jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xin Yang | Jun Zou | Yifei Wang | Ying Xue | Shengyong Yang
Source: Chemistry - A European Journal (article, 2019-03-19)
Field: Materials Science — Materials Chemistry

Role of Water in the Reaction Mechanism and <i>endo</i>/<i>exo</i> Selectivity of 1,3‐Dipolar Cycloadditions Elucidated by Quantum Chemistry and Machine Learning Asymmetric 1,3-dipolar cycloadditions of azomethine ylides with activated olefins are among the most important and versatile methods for the synthesis of enantioenriched pyrroline and pyrrolidine derivatives. Despite both theoretical and practical importance, the role of water molecules in the reactivity and endo/exo selectivity remains unclear. To explore how water accelerates the reactions and improves the endo/exo selectivity of the cycloadditions of 1,3-dipole phthalazinium-2-dicyanomethanide (1) and two dipolarophiles, an ab initio-quality neural network potential that overcomes the computational bottleneck of explicitly considering water molecules was used. It is demonstrated that not only the nature of both the dipolarophile and the 1,3-dipole, but also the solvent medium, can perturb or even alter the reaction mechanism. An extreme case was found for the reaction of 1,3-dipole 1 with methyl vinyl ketone, in which the reaction mechanism changes from a concerted to a stepwise mode on going from MeCN to H<sub>2</sub> O as solvent, with formation of a zwitterionic intermediate that is a very shallow minimum on the energy surface. Thus, high stereocontrol can still be expected despite the stepwise nature of the mechanism. The results indicate that water can induce global polarization along the reaction coordinate and highlight the role of microsolvation effects and bulk-phase effects in reproducing the experimentally observed aqueous acceleration and enhanced endo/exo selectivity. Chemistry ; Selectivity ; Azomethine ylide ; Computational chemistry ; Solvent effects ; Quantum chemistry ; Solvation ; Reaction mechanism ; Molecule ; Dipole ; Pyrrolidine ; Ab initio ; Density functional theory ; Solvent ; 1,3-Dipolar cycloaddition ; Cycloaddition ; Stereochemistry ; Organic chemistry ; Supramolecular chemistry ; Catalysis Machine Learning in Materials Science ; Organic Chemistry Cycloaddition Reactions ; Synthesis and Catalytic Reactions
Authors: Mohammad Atif Faiz Afzal | Aditya Sonpal | Mojtaba Haghighatlari | Andrew J. Schultz | Johannes Hachmann
Source: ChemRxiv (preprint, 2019-06-04)
Field: Materials Science — Materials Chemistry

A Deep Neural Network Model for Packing Density Predictions and its Application in the Study of 1.5 Million Organic Molecules &lt;pre&gt;The process of developing new compounds and materials is increasingly driven by computational modeling and simulation, which allow us to characterize candidates before pursuing them in the laboratory. One of the non-trivial properties of interest for organic materials is their packing in the bulk, which is highly dependent on their molecular structure. By controlling the latter, we can realize materials with a desired density (as well as other target properties). Molecular dynamics simulations are a popular and reasonably accurate way to compute the bulk density of molecules, however, since these calculations are computationally intensive, they are not a practically viable option for high-throughput screening studies that assess material candidates on a massive scale. In this work, we employ machine learning to develop a data-derived prediction model that is an alternative to physics-based simulations, and we utilize it for the hyperscreening of 1.5 million small organic molecules as well as to gain insights into the relationship between structural makeup and packing density.We also use this study to analyze the learning curve of the employed neural network approach and gain empirical data on the dependence of model performance and training data size, which will inform future investigations.&lt;/pre&gt; Artificial neural network ; Process (computing) ; Computer science ; Organic molecules ; Work (physics) ; Molecular dynamics ; Throughput ; Artificial intelligence ; Machine learning ; Statistical physics ; Molecule ; Physics ; Computational chemistry ; Chemistry ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jack Yang | Nathan Li | Sean Li
Source: CrystEngComm (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

The interplay among molecular structures, crystal symmetries and lattice energy landscapes revealed using unsupervised machine learning: a closer look at pyrrole azaphenacenes Using unsupervised machine learning and CSPs to help crystallographers better understand how crystallizations are affected by molecular structures. Pyrrole ; Lattice (music) ; Homogeneous space ; Unsupervised learning ; Lattice energy ; Computer science ; Crystal structure ; Artificial intelligence ; Materials science ; Chemistry ; Physics ; Crystallography ; Mathematics ; Geometry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Justin S. Smith | Benjamin Nebgen | R.I. Zubatyuk | Nicholas Lubbers | Christian Devereux | Kipton Barros | Sergei Tretiak | Olexandr Isayev | Adrián E. Roitberg
Source: ChemRxiv (preprint, 2019-06-14)
Field: Materials Science — Materials Chemistry

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology and chemistry, and billions of times fasterthan CCSD(T)/CBS calculations. Computer science ; Scalability ; Coupled cluster ; Thermochemistry ; Artificial neural network ; Transferability ; Quantum chemical ; Artificial intelligence ; Computational science ; Machine learning ; Chemistry ; Physical chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Bingnan Han | Yuxuan Lin | Yafang Yang | Nannan Mao | Wenyue Li | Haozhe Wang | Valla Fatemi | Lin Zhou | Joel I-Jan Wang | Qiong Ma | Yuan Cao | Daniel Rodan‐Legrain | Ya‐Qing Bie | Efrén Navarro‐Moratalla | Dahlia Klein | David MacNeill | Sanfeng Wu | Wei Sun Leong | Hikari Kitadai | Xi Ling | Pablo Jarillo‐Herrero | Tomás Palacios | Jihao Yin | Jing Kong
Source: arXiv (Cornell University) (article, 2019-06-26)
Field: Materials Science — Materials Chemistry

Deep Learning Enabled Fast Optical Characterization of Two-Dimensional Materials Characterization (materials science) ; Deep learning ; Computer science ; Materials science ; Artificial intelligence ; Nanotechnology Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and ELM
Authors: Sambhudutta Nanda | Prasanna Kumar Sahu | Rabindra Kishore Mishra
Source: Journal of Computational Electronics (article, 2019-07-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse artificial neural network modeling for metamaterial unit cell synthesis Inverse ; Metamaterial ; Artificial neural network ; Computer science ; Backpropagation ; Algorithm ; Inverse problem ; Mathematics ; Artificial intelligence ; Mathematical analysis ; Physics Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Speech and Audio Processing
Authors: Arindam Paul | Dipendra Jha | Reda Al-Bahrani | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source:  (preprint, 2019-07-01)
Field: Materials Science — Materials Chemistry

Transfer Learning Using Ensemble Neural Networks for Organic Solar Cell Screening Organic Solar Cells are a promising technology for solving the clean energy crisis in the world. However, generating candidate chemical compounds for solar cells is a time-consuming process requiring thousands of hours of laboratory analysis. For a solar cell, the most important property is the power conversion efficiency which is dependent on the highest occupied molecular orbitals (HOMO) values of the donor molecules. Recently, machine learning techniques have proved to be very useful in building predictive models for HOMO values of donor structures of Organic Photovoltaic Cells (OPVs). Since experimental datasets are limited in size, current machine learning models are trained on data derived from calculations based on density functional theory (DFT). Molecular line notations such as SMILES or InChI are popular input representations for describing the molecular structure of donor molecules. The two types of line representations encode different information, such as SMILES defines the bond types while InChi defines protonation. In this work, we present an ensemble deep neural network architecture, called SINet, which harnesses both the SMILES and InChI molecular representations to predict HOMO values and leverage the potential of transfer learning from a sizeable DFT-computed dataset- Harvard CEP to build more robust predictive models for relatively smaller HOPV datasets. Harvard CEP dataset contains molecular structures and properties for 2.3 million candidate donor structures for OPV while HOPV contains DFT-computed and experimental values of 350 and 243 molecules respectively. Our results demonstrate significant performance improvement from the use of transfer learning and leveraging both molecular representations. Leverage (statistics) ; Organic solar cell ; Artificial neural network ; Artificial intelligence ; Computer science ; Deep learning ; Transfer of learning ; Molecular orbital ; Machine learning ; ENCODE ; Photovoltaic system ; Molecule ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Guillermina Capiel | Florencia Arrosio | Vera A. Álvarez | Pablo E. Montemartini | Juan Morán
Source: Journal of Materials Science and Chemical Engineering (article, 2019-01-01)
Field: Materials Science — Polymers and Plastics

An Artificial Neural Network (ANN) Model for Predicting Water Absorption of Nanoclay-Epoxy Composites Glass fiber reinforced epoxy (GFRE) composite materials are prone to suffer from water absorption due to their heterogeneous structure. The main process governing water absorption is diffusion of water molecules through the epoxy matrix. However, hydrolytic degradation may also take place during components service life specially due high temperatures. In order to mitigate the effects of the water diffusive processes in the deterioration of in-service behavior of epoxy matrix composites, the use of chemically modified nanoclays as an additive has been proposed and studied in previous works [1]. In this work, an Artificial Neural Network (ANN) model was developed for better understanding and predicting the influence of modified and unmodified bentonite addition on the water absorption behavior of epoxy-anhydride systems. An excellent correlation between model and experimental data was found. The ANN model allowed the identification of critical points like the precise temperature at which a particular system’s water uptake goes beyond a predefined threshold, or which system will resist an immersion longer than a particular time. Epoxy ; Materials science ; Absorption of water ; Composite material ; Composite number ; Artificial neural network ; Absorption (acoustics) ; Bentonite ; Chemical engineering ; Computer science Polymer Nanocomposites and Properties ; High voltage insulation and dielectric phenomena ; Epoxy Resin Curing Processes
Authors: Irina Cherepanska | Олена Миколаївна Безвесільна | Yurii Koval | Artem Sazonov
Source: METALLOFIZIKA I NOVEISHIE TEKHNOLOGII (article, 2019-03-27)
Field: Chemistry — Analytical Chemistry

Intelligent Precise Goniometric System of Analysis of Spectral Distribution Intensities for Definition of Chemical Composition of Metal-Containing Substances The article dedicated to urgent task-definition of chemical composition of metal-containing substances. The new precise intelligent goniometric system, which contains laser goniometer, CMOS image sensor, and artificial neural network, is proposed. This system combines the advantages such as safety for humans and environment, high productivity, usage simplicity, universality, automated processing of the measuring data. Goniometer ; Materials science ; Composition (language) ; Chemical composition ; Spectral power distribution ; Metal ; Optics ; Physics ; Chemistry ; Organic chemistry ; Metallurgy ; Art ; Literature Spectroscopy and Chemometric Analyses ; Laser-induced spectroscopy and plasma ; Water Quality Monitoring and Analysis
Authors: Roald Hoffmann | Jean‐Paul Malrieu
Source: Angewandte Chemie (article, 2019-11-01)
Field: Materials Science — Materials Chemistry

Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. Part A. Stage Setting Abstract We begin our tripartite Essay with a triangle of understanding, theory and simulation. Sketching the intimate tie between explanation and teaching, we also point to the emotional impact of understanding. As we trace the development of theory in chemistry, Dirac's characterization of what is known and what is needed for theoretical chemistry comes up, as does the role of prediction, and Thom's phrase “To predict is not to explain.” We give a typology of models, and then describe, no doubt inadequately, machine learning and neural networks. In the second part, we leave philosophy, beginning by describing Roald's being beaten by simulation. This leads us to artificial intelligence (AI), Searle's Chinese room, and Strevens’ account of what a go ‐playing program knows. Back to our terrain—we ask “Quantum Chemistry, † ca. 2020?” Then move to examples of AI affecting social matters, ranging from trivial to scary. We argue that moral decisions are hardly to be left to a computer. At this point, we try to pull the reader up, giving the opposing view of an optimistic, limitless future a voice. But we don't do justice to that view—how could we? We return to questioning the ascetic dimension of scientists, their romance with black boxes. Onward: In the 3rd part of this Essay, we work our way up from pessimism. We trace (another triangle!) the special interests of experimentalists, who want the theory we love, and reliable numbers as well. We detail in our own science instances where theory gave us real joy. Two more examples‐on magnetic coupling in inorganic diradicals, and the way to think about alkali metal halides, show us the way to integrate simulation with theory. Back and forth is how it should be—between painfully‐obtained, intriguing numbers, begging for interpretation, in turn requiring new concepts, new models, new theoretically grounded tools of computation. Through such iterations understanding is formed. As our tripartite Essay ends, we outline a future of consilience, with a role both for fact‐seekers, and searchers for understanding. Chemistry's streak of creation provides in that conjoined future a passage to art and to perceiving, as we argue we must, the sacred in science. TRACE (psycholinguistics) ; Epistemology ; Argument (complex analysis) ; Chemistry ; Computer science ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Guido Ori | Assil Bouzid | Évelyne Martin | Carlo Massobrio | Sébastien Le Roux | Mauro Boero
Source: Solid State Sciences (article, 2019-07-04)
Field: Materials Science — Materials Chemistry

Chalcogenide glasses as a playground for the application of first-principles molecular dynamics to disordered materials Chalcogenide ; van der Waals force ; Computer science ; Scope (computer science) ; Nanotechnology ; Statistical physics ; Dispersion (optics) ; Materials science ; Physics ; Quantum mechanics ; Molecule Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties
Authors: Kenji Homma | Yu Liu | Masato Sumita | Ryo Tamura | Naoki Fushimi | Junichi Iwata | Koji Tsuda | Chioko Kaneta
Source: arXiv (Cornell University) (preprint, 2019-11-28)
Field: Materials Science — Materials Chemistry

