Chongcheawchamnan
Source: arXiv (Cornell University) (preprint, 2020-10-29)
Field: Chemistry — Analytical Chemistry

Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network With noisy environment caused by fluoresence and additive white noise as well as complicated spectrum fingerprints, the identification of complex mixture materials remains a major challenge in Raman spectroscopy application. In this paper, we propose a new scheme based on a constant wavelet transform (CWT) and a deep network for classifying complex mixture. The scheme first transforms the noisy Raman spectrum to a two-dimensional scale map using CWT. A multi-label deep neural network model (MDNN) is then applied for classifying material. The proposed model accelerates the feature extraction and expands the feature graph using the global averaging pooling layer. The Sigmoid function is implemented in the last layer of the model. The MDNN model was trained, validated and tested with data collected from the samples prepared from substances in palm oil. During training and validating process, data augmentation is applied to overcome the imbalance of data and enrich the diversity of Raman spectra. From the test results, it is found that the MDNN model outperforms previously proposed deep neural network models in terms of Hamming loss, one error, coverage, ranking loss, average precision, F1 macro averaging and F1 micro averaging, respectively. The average detection time obtained from our model is 5.31 s, which is much faster than the detection time of the previously proposed models. Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Chaochao Yan | Qianggang Ding | Peilin Zhao | Shuangjia Zheng | Jinyu Yang | Yang Yu | Junzhou Huang
Source: ChemRxiv (preprint, 2020-02-24)
Field: Materials Science — Materials Chemistry

Interpretable Retrosynthesis Prediction in Two Steps Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and lacks interpretability behind their predictions. In this study, we devise a novel template-free model for retrosynthetic expansion by automating the procedure that chemistsusedtodo. Our method plans synthesis in two steps, by ﬁrst identifying the potential disconnection bonds of the molecule graph with a graph neural network and thereafter generating synthons according to the identiﬁed disconnection bonds of the target molecule graph, and then predicting the associated reactants SMILES based on the obtained synthons with a reactant prediction model. While outperforming previous state-of-the-art baselines by a signiﬁcant margin on the benchmark datasets, our model also provides predictions with high diversity and chemically reasonable interpretation. Retrosynthetic analysis ; Interpretability ; Computer science ; Artificial intelligence ; Benchmark (surveying) ; Graph ; Margin (machine learning) ; Interpretation (philosophy) ; Machine learning ; Theoretical computer science ; Programming language ; Chemistry ; Cartography ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Alexander Winckelmann
Source:  (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

High-resolution optical spectrometry for lithium isotope ratio analysis An alternative method for lithium isotope amount ratio analysis is proposed by combining atomic absorption spectrometry with spectra data analysis by machine leaning. It is based on the well-known isotope shift of around 15 pm for the electronic transition at wavelength 670.7845 nm which can be measured by a high-resolution continuum source atomic absorption spectrometer (HR-CS-AAS). For isotope amount ratio analysis, a scalable three boosting machine learning algorithm (XGBoost) was employed and calibrated with a set of samples with a 6Li isotope amount fraction ranging from 99% to 6%. The absolute Li isotope amount fractions of these calibration samples were previously measured by multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) and used as ab-initio data for the machine learning algorithm. Validation of the machine leaning model was performed with two standard reference materials (LSVEC and IRMM-016). The procedure was employed for the isotope amount ratio determination of a set of stock chemicals (Li2CO3, LiNO3, LiCl, LiOH, and LiF) as well as a BAM candidate LiMNC cathode reference material. Achieved uncertainties are one order of magnitude higher than those obtained by MC-ICP-MS. This precision and accuracy is nonetheless sufficient to resolve natural occurring variations in Lithium isotope ratios. Also, the LiMNC material was analyzed by HR-CS-AAS with and without matrix purification. The results are comparable within statistical error. Analytical Chemistry (journal) ; Chemistry ; Isotope ; Mass spectrometry ; Chromatography ; Physics Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications ; Isotope Analysis in Ecology
Authors: Yu Kitamura | Emi Terado | Zechen Zhang | Hirofumi Yoshikawa | Tomoko Inose | Hiroshi Uji‐i | Masaharu Tanimizu | Akihiro Inokuchi | Yoshinobu Kamakura | Daisuke Tanaka
Source: ChemRxiv (preprint, 2020-12-29)
Field: Chemistry — Inorganic Chemistry

Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10−4 S cm−1 for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%). Lanthanide ; Materials science ; Adsorption ; Metal-organic framework ; Porosity ; Reproducibility ; Metal ; Chromatography ; Chemistry ; Composite material ; Organic chemistry ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Organic and Molecular Conductors Research
Authors: Martin P. Bircher | Andreas Singraber | Christoph Dellago
Source: arXiv (Cornell University) (preprint, 2020-10-27)
Field: Materials Science — Materials Chemistry

Improved Description of Atomic Environments using Low-cost Polynomial\n Functions with Compact Support The prediction of chemical properties using Machine Learning (ML) techniques\ncalls for a set of appropriate descriptors that accurately describe atomic and,\non a larger scale, molecular environments. A mapping of conformational\ninformation on a space spanned by atom-centred symmetry functions (SF) has\nbecome a standard technique for energy and force predictions using\nhigh-dimensional neural network potentials (HDNNP). Established atom-centred\nSFs, however, are limited in their flexibility, since their functional form\nrestricts the angular domain that can be sampled. Here, we introduce a class of\natom-centred symmetry functions based on polynomials with compact support\ncalled polynomial symmetry functions (PSF), which enable a free choice of both,\nthe angular and the radial domain covered. We demonstrate that the accuracy of\nPSFs is either on par or considerably better than that of conventional,\natom-centred SFs, with reductions in force prediction errors over a test set\napproaching 50% for certain organic molecules. Contrary to established\natom-centred SFs, computation of PSF does not involve any exponentials, and\ntheir intrinsic compact support supersedes use of separate cutoff functions.\nMost importantly, the number of floating point operations required to compute\npolynomial SFs introduced here is considerably lower than that of other SFs,\nenabling their efficient implementation without the need of highly optimised\ncode structures or caching, with speedups with respect to other\nstate-of-the-art SFs reaching a factor of 4.5 to 5. This low-effort performance\nbenefit substantially simplifies their use in new programs and emerging\nplatforms such as graphical processing units (GPU). Overall, polynomial SFs\nwith compact support improve accuracy of both, energy and force predictions\nwith HDNNPs while enabling significant speedups with respect to their\nwell-established counterparts.\n Polynomial ; Atom (system on chip) ; Computer science ; Symmetry (geometry) ; Set (abstract data type) ; Flexibility (engineering) ; Function (biology) ; Domain (mathematical analysis) ; Algorithm ; Topology (electrical circuits) ; Mathematics ; Geometry ; Mathematical analysis ; Parallel computing ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics