jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunchao Xie | Chen Zhang | Xiangquan Hu | Chi Zhang | Steven P. Kelley | Jerry L. Atwood | Jian Lin
Source: ChemRxiv (preprint, 2019-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Synthesis of Metal-Organic Nanocapsules &lt;p&gt;Herein, we report the successful discovery of a new hierarchical structure of metal-organic nanocapsules (MONCs) by integrating chemical intuition and machine learning algorithms. By training datasets from a set of both succeeded and failed experiments, we studied the crystallization &lt;a&gt;propensity &lt;/a&gt;of metal-organic nanocapsules (MONCs). Among four machine learning models, XGB model affords the highest prediction accuracy of 91%. The derived chemical feature scores and chemical hypothesis from the XGB model assist to identify proper synthesis parameters showing superior performance to a well-trained chemist. This paper will shed light on the discovery of new crystalline inorganic-organic hybrid materials guided by machine learning algorithms.&lt;/p&gt; Nanocapsules ; Intuition ; Artificial intelligence ; Computer science ; Nanotechnology ; Machine learning ; Materials science ; Nanoparticle ; Psychology ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Yu Saito | Shin Kento | Kei Terayama | Shaan Desai | Masaru Onga | Yuji Nakagawa | Yuki M. Itahashi | Yoshihiro Iwasa | Makoto Yamada | Koji Tsuda
Source: arXiv (Cornell University) (preprint, 2019-07-07)
Field: Materials Science — Materials Chemistry

Deep learning-based quality filtering of mechanically exfoliated 2D crystals Two-dimensional (2D) crystals are attracting growing interest in various research fields such as engineering, physics, chemistry, pharmacy and biology owing to their low dimensionality and dramatic change of properties compared to the bulk counterparts. Among the various techniques used to manufacture 2D crystals, mechanical exfoliation has been essential to practical applications and fundamental research. However, mechanically exfoliated crystals on substrates contain relatively thick flakes that must be found and removed manually, limiting high-throughput manufacturing of atomic 2D crystals and van der Waals heterostructures. Here we present a deep learning-based method to segment and identify the thickness of atomic layer flakes from optical microscopy images. Through carefully designing a neural network based on U-Net, we found that our neural network based on U-net trained only with the data based on 24 images successfully distinguish monolayer and bilayer MoS2 with a success rate of 70%, which is a practical value in the first screening process for choosing monolayer and bilayer flakes of MoS2 of all flakes on substrates without human eye. The remarkable results highlight the possibility that a large fraction of manual laboratory work can be replaced by AI-based systems, boosting productivity. Monolayer ; Exfoliation joint ; Bilayer ; Nanotechnology ; Curse of dimensionality ; Materials science ; Artificial neural network ; Artificial intelligence ; Limiting ; van der Waals force ; Graphene ; Deep learning ; Computer science ; Engineering ; Mechanical engineering ; Chemistry 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: А. Н. Залога | Vladimir Stanovov | Oksana E. Bezrukovа | P. S. Dubinin | Igor S. Yakimov
Source: Journal of Siberian Federal University Chemistry (article, 2019-06-01)
Field: Materials Science — Materials Chemistry

Possibilities of Neural Network Powder Diffraction Analysis Crystal Structure of Chemical Compounds Some possibilities of using convolutional artificial neural networks (ANN) for powder diffraction structural analysis of crystalline substances have been investigated. First, ANNs are used to classify crystalline systems and space groups according to calculated full-profile diffractograms calculated from the crystal structures of the ICSD database (2017 year). The ICSD database contains 192004 structures, of which 80% was used for in-depth network training, and 20% for independent testing of recognition accuracy. The accuracy of classification by a network of crystalline systems was 87.9%, and that of space groups was 77.2%. Secondly, the ANN is used for a similar classification of structural models generated by the stochastic genetic algorithm in the search processes for triclinic crystal structures of test compound K4SnO4 according to their full-profile diffraction patterns. The classification criterion was the entry of one or several atoms into their crystallographic positions in the structure of a substance. Independent deep network training was performed on 120 thousand structural models of the K4PbO4 triclinic structure generated in several runs of the genetic algorithm. The accuracy of the classification of K4SnO4 structural models exceeded 50%. The results show that deeply trained convolutional ANNs can be effective for classifying crystal structures according to the structural characteristics of their powder diffraction patterns Triclinic crystal system ; Diffraction ; Crystal structure ; Convolutional neural network ; Powder diffraction ; Artificial neural network ; Genetic algorithm ; Artificial intelligence ; Materials science ; Pattern recognition (psychology) ; Computer science ; Crystallography ; Biological system ; Chemistry ; Physics ; Machine learning ; Optics ; Biology X-ray Diffraction in Crystallography ; Pigment Synthesis and Properties
Authors: Chuhong Wang | Koutarou Aoyagi | Pandu Wisesa | Tim Mueller
Source: ChemRxiv (preprint, 2019-11-14)
Field: Materials Science — Materials Chemistry

Lithium Ion Conduction in Cathode Coating Materials from On-the-Fly Machine Learning Cathode coatings have the potential to significantly improve the performance of solid-state lithium-ion batteries, but computationally identifying materials with suitable ionic conductivity can be challenging. We demonstrate how this problem can be resolved using on-the-fly machine learning, and we validate our approach against experimental data. Based on a screen for high electrochemical stability, low interfacial reactivity and viable lithium ion conduction, we suggest two promising coating materials Li₃Sc₂(PO₄)₃ and Li₃B₇O₁₂. Li₃B₇O₁₂ to our knowledge has not been studied as an ionic conductor and is predicted to have high interfacial stability for a variety of leading electrolyte/cathode material combinations. The workflow we present accelerates the discovery of fast-to-intermediate ionic conductors and can readily be generalized to other types of materials. Cathode ; Materials science ; Coating ; Electrolyte ; Ionic conductivity ; On the fly ; Lithium (medication) ; Electrochemistry ; Ion ; Ionic bonding ; Electrical conductor ; Chemical engineering ; Nanotechnology ; Computer science ; Composite material ; Chemistry ; Electrode ; Physical chemistry ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials
Authors: Rainier Barrett | Maghesree Chakraborty | Dilnoza Amirkulova | Heta A. Gandhi | Andrew Dickson White
Source: ChemRxiv (preprint, 2019-04-23)
Field: Materials Science — Materials Chemistry

A GPU-Accelerated Machine Learning Framework for Molecular Simulation: Hoomd-Blue with TensorFlow We have designed and implemented software that enables integration of a scalable GPU-accelerated molecular mechanics engine, Hoomd-blue, with the machine learning (ML) TensorFlow package. TensorFlow is a GPU accelerated, scalable, graph-based tensor computation model building package that has been the implementation of many recent innovations in deep learning and other ML tasks. Tensor computation graphs allow for designation of robust, flexible, and easily replicated computational models for a variety of tasks. Our plugin leverages the generality and speed of computational tensor graphs in TensorFlow to enable four previously challenging tasks in molecular dynamics: (1) the calculation of arbitrary force-fields including neural-network-based, stochastic, and/or automatically-generated force-fields which are differentiated from potential functions; (2) the efficient computation of arbitrary collective variables; (3) the biasing of simulations via automatic differentiation of collective variables and consequently the implementation of many free energy biasing methods; (4) ML on any of the above tasks, including coarse grain force fields, on-the-fly learned biases, and collective variable calculations. The TensorFlow models are constructed in Python and can be visualized or debugged using the rich set of tools implemented in the TensorFlow package. In this article, we present examples of the four major tasks this method can accomplish, benchmark data, and describe the architecture of our implementation. This method should lead to both the design of new models in computational chemistry research and reproducible model specification without requiring recompiling or writing low-level code. Computer science ; Python (programming language) ; Scalability ; Artificial intelligence ; Automatic differentiation ; Computational science ; Computation ; Plug-in ; Machine learning ; Deep learning ; Artificial neural network ; Software ; Benchmark (surveying) ; Theoretical computer science ; Computer engineering ; Algorithm ; Programming language Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques
Authors: Jay Whitacre | Jared Mitchell | Adarsh Dave | Sven Burke | Venkatasubramanian Viswanathan
Source: ChemRxiv (preprint, 2019-10-15)
Field: Materials Science — Materials Chemistry

An Autonomous Electrochemical Test stand for Machine Learning Informed Electrolyte Optimization A fully automated, computer-controlled test stand capable of rapidly creating and electrochemically characterizing any arbitrary liquid electrolyte solution is described. Hundreds of different electrolytes were studied, and the results were used to verify the precision and accuracy of the system. To test the functionality of the approach, several 2-dimensional co-solvated electrolyte solutions containing blends of aqueous sulfates and nitrates were rapidly created and examined automatically. The test stand took less than a day to conduct these searches, while conventional manual methods would have taken much longer. The demonstrated standard error of the test-stand was 0.5 mS/cm on conductivity and 0.02 V for voltage stability window measurements, and several of the combinations studied revealing surprisingly high voltage stability and conductivity values. The demonstrated success of the test-stand in a 2-dimensional search spaces shows the promise of conducting high speed co-optimization studies of liquid electrolytes in particular when used in concert with a machine learning-based real time/in-loop data assessment computational package. Electrolyte ; Conductivity ; Electrochemical window ; Voltage ; Electrochemistry ; Computer science ; Stability (learning theory) ; Materials science ; Analytical Chemistry (journal) ; Simulation ; Chemistry ; Electrical engineering ; Engineering ; Chromatography ; Machine learning ; Ionic conductivity ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Nasrin Bakhshizadeh | Ashkan Forootan
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2019-07-04)
Field: Chemistry — Spectroscopy

Forecast of Polyethylene Properties in the Gas Phase Polymerization Aided by Neural Network A major problem that affects the quality control of polymer in the industrial polymerization is the lack of suitable on-line measurement tools to evaluate the properties of the polymer such as melt and density indices. Controlling the polymerization in ordinary method is performed manually by taking samples, measuring the quality of polymer in the lab and registry of results. This method is highly time consuming and leads to producing large number of incompatible products. An online application for estimating melt index and density proposed in this study is a neural network based on the input-output data of the polyethylene production plant. Temperature, the level of reactors' bed, the intensity of ethylene mass flow, hydrogen and butene-1, the molar concentration of ethylene, hydrogen and butene-1 are used for the process to establish the neural model. The neural network is taught based on the actual operational data and back-propagation and Levenberg-Marquart techniques. The simulated results indicate that the neural network process model established with three layers (one hidden layer) for forecasting the density and the four layers for the melt index is able to successfully predict those quality properties. Polymerization ; Polyethylene ; Artificial neural network ; Gas phase ; Phase (matter) ; Chemistry ; Materials science ; Computer science ; Polymer ; Organic chemistry ; Artificial intelligence Adsorption, diffusion, and thermodynamic properties of materials
Authors: Daichi Minami | Tokuteru Uesugi | Yorinobu Takigawa | Kenji Higashi
Source: Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

First-principles calculations assisted machine learning for predicting transformation temperatures in shape memory alloys A key property for the design of new shape memory alloys is their working temperature range that depends on their transformation temperature T0. In previous works, T0 was predicted using a simple linear regression with respect to the energy difference between the parent and the martensitic phases, ΔEp-m. In this paper, we developed an accurate method to predict T0 based on machine learning assisted by the first-principles calculations. First-principles calculations were performed on 15 shape memory alloys; then, we proposed an artificial neural network method that used not only computed ΔEp-m but also bulk moduli as input variables to predict T0. The prediction error of T0 was improved to 49 K for the proposed artificial neural network compared with 188 K for simple linear regression. Shape-memory alloy ; Artificial neural network ; Transformation (genetics) ; Simple (philosophy) ; Range (aeronautics) ; Linear regression ; Regression ; Diffusionless transformation ; Artificial intelligence ; Computer science ; Property (philosophy) ; Energy (signal processing) ; Moduli ; Algorithm ; Materials science ; Machine learning ; Mathematics ; Martensite ; Physics ; Statistics ; Chemistry ; Metallurgy Shape Memory Alloy Transformations ; Machine Learning in Materials Science
Authors: Paulius Mikulskis | Morgan Alexander | David Winkler | La Trobe University Library
Source: La Trobe University (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Towards efficient and interpretable machine learning models for materials discovery Machine learning (ML) and artificial intelligence (AI) methods for modeling useful materials properties are now important technologies for rational design and optimization of bespoke functional materials. Although these methods make good predictions of the properties of new materials, current modeling methods use efficient but rather arcane (difficult-to-interpret) mathematical features (descriptors) to characterize materials. Data-driven ML models are considerably more useful if more chemically interpretable descriptors are used to train them, as long as these models also accurately recapitulate the properties of materials in training and test sets used to generate and validate the models. Herein, how a particular type of molecular fragment descriptor, the signature descriptor, achieves these joint aims of accuracy and interpretability is described. Seven different types of materials properties are modeled, and the performance of models generated from signature descriptors is compared with those generated by widely used Dragon descriptors. The key descriptors in the model represent functionalities that make chemical sense. Mapping these fragments back on to exemplar materials provides a useful guide to chemists wishing to modify promising lead materials to improve their properties. This is one of the first applications of signature descriptors to the modeling of complex materials properties. Interpretability ; Bespoke ; Signature (topology) ; Key (lock) ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Issei Nakamura
Source: arXiv (Cornell University) (preprint, 2019-08-15)
Field: Materials Science — Materials Chemistry

Neural network for phase diagrams of polymer-containing liquid mixtures Polymer-containing liquid mixtures can exhibit macroscopic phase separation and microphase separation. The diversity of the phase behavior exponentially increases when altering the polymer architecture or adding salt ions, yet exploring and modeling this vast parameter space in experiments, theory, and molecular simulations are often challenging. Thus, we develop a deep neural network (DNN) to address this issue. The key component in the DNN consists of a hidden layer that captures the characteristic features of phase behavior via coarse-grained mean-field theory and scaling laws and substantially enhances the accuracy of the DNN. This study also presents the predictive power of the DNN for the phase behaviors of polymer solutions and salt-free and salt-doped diblock copolymer melts. Polymer ; Materials science ; Phase (matter) ; Artificial neural network ; Phase diagram ; Component (thermodynamics) ; Scaling ; Salt (chemistry) ; Chemical physics ; Biological system ; Computer science ; Statistical physics ; Artificial intelligence ; Thermodynamics ; Physics ; Chemistry ; Organic chemistry ; Mathematics ; Composite material Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Lei Gu | Ruqian Wu
Source: arXiv (Cornell University) (preprint, 2019-04-01)
Field: Materials Science — Materials Chemistry

Property-aimed embedding: a machine learning framework for material discovery Proposing new materials by atom substitution based on periodic table similarity is a conventional strategy of searching for materials with desired property. We introduce a machine learning frame work that promotes this paradigm to be property-specific and quantitative. It is of peculiar usefulness in situations where abundance data is accessible for learning general knowledge but samples for the problem of interest are relatively scarce. We showcase its usage and viability in the problem of separating high entropy alloys with different structural phases, for which a very simple data-driven criterion achieves differentiating ability comparable with widely used empirical criteria. Its flexibility and generability make it a promising tool in other material discovery tasks and far beyond. Computer science ; Embedding ; Property (philosophy) ; Flexibility (engineering) ; Frame (networking) ; Simple (philosophy) ; Artificial intelligence ; Similarity (geometry) ; Entropy (arrow of time) ; Table (database) ; Machine learning ; Data mining ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Additive Manufacturing Materials and Processes
Authors: Anthony Tabet | Thomas Gebhart | Guanglu Wu | Charlie Readman | Merrick Pierson Smela | Vijay Kumar Rana | Cole Baker | Harry Bulstrode | Polina Anikeeva | David H. Rowitch | Oren A. Scherman
Source: ChemRxiv (preprint, 2019-08-13)
Field: Chemistry — Organic Chemistry

Predicting Macrocyclic Molecular Recognition with Machine Learning DFT calculations are used as training data to predict equilibrium binding constants of small molecules to cucurbit [7]uril (CB[7]) with kernel-based support vector machine learning. This trained algorithm was then used to predict the binding of two promising small molecule drugs in the clinic against pediatric low grade glioma, TAK-580 and Selumetinib. The algorithm predicted strong binding for TAK-580 and poor binding for Selumetinib. These results were experimentally validated. It was also discovered that the slightly larger homologue cucurbit[8]uril (CB[8]) is partial to Selumetinib, suggesting an opportunity for tunable release kinetics by introducing different concentrations of CB [7] or CB[8] into a system such as a hydrogel depot for local drug delivery. We also qualitatively demonstrated that these two drugs have different therapeutic windows and may have utility in combination against low grade gliomas. Finally, mass transfer simulations were performed to show how CB [7] can independently tune the release of TAK-580 across time scales from seconds to a year without changing the kinetics of Selumetinib. This work shows how machine learning may prove valuable in the development of drug-delivery systems for combination therapies and the field of supramolecular chemistry more broadly. Supramolecular chemistry ; Computer science ; Artificial intelligence ; Machine learning ; Molecular recognition ; Pattern recognition (psychology) ; Chemistry ; Molecule ; Organic chemistry Supramolecular Chemistry and Complexes ; Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications
Authors: Kareem Abdelfatah | Wenqiang Yang | Rajadurai Vijay Solomon | Biplab Rajbanshi | Asif Chowdhury | Mehdi Zare | Subrata Kumar Kundu | Adam Yonge | Andreas Heyden | Gabriel Terejanu
Source: The Journal of Physical Chemistry (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Transition-State Energies of Hydrodeoxygenation Reactions on Transition-Metal Surfaces Based on Machine Learning Computational catalyst discovery involves identification of a meaningful model and suitable descriptors that determine the catalyst properties. We study the impact of combining various descriptors (e.g., reaction energies, metal descriptors, and bond counts) for modeling transition-state energies (TS) based on a database of adsorption and TS energies across transition-metal surfaces for the decarboxylation and decarbonylation of propionic acid, a chemistry characteristic for biomass conversion. Results of different machine learning models for more than 1572 descriptor combinations suggest that there is no statistically significant difference between linear and nonlinear models when using the right combination of reactant energies, metal descriptors, and bond counts. However, linear models are inferior when not including bond count and metal descriptors. Furthermore, when there are missing data for reaction steps on all metals, conventional linear scaling is inferior to linear and nonlinear models with proper choice of descriptors that are surprisingly robust. Chemistry ; Decarbonylation ; Hydrodeoxygenation ; Transition metal ; Decarboxylation ; Catalysis ; Nonlinear system ; Metal ; Computational chemistry ; Thermodynamics ; Organic chemistry ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Electrocatalysts for Energy Conversion