Optimization of heterogeneous ternary Li3PO4-Li3BO3-Li2SO4 mixture for Li-ion conductivity by machine learning Mixing heterogeneous Li-ion conductive materials is one of potential ways to enhance the Li-ion conductivity more than that of the parent materials. However, the development of the mixtures had not exhibited significant progress because it is a formidable task to cover the vast possible composition of the parent materials using traditional ways. Here, we introduce a fashion based on machine learning to optimize the composition ratio of ternary Li3PO4-Li3BO3-Li2SO4 mixture for its Li-ion conductivity. According to our results, the optimum composition of the ternary mixture system is 25:14:61 (Li3PO4: Li3BO3: Li2SO4 in mol%), whose Li-ion conductivity is measured as 4.9 x 10E-4 S/cm at 300 °C. Our X-ray structure analysis indicates that Li-ion conductivity in the mixing systems is enhanced by virtue of the coexistence of two or more phases. Although the mechanism enhancing Li-ion conductivity is not simple, our results demonstrate the effectiveness of machine learning for the development of materials. Ternary operation ; Conductivity ; Ion ; Mixing (physics) ; Materials science ; Electrical conductor ; Analytical Chemistry (journal) ; Chemistry ; Computer science ; Chromatography ; Physical chemistry ; Composite material ; Physics ; Organic chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Sima E. Borujeni | Ramkumar Harikrishnakumar | Saideep Nannapaneni
Source:  (article, 2019-12-01)
Field: Materials Science — Materials Chemistry

Quantum Grover search-based optimization for innovative material discovery Advances in data analysis, machine learning, and combinatorial optimization have accelerated the discovery of new materials with desired properties; this paradigm is commonly referred to as Materials 4.0. Quantum computing represents a new computing framework that uses the principles of quantum mechanics such as entanglement and superposition. Quantum algorithms such as Grover search has been proven to have quadratic speedup over the classical search methods. This paper considers the application of Grover search-based combinatorial optimization for material discovery. In particular, this paper considers the identification of the Nickel-Titanium (Ni-Ti) based shape memory alloy of the composition, Ti <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> Ni <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50-x-y</sub> Cu <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> Fe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> that has the minimum thermal hysteresis associated with the austenite-martensite transformations. The paper details the construction of quantum circuits to carry out the optimization analysis, and demonstrates the analysis on a simulation platform using the Python Qiskit package. Computer science ; Quantum computer ; Combinatorial optimization ; Quantum annealing ; Quantum ; Algorithm ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Katja Ovchinnikova | Vitaly Kovalev | Lachlan Stuart | Theodore Alexandrov
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-01-14)
Field: Chemistry — Spectroscopy

Recognizing off-sample mass spectrometry images with machine and deep learning Abstract Motivation Imaging mass spectrometry (imaging MS) is a powerful technology for revealing localizations of hundreds of molecules in tissue sections. However, imaging MS data is polluted with off-sample ions caused by caused by sample preparation, particularly by the MALDI matrix application. The presence of the off-sample ion images confounds and hinders metabolite identification and downstream analysis. Results We created a high-quality gold standard of 23238 manually tagged ion images from 87 public datasets from the METASPACE knowledge base. We developed several machine and deep learning methods for recognizing off-sample ion images. Deep residual learning performed the best with the F1 score of 0.97. Spatio-molecular biclustering method achieved the F1 scores of 0.96 and 0.93 in semi- and fully-automated scenarios, respectively. Molecular co-localization method achieved the F1 score of 0.90. We investigated the clusters of the DHB matrix, the most common MALDI matrix, and characterized parameters of a clusters combinatorial model. This work addresses an important issue in imaging MS and illustrates how public data, modern web technologies, and machine and deep learning open novel avenues in imaging MS. Availability and Implementation Data and source code are available at: https://github.com/metaspace2020/offsample . Contact theodore.alexandrov@embl.de Mass spectrometry imaging ; Artificial intelligence ; Sample (material) ; Computer science ; Mass spectrometry ; Deep learning ; MALDI imaging ; Pattern recognition (psychology) ; Matrix (chemical analysis) ; Machine learning ; Data mining ; Matrix-assisted laser desorption/ionization ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Molecular Biology Techniques and Applications