jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shoichi Ishida | Kei Terayama | Ryosuke Kojima | Kiyosei Takasu | Yasushi Okuno
Source: ChemRxiv (preprint, 2019-07-01)
Field: Materials Science — Materials Chemistry

Prediction and Interpretable Visualization of Synthetic Reactions Using Graph Convolutional Networks &lt;div&gt;Recently, many research groups have been addressing data-driven approaches for reaction prediction and retrosynthetic analysis. Although the performances of the data-driven approach have progressed due to recent advances of machine learning and deep learning techniques, problems such as improving capability of reaction prediction and the black-box problem of neural networks still persist for practical use by chemists. To expand data-driven approaches to chemists, we focused on two challenges: improvement of reaction prediction and interpretability of the prediction. In this paper, we propose an interpretable prediction framework using Graph Convolutional Networks (GCN) for reaction prediction and Integrated Gradients (IGs) for visualization of contributions to the prediction to address these challenges. As a result, our model showed better performances than the approach using Extended-Connectivity Fingerprint (ECFP). Furthermore, IGs based visualization of the GCN prediction successfully highlighted reaction-related atoms.&lt;/div&gt; Interpretability ; Visualization ; Computer science ; Graph ; Convolutional neural network ; Machine learning ; Artificial intelligence ; Deep learning ; Graph drawing ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Hiroyuki Tahara | Rudolf Jason Kwaria | T. Hayashi
Source: Vacuum and Surface Science (article, 2019-03-09)
Field: Materials Science — Materials Chemistry

Data-driven Design of Protein- and Cell-resistant Surfaces : A Challenge to Design Biomaterials Using Materials Informatics In this review, we present the current situation of designs of biomaterials using techniques of informatics. In particular, we discuss the prediction the responses of proteins and cells toward materials and data-driven design of new biomaterials. We introduce our recent work, in which we analyzed the correlation among chemical structures of molecules constituting self-assembled monolayers (SAMs), amounts of adsorbed protein, a density of adhered platelets by machine learning using an artificial neural network model. The main conclusion is that the quality of the database is a critical factor in determining the accuracy of the prediction and material design. We also discuss technical issues to develop databases efficiently and systematically to expand the possibility of data-driven strategies to design biomaterials. Informatics ; Materials informatics ; Computer science ; Rational design ; Nanotechnology ; Systems engineering ; Materials science ; Engineering ; Health informatics ; Engineering informatics Machine Learning in Materials Science ; Nanofabrication and Lithography Techniques ; 3D Printing in Biomedical Research
Authors: Huziel E. Sauceda | Stefan Chmiela | Igor Poltavsky | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: Bulletin of the American Physical Society (article, 2019-03-06)
Field: Materials Science — Materials Chemistry

Molecular Force Fields with Gradient-Domain Machine Learning: Dynamics of Small Molecules with Coupled Cluster Forces Dynamics (music) ; Cluster (spacecraft) ; Force field (fiction) ; Molecular dynamics ; Domain (mathematical analysis) ; Chemical physics ; Physics ; Computer science ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Ruiyang Li | Eungkyu Lee | Tengfei Luo
Source: arXiv (Cornell University) (preprint, 2019-12-10)
Field: Materials Science — Materials Chemistry

A Unified Deep Neural Network Potential Capable of Predicting Thermal Conductivity of Silicon in Different Phases Molecular dynamics simulations have been extensively used to predict thermal properties, but simulating different phases with similar precision using a unified force field is often difficult, due to the lack of accurate and transferrable interatomistic potential fields. As a result, this issue has become a major barrier to predicting the phase change of materials and their transport properties with atomistic-level modeling techniques. Recently, machine learning based algorithms have emerged as promising tools to develop accurate potentials for molecular dynamics simulations. In this work, we approach the problem of predicting the thermal conductivity of silicon in different phases by performing molecular dynamics simulations with a deep neural network potential. This neural network potential is trained with ab-initio data of silicon in the crystalline, liquid and amorphous phases. The accuracy of our potential is first validated through reproducing the atomistic structures during the phase transition, where other empirical potentials usually fail. The thermal conductivity of different phases is then calculated, showing a good agreement with the experimental results and ab-initio calculation results. Our work shows that a unified neural network-based potential can be a promising tool for studying phase change and thermal transport of materials with high accuracy. Thermal conductivity ; Molecular dynamics ; Artificial neural network ; Silicon ; Ab initio ; Computer science ; Work (physics) ; Field (mathematics) ; Materials science ; Phase transition ; Phase (matter) ; Thermal ; Force field (fiction) ; Statistical physics ; Artificial intelligence ; Computational chemistry ; Thermodynamics ; Physics ; Chemistry ; Mathematics ; Optoelectronics Thermal properties of materials ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Anna M. Hiszpanski | Brian Gallagher | Karthik Chellapan | Peggy Li | Shusen Liu | Hyojin Kim | Jinkyu Han | Bhavya Kailkhura | David Buttler | T. Yong-Jin Han
Source: ChemRxiv (preprint, 2019-11-20)
Field: Materials Science — Materials Chemistry

Nanomaterials Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge &lt;p&gt;Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ca. 35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles’ text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% prediction on morphology prediction, 0.99 AUC on protocol identification, and up to 0.87 F1-score on chemical entity recognition. In addition to processing articles’ text, microscopy images of nanomaterials within articles are also automatically identified and analyzed to determine nanomaterials’ morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design.&lt;/p&gt; Computer science ; Nanomaterials ; Categorization ; Visualization ; Structuring ; Identification (biology) ; Flexibility (engineering) ; Data science ; Nanotechnology ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Anna M. Hiszpanski | Brian Gallagher | Karthik Chellapan | Peggy Li | Shusen Liu | Hyojin Kim | Jinkyu Han | Bhavya Kailkhura | David Buttler | T. Yong-Jin Han
Source: ChemRxiv (preprint, 2019-11-19)
Field: Materials Science — Materials Chemistry

Nanomaterials Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ca. 35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles’ text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% prediction on morphology prediction, 0.99 AUC on protocol identification, and up to 0.87 F1-score on chemical entity recognition. In addition to processing articles’ text, microscopy images of nanomaterials within articles are also automatically identified and analyzed to determine nanomaterials’ morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design. Computer science ; Nanomaterials ; Categorization ; Visualization ; Flexibility (engineering) ; Identification (biology) ; Structuring ; Nanotechnology ; Data science ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Zhen Song | Quanlin Liu
Source: The Journal of Physical Chemistry (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Structural Indicator to Characterize the Crystal-Field Splitting of Ce³⁺ in Garnets The structure–property relationship is a hot research topic in both chemistry and material science. A structural descriptor has significant applications in machine learning and high-throughput screening for rapid estimations of material properties. In this work, we report a simple structural indicator to characterize the crystal-field splitting of Ce³⁺ ions in the garnet structure. The indicator is the interplanar distance of the octahedron, which is calculated as 2/√3 | x + y + z | · L, where L stands for the cell parameter and (x, y, z) are the oxygen coordinates in a specific form. The indicator value is correlated to the crystal-field splitting of Ce³⁺ in the lanthanide aluminum/gallium garnets and is able to reproduce the reverse garnet effect. By inspecting the polyhedral competition effect in the garnet structure, this indicator is found to be correlated to the tetragonal distortion of the dodecahedron, which determines the crystal-field splitting. Tetragonal crystal system ; Dodecahedron ; Chemistry ; Gallium ; Octahedron ; Crystal structure ; Lanthanide ; Crystallography ; Crystal (programming language) ; Crystal field theory ; Ion Radioactive element chemistry and processing
Authors: Lin Hu | Rui Su | Bing Huang | Feng Liu
Source: arXiv (Cornell University) (preprint, 2019-01-07)
Field: Materials Science — Materials Chemistry

An Accurate and Transferable Machine-Learning Interatomic Potential for Silicon The development of modern ab initio methods has rapidly increased our understanding of physics, chemistry and materials science. Unfortunately, intensive ab initio calculations are intractable for large and complex systems. On the other hand, empirical force fields are less accurate with poor transferability even though they are efficient to handle large and complex systems. The recent development of machine-learning based neural-network (NN) for local atomic environment representation of density functional theory (DFT) has offered a promising solution to this long-standing challenge. Si is one of the most important elements in science and technology, however, an accurate and transferable interatomic potential for Si is still lacking. Here, we develop a generalized NN potential for Si, which correctly predicts the Si(111)-(7x7) ground-state surface reconstruction for the first time and accurately reproduces the DFT results in a wide range of complex Si structures. We envision similar developments will be made for a wide range of materials systems in the near future. Transferability ; Ab initio ; Interatomic potential ; Density functional theory ; Computer science ; Range (aeronautics) ; Silicon ; Representation (politics) ; Ab initio quantum chemistry methods ; Artificial neural network ; Statistical physics ; Artificial intelligence ; Machine learning ; Materials science ; Chemistry ; Physics ; Computational chemistry ; Molecular dynamics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides
Authors: Thomas J. Struble | Connor W. Coley | Klavs F. Jensen
Source: ChemRxiv (preprint, 2019-08-28)
Field: Chemistry — Organic Chemistry

Multitask Prediction of Site Selectivity in Aromatic C-H Functionalization Reactions Aromatic C-H functionalization reactions are an important part of the synthetic chemistry toolbox. Accurate prediction of site selectivity can be crucial for prioritizing target compounds and synthetic routes in both drug discovery and process chemistry. However, selectivity may be highly dependent on subtle electronic and steric features of the substrate. We report a generalizable approach to prediction of site selectivity that is accomplished using a graph-convolutional neural network for the multitask prediction of 123 C-H functionalization tasks. In an 80/10/10 training/validation/testing pseudo-time split of about 58,000 aromatic C-H functionalization reactions from the Reaxys database, the model achieves a mean reciprocal rank of 92%. Once trained, inference requires approximately 200 ms per compound to provide quantitative likelihood scores for each task. This approach and model allow a chemist to quickly determine which C-H functionalization reactions-if any-might proceed with high selectivity. Surface modification ; Selectivity ; Chemistry ; Combinatorial chemistry ; Toolbox ; Computer science ; Inference ; Steric effects ; Artificial intelligence ; Stereochemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science
Authors: Alain Tchagang | Julio J. Valdés
Source: arXiv (Cornell University) (preprint, 2019-04-23)
Field: Materials Science — Materials Chemistry

A Classification Scheme for Inverse Design of Molecules: from Targeted Electronic Properties to Atomicity In machine learning and molecular design, there exist two approaches: discriminative and generative. In the discriminative approach dubbed forward design, the goal is to map a set of features/molecules to their respective electronics properties. In the generative approach dubbed inverse design, a set of electronics properties is given and the goal is to find the features/molecules that have these properties. These tasks are very challenging because the chemical compound space is very large. In this study, we explore a new scheme for the inverse design of molecules based on a classification paradigm that takes as input the targeted electronic properties and output the atomic composition of the molecules (i.e. atomicity or atom counts of each type in a molecule). To test this new hypothesis, we analyzed the quantum mechanics QM7b dataset consisting of 7211 small organic molecules and 14 electronic properties. Results obtained using twenty three different classification approaches including a regularized Bayesian neural network show that it is possible to achieve detection/prediction accuracy &gt; 90%. Discriminative model ; Atomicity ; Set (abstract data type) ; Computer science ; Inverse ; Generative grammar ; Artificial neural network ; Chemical space ; Artificial intelligence ; Scheme (mathematics) ; Theoretical computer science ; Machine learning ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Osami Yamamoto | Yuichi Matsuo | Shinichi Tosa | Tatsuya Okayama | John Tolsma | Zhiwei Zhang | Bradley Niesner
Source: SAE technical papers on CD-ROM/SAE technical paper series (article, 2019-12-19)
Field: Materials Science — Materials Chemistry

Catalyst Design Study of Detailed Reaction Model including Gas Diffusivity into the Washcoat &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;To understand the effect of diffusion into catalyst washcoat pores on the NO&lt;sub&gt;x&lt;/sub&gt; purification performance, the pseudo-2D model was developed, combining diffusion theory and the elementary reaction model. Using a Pt/Al&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; + CeO&lt;sub&gt;2&lt;/sub&gt; catalyst, the rate coefficients for NO&lt;sub&gt;x&lt;/sub&gt; storage and reduction reactions were determined with a plug flow reactor. As utilization, the sensitivity analysis on the performance showed that meso pore properties, particle size, and washcoat thickness were found to have large influences. Moreover, by the combination of machine learning as material informatics approach, it was possible to comprehensively calculate the performance under various washcoat properties.&lt;/div&gt;&lt;/div&gt; Catalysis ; Thermal diffusivity ; Process engineering ; Computer science ; Nuclear engineering ; Chemical engineering ; Thermodynamics ; Chemistry ; Engineering ; Physics Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Yanhui Zhang | Alessandro Lunghi | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2019-11-08)
Field: Materials Science — Materials Chemistry

Pushing the limits of atomistic simulations towards ultra-high temperature: a machine-learning force field for ZrB2 Determining thermal and physical quantities across a broad temperature domain, especially up to the ultra-high temperature region, is a formidable theoretical and experimental challenge. At the same time it is essential for understanding the performance of ultra-high temperature ceramic (UHTC) materials. Here we present the development of a machine-learning force field for ZrB2, one of the primary members of the UHTC family with a complex bonding structure. The force field exhibits chemistry accuracy for both energies and forces and can reproduce structural, elastic and phonon properties, including thermal expansion and thermal transport. A thorough comparison with available empirical potentials shows that our force field outperforms the competitors. Most importantly, its effectiveness is extended from room temperature to the ultra-high temperature region (up to ~ 2,500 K), where measurements are very difficult, costly and some time impossible. Our work demonstrates that machine-learning force fields can be used for simulations of materials in a harsh environment, where no experimental tools are available, but crucial for a number of engineering applications, such as in aerospace, aviation and nuclear. Aerospace ; Force field (fiction) ; Field (mathematics) ; Thermal expansion ; Thermal ; Ceramic ; Mechanical engineering ; Work (physics) ; Materials science ; Aerospace engineering ; Engineering physics ; Nanotechnology ; Computer science ; Engineering ; Artificial intelligence ; Physics ; Thermodynamics ; Composite material Machine Learning in Materials Science ; Nuclear Materials and Properties ; Boron and Carbon Nanomaterials Research
Authors: Piero Gasparotto | Davide Bochicchio | Michele Ceriotti | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2019-06-14)
Field: Materials Science — Biomaterials

A Complete Structural and Dynamic Characterization of Supramolecular Polymers via Automatic Learning of Defects A central paradigm of self-assembly is to create ordered structures starting from molecular monomers that spontaneously recognize and interact with each other via noncovalent interactions. In the recent years, great efforts have been directed toward reaching the perfection in the design of a variety of supramolecular polymers and materials with different architectures. The resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers, micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the level of statistical ensembles to assess their average properties. However, high-resolution molecular simulations recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic behavior and properties. The study of these defects poses considerable challenges, as the flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes a defect, and to characterize its stability and evolution. Here, we demonstrate the power of unsupervised machine learning techniques to systematically identify and compare defects in supramolecular polymer variants in different conditions, using as a benchmark high-resolution molecular simulations of a family of supramolecular polymers. We shot that this approach allows a complete data-driven characterization of the internal structure and dynamics of these complex assemblies and of the dynamic pathways for defects formation and resorption. This provides a useful, generally applicable approach to unambiguously identify defects in these dynamic self-assembled materials and to classify them based on their structure, stability and dynamics. Supramolecular chemistry ; Characterization (materials science) ; Supramolecular polymers ; Nanotechnology ; Computer science ; Materials science ; Polymer ; Biological system ; Chemistry ; Molecule Supramolecular Self-Assembly in Materials ; Polymer composites and self-healing ; Polydiacetylene-based materials and applications