jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jan Weinreich | Martín Leandro Paleico | Jörg Behler
Source: arXiv (Cornell University) (preprint, 2021-03-25)
Field: Materials Science — Materials Chemistry

Properties of α-Brass Nanoparticles II: Structure and Composition Nanoparticles have become increasingly interesting for a wide range of applications, because in principle it is possible to tailor their properties by controlling size, shape and composition. One of these applications is heterogeneous catalysis, and a fundamental understanding of the structural details of the nanoparticles is essential for any knowledge-based improvement of reactivity and selectivity. In this work we investigate the atomic structure of brass nanoparticles containing up to 5000 atoms as a typical example for a binary alloy consisting of Cu and Zn. As systems of this size are too large for electronic structure calculations, in our simulations we use a recently parametrized machine learning potential providing close to density functional theory accuracy. This potential is employed for a structural characterization as a function of chemical composition by various types of simulations like Monte Carlo in the Semi-Grand Canonical Ensemble and simulated annealing molecular dynamics. Our analysis reveals that the distribution of both elements in the nanoparticles is inhomogeneous, and zinc accumulates in the outermost layer, while the first subsurface layer shows an enrichment of copper. Only for high zinc concentrations alloying can be found in the interior of the nanoparticles, and regular patterns corresponding to crystalline bulk phases of $α$-brass can then be observed. The surfaces of the investigated clusters exhibit well-ordered single-crystal facets, which can give rise to grain boundaries inside the clusters. The melting temperature of the nanoparticles is found to decrease with increasing zinc-atom fraction, a trend which is well-known also for the bulk phase diagram of brass. Nanoparticle ; Materials science ; Brass ; Molecular dynamics ; Annealing (glass) ; Chemical physics ; Alloy ; Coordination number ; Zinc ; Copper ; Nanotechnology ; Chemistry ; Computational chemistry ; Metallurgy ; Ion Nanoporous metals and alloys ; Quasicrystal Structures and Properties
Authors: Mavrik Zavarin | Haruko Wainwright | Jadallah Zouabe | Elliot Chang
Source: Goldschmidt2021 abstracts (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

New Approaches to Surface Complexation Modeling and Application of Machine Learning to Radionuclide-Mineral Interface Reactions Radionuclide ; Interface (matter) ; Computer science ; Mineral ; Radiochemistry ; Chemistry ; Materials science ; Metallurgy ; Nuclear physics ; Operating system ; Physics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Radioactive element chemistry and processing
Authors: Masato Sumita | Kei Terayama | Naoya Suzuki | Shinsuke Ishihara | Ryo Tamura | Mandeep K. Chahal | Daniel T. Payne | Kazuki Yoshizoe | Koji Tsuda
Source: ChemRxiv (preprint, 2021-03-26)
Field: Materials Science — Materials Chemistry

De Novo Creation of a Naked-Eye-Detectable Fluorescent Molecule Based on Quantum-Chemical Computation and Machine Learning Correlations between molecular properties and structures, such as those between the absorption wavelength and conjugate length, are beneficial for designing materials and controlling their properties. However, determining the molecular structures that correlate with the target molecular properties (such as molecular fluorescence) is not an easy task. In this study, we have used a de novo molecule generator (DNMG) coupled with quantum-chemical computation (QC) to develop new fluorescent molecules, which are garnering significant attention in various disciplines. With massive parallel computation (1024 cores, 5 days), DNMG has produced 3,643 candidate molecules within the density functional theory (DFT; one of QC) framework. Among the generated molecules, we have selected an unreported molecule and synthesized it for photoluminescence spectrum measurement. Our experimental verification demonstrated that DNMG can successfully create a new molecule which emits fluorescence detectable by the naked eye, as predicted by the DFT. Molecule ; Fluorescence ; Density functional theory ; Naked eye ; Computation ; Conjugate ; Photoluminescence ; Materials science ; Nanotechnology ; Chemical physics ; Chemistry ; Computer science ; Computational chemistry ; Physics ; Optoelectronics ; Optics ; Quantum mechanics ; Algorithm ; Mathematics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Photochromic and Fluorescence Chemistry
Authors: Marcos Andrade | Linfeng Zhang | Hsin‐Yu Ko | Grace M. Sommers | Roberto Car | Annabella Selloni
Source: Bulletin of the American Physical Society (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Expanding the time- and length-scale of ab initio molecular dynamics with deep neural network potentials Scale (ratio) ; Dynamics (music) ; Ab initio ; Artificial neural network ; Statistical physics ; Molecular dynamics ; Computer science ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science
Authors: 知輝 保田 | Tomoki Yasuda | 真一 大川原 | Shinichi Ookawara | 史郎 吉川 | Shiro Yoshikawa | 秀行 松本 | Hideyuki Matsumoto
Source: Institutional Repositories DataBase (IRDB) (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Machine Learning and Data-Driven Characterization Framework for Porous Materials: Permeability Prediction and Channeling Defect Detection Characterization (materials science) ; Permeability (electromagnetism) ; Porosity ; Pattern recognition (psychology) ; Support vector machine Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Composite Material Mechanics
Authors: Mostafa Yaghoobi | Mojtaba Alaei
Source: arXiv (Cornell University) (preprint, 2021-11-17)
Field: Materials Science — Materials Chemistry

Machine Learning for compositional disorder: A Comparison Between Different Descriptors Compositional disorder is common in crystal compounds. In these compounds, some atoms are randomly distributed at some crystallographic sites. For such compounds, randomness forms many non-identical independent structures. Thus, calculating the energy of all structures using ordinary quantum ab initio methods can be significantly time-consuming. Machine learning can be a reliable alternative to ab initio methods. We calculate the energy of these compounds with an accuracy close to that of density functional theory calculations in a considerably shorter time using machine learning. In this study, we use kernel ridge regression and neural network to predict energy. In the KRR, we employ sine matrix, Ewald sum matrix, SOAP, ACSF, and MBTR, and in the neural network method, we use the ACSF to convert the structures into suitable inputs to use in the machine learning models. We show that kernel ridge regression using MBTR and neural network using ACSF can provide better accuracy than other methods. Artificial neural network ; Ab initio ; Randomness ; Computer science ; Kernel (algebra) ; Energy (signal processing) ; Artificial intelligence ; Machine learning ; Algorithm ; Statistical physics ; Mathematics ; Physics ; Quantum mechanics ; Statistics ; Discrete mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Michael Maser | Sarah E. Reisman
Source: ChemRxiv (preprint, 2021-07-12)
Field: Materials Science — Materials Chemistry

3D Computer Vision Models Predict DFT-Level HOMO-LUMO Gap Energies from Force-Field-Optimized Geometries We investigate 3D deep learning methods for predicting quantum mechanical energies at high-theory-level accuracy from inexpensive, rapidly computed molecular geometries. Using space-filled volumetric representations (voxels), we explore the effects of radial decay from atom centers and rotational data augmentation on learnability. We test several published computer vision models for 3D shape learning, and construct our own architecture based on 3D inception networks with physically meaningful kernels. We provide a framework for further studies and propose a modeling challenge for the computer vision and molecular machine learning communities. Learnability ; Voxel ; Artificial intelligence ; Computer science ; Construct (python library) ; Deep learning ; HOMO/LUMO ; Field (mathematics) ; Physics ; Mathematics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis
Authors: Seiichi Hata
Source: The Proceedings of Mechanical Engineering Congress Japan (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Material search in the age of AI applying microfabrication In this paper, it is reviewed that combinatorial technologies for the fabrication and characterization of a large number of samples at once, the use of MEMS and other microfabrication technologies for the characterization of the samples, and our efforts to use machine learning to analyze and improve the search efficiency of the large number of sample data obtained by the combinatorial technologies. We introduce combinatorial arc plasma deposition, which enables the combinatorial deposition of amorphous alloy materials. The composition-graded films fabricated by this method are separated and labeled into thin-film libraries by using microfabrication methods, and their properties are evaluated using MEMS structures. As an example of material search with the aid of machine learning, we describe the identification of physical properties with high contribution to the current density of electrocatalysts by random forest analysis, the examination of search termination conditions by Bayesian optimization, and the estimation of crystal grain size of magnetic materials from Barkhausen noise by machine learning. Microfabrication ; Materials science ; Microelectromechanical systems ; Deposition (geology) ; Gyroscope ; Fabrication ; Nanotechnology ; Characterization (materials science) ; Computer science ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Theoretical and Computational Physics
Authors: José J. Plata | Víctor Posligua | Antonio M. Márquez | Javier Fdez. Sanz | Ricardo Grau‐Crespo
Source:  (preprint, 2021-09-17)
Field: Materials Science — Materials Chemistry

Fast, accurate and non-empirical determination of the lattice thermal conductivities of I-III-VI2 chalcopyrite semiconductors The use of computer simulation to predict the lattice thermal conductivity of materials has the potential to accelerate the discovery of new thermoelectric materials. However, the accurate prediction of this property from first principles, without input from experiment, is very computationally demanding, which limits the use of high-throughput strategies in thermoelectric materials design. We present here an accurate, fast, and non-empirical determination of the lattice thermal conductivities of a large family of semiconductors, with composition ABX2 (I-III-VI2), with A=Cu, Ag; B=Al, Ga, In, Tl; and X=S, Se, Te. We solve the Boltzmann transport equation with force constants derived from density functional theory calculations and machine-learning-based regression algorithms, reducing between one and two orders of magnitude the computational cost with respect to conventional approaches of the same accuracy. The results are in good agreement with available experimental data and allow us to rationalize the role of chemical composition, temperature and nanostructuring on the thermal conductivities across this important family of semiconductors. Thermoelectric effect ; Thermal conductivity ; Semiconductor ; Lattice (music) ; Chalcopyrite ; Thermal ; Density functional theory ; Materials science ; Computation ; Lattice constant ; Thermodynamics ; Lattice Boltzmann methods ; Condensed matter physics ; Computer science ; Statistical physics ; Computational physics ; Physics ; Chemistry ; Algorithm ; Computational chemistry ; Optoelectronics ; Quantum mechanics ; Diffraction Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2021-12-06)
Field: Materials Science — Materials Chemistry

Database for machine-learning research Researchers are launching an open-source database of chemical synthesis procedures that they think will benefit artificial intelligence algorithms for reaction prediction, synthesis planning, and other tasks ( J. Am. Chem. Soc. 2021, DOI: 10.1021/jacs.1c09820 ). Machine-learning algorithms typically become more accurate when they are trained on large amounts of data. Machine-learning tools that can identify faces and translate text have become ubiquitous and are accurate because their developers have been able to take advantage of open databases of text and images, according to Steven Kearnes of Relay Therapeutics, a member of the governing committee of the new Open Reaction Database (ORD). Chemists and computer scientists have already demonstrated machine-learning tools that can successfully plot synthetic routes, predict reaction products, and estimate the properties of new compounds. But Kearnes and his colleagues think access to more data is needed to accelerate progress in this area of research. The ORD is designed Computer science ; Artificial intelligence ; Machine learning ; Plot (graphics) ; Open source ; Database ; Software ; Programming language Machine Learning in Materials Science ; Advanced Data Processing Techniques
Authors: Hiroki Shibahara
Source: The Chemical Society of Japan (article, 2021-01-18)
Field: Chemistry — Inorganic Chemistry

Machine-Learning-Assisted Exploration of Synthesis Condition of Novel MOFs Containing Bi-S Bonds Computer science ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Organometallic Compounds Synthesis and Characterization
Authors: Philippe Leclère
Source: ORBi UMONS (article, 2021-04-19)
Field: Materials Science — Materials Chemistry

Towards the quantitative mapping of the mechanical properties of soft materials using unsupervised data clustering and deep learning methods ) Deep learning ; Cluster analysis ; Pattern recognition (psychology) ; Unsupervised learning ; Artificial neural network Machine Learning in Materials Science ; Composite Material Mechanics ; Metallurgy and Material Forming
Authors: Towfiq Ahmed | Mohammad Rezaul Karim | Magali Ferrandon | Edward F. Holby | Deborah J. Myers | Piotr Zelenay
Source: ECS Meeting Abstracts (article, 2021-10-19)
Field: Materials Science — Materials Chemistry

Optimizing High-Throughput Synthesis of PGM-Free ORR Electrocatalyst Using Machine Learning Approach Electrocatalyst ; Throughput ; Computer science ; High-throughput screening ; Chemistry ; Electrochemistry ; Electrode ; Biochemistry ; Telecommunications Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Fatemah Mukadum | Quan Nguyen | Daniel Adrion | Gabriel Appleby | Rui Chen | Haley Dang | Remco Chang | Roman Garnett | Steven A. Lopez
Source:  (preprint, 2021-08-09)
Field: Materials Science — Materials Chemistry

Efficient discovery of visible light-activated azoarene photoswitches with long half-lives using active search Photoswitches are molecules that undergo a reversible, structural isomerization after exposure to different wavelengths of light. The dynamic control offered by molecular photoswitches is favorable for applications in materials chemistry, photopharmacology, and catalysis. Ideal photoswitches absorb visible light and have long-lived metastable isomers. We used high throughput virtual screening to predict the absorption maxima (λmax) of the E-isomer and half-lives (t1/2) of the Z-isomer. However, computing the photophysical and kinetic properties of each entry of a virtual molecular library containing 103–106 entries with density functional theory is prohibitively time-consuming. We applied active search, a machine learning technique to intelligently search a chemical search space of 255991 photoswitches based on 29 known azoarenes and their derivatives. We iteratively trained the active search algorithm based on whether a candidate absorbed visible light (λmax &gt; 450 nm). Active search was found to triple the discovery rate compared to random search. Further, we projected 1962 photoswitches to 2D using the Uniform Manifold Approximation and Projection (umap) algorithm and found that λmax depends on the core, which is tunable with substituents. We then incorporated a second stage of screening with to predict the stabilities of the Z-isomers for the top 1% of candidates. We identified four ideal photoswitches that concurrently satisfy λmax &gt; 450 nm and t1/2 &gt; 2 hours; the range of λmax and t1/2 range from 465 to 531 nm and hours to years, respectively. Isomerization ; Virtual screening ; Spiropyran ; Photoswitch ; Chemistry ; Physics ; Computer science ; Photochromism ; Photochemistry ; Computational chemistry ; Molecular dynamics ; Catalysis Photochromic and Fluorescence Chemistry ; Advanced biosensing and bioanalysis techniques ; Machine Learning in Materials Science
Authors: Geemi P. Wellawatte | Zhiheng Li | Maghesree Chakraborty | Heta A Gandhi | Chenliang Xu | Andrew J. P. White
Source:  (preprint, 2021-09-08)
Field: Materials Science — Materials Chemistry

Predicting coarse-grained (CG) mappings using graph neural networks: Applications in CG molecular dynamics Computer science ; Artificial neural network ; Dynamics (music) ; Graph ; Artificial intelligence ; Theoretical computer science ; Physics ; Acoustics Machine Learning in Materials Science ; NMR spectroscopy and applications
Authors: Xinqiao Wang | Chuansheng Yao | Yun Zhang | Jiahui Yu | Haoran Qiao | Chengyun Zhang | Yejian Wu | Renren Bai | Hongliang Duan
Source:  (preprint, 2021-08-06)
Field: Materials Science — Materials Chemistry

From Theory to Experiment: Transformer-Based Generation Enables Rapid Discovery of Novel Reactions Deep learning methods have been proven their potential roles in the chemical field, such as reaction prediction and retrosynthesis analysis. However, the de novo generation of unreported reactions using artificial intelligence technology remains not be completely explored. Inspired by molecular generation, we proposed the task of novel reaction generation. In this work, we applied the Heck reactions to train the transformer model, state-of-art natural language process model and obtained 4717 generated reactions after sampling and processing. We then confirmed that 2253 novel Heck reactions by organizing chemists to judge the generated reactions, and adopted organic synthesis experiment to verify the feasibility of unreported reactions. In this process, it only took 15 days from Heck reaction generation to experimental verification, proving that our model learns reaction rules in-depth and can make great contributions in the novel reaction discovery. Transformer ; Computer science ; Retrosynthetic analysis ; Artificial intelligence ; Heck reaction ; Process (computing) ; Chemistry ; Engineering ; Programming language ; Organic chemistry ; Palladium ; Electrical engineering Machine Learning in Materials Science
Authors: Jacqui Cole
Source: ePrints Soton (University of Southampton) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Video: Data-driven materials discovery for functional applications Large-scale data-mining workflows are increasingly able to predict successfully new chemicals that possess a targeted functionality. The success of such materials discovery approaches is nonetheless contingent upon having the right data source to mine, adequate supercomputing facilities and machine-learning workflows to calculate or sample a large range of materials, and algorithms that suitably encode structure-function relationships as datamining workflows which progressively short list data toward the prediction of a lead material for experimental validation. This talk shows how to meet these data-science requirements via 'chemistry-aware' natural language processing, image recognition and machine learning developments using case studies to showcase their successful application to data-driven materials discovery. Workflow ; Computer science ; Data science ; Data type ; Data mining ; Function (biology) ; Range (aeronautics) ; Machine learning ; Artificial intelligence ; Database ; Programming language ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: David Kreutter | Philippe Schwaller | Jean‐Louis Reymond
Source: The Novartis Repository (Novartis) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Predicting Biotransformations with a Molecular Transformer The use of enzymes for organic synthesis allows for simplified, more economical and selective synthetic routes not accessible to conventional reagents. However, predicting whether a particular molecule might undergo a specific enzyme transformation is very difficult. Here we exploited recent advances in computer assisted synthetic planning (CASP) by considering the molecular transformer, which is a sequence-to-sequence machine learning model that can be trained to predict the products of organic transformations, including their stereochemistry, from the structure of reactants and reagents. We used multi-task transfer learning to train the molecular transformer with one million reactions from the US Patent Office (USPTO) database as a source of general chemistry knowledge combined with 32,000 enzymatic transformations, each one annotated with a text description of the enzyme. We show that the resulting Enzymatic Transformer model predicts the products formed from a given substrate and enzyme with remarkable accuracy, including typical kinetic resolution processes. Transformer ; Computer science ; Organic molecules ; Reagent ; Chemistry ; Enzyme ; CASP ; Combinatorial chemistry ; Substrate specificity ; Artificial intelligence ; Molecule ; Protein structure ; Engineering ; Biochemistry ; Organic chemistry ; Protein structure prediction ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Metabolic Engineering and Bioproduction
Authors: David Winkler | Tim Würger | D Mei | B Vaghefinazari | Sviatlana V. Lamaka | M Zheludkevich | R Meißner | Christian Feiler
Source: La Trobe University (article, 2021-01-08)
Field: Materials Science — Materials Chemistry

Exploring the Structure-Property Relationship of Magnesium Dissolution Modulators https://www.nature.com/articles/s41529-020-00148-zAbstract: Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-based quantitative structure-property relationship models, accelerating the discovery of potent dissolution modulators. Here, we demonstrate how unsupervised clustering of a large number of potential Mg dissolution modulators by structural similarities and sketch-maps can predict their experimental performance using a kernel ridge regression model. We compare the prediction accuracy of this approach to that of a prior artificial neural networks study. We confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates the automated discovery of chemicals with desired dissolution modulating properties from a commercial database. We subsequently prove this concept by blind validation of five chemicals. Dissolution ; Robustness (evolution) ; Workflow ; Cluster analysis ; Artificial neural network ; Kernel (algebra) ; Principal component regression Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography