jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Merian Crumbach | Jonas Bachmann | Lars Fritze | Andreas Helbig | Ivo Krummenacher | Holger Braunschweig | Holger Helten
Source: Angewandte Chemie International Edition (article, 2021-02-01)
Field: Chemistry — Organic Chemistry

Dithiophene‐Fused Oxadiborepins and Azadiborepins: A New Class of Highly Fluorescent Heteroaromatics Access to dithiophene-fused oxadiborepins and the first azadiborepins attained via a modular synthesis route are presented. The new compounds emit intense blue light, some of which demonstrate fluorescence quantum yields close to unity. Cyclic voltammetry (CV) revealed electrochemically reversible one-electron reduction processes. The weak aromatic character of the novel 1,2,7-azadiborepin ring is demonstrated with in-depth theoretical investigations using nucleus-independent chemical shift (NICS) scans and anisotropy of the induced current density (ACID) calculations. Fluorescence ; Cyclic voltammetry ; Ring (chemistry) ; Anisotropy ; Character (mathematics) ; Materials science ; Photochemistry ; Chemistry ; Physical chemistry ; Electrochemistry ; Organic chemistry ; Electrode ; Physics ; Optics ; Mathematics Organoboron and organosilicon chemistry ; Organic and Molecular Conductors Research ; Organic Light-Emitting Diodes Research
Authors: Kishan Zadafiya | Prassan Shah | Alborz Shokrani | Navneet Khanna
Source: Journal of Manufacturing Processes (article, 2021-06-07)
Field: Materials Science — Materials Chemistry

Recent advancements in nano-lubrication strategies for machining processes considering their health and environmental impacts Lubrication ; Machining ; Nanofluid ; Scope (computer science) ; Mechanical engineering ; Materials science ; Environmental impact assessment ; Cutting fluid ; Manufacturing engineering ; Nanotechnology ; Engineering ; Computer science ; Nanoparticle Nanoparticles: synthesis and applications ; Lubricants and Their Additives ; Injection Molding Process and Properties
Authors: Kevin C. Leonard | M. M. Faruque Hasan | Helen F. Sneddon | Fengqi You
Source: ACS Sustainable Chemistry & Engineering (article, 2021-05-10)
Field: Materials Science — Materials Chemistry

Can Artificial Intelligence and Machine Learning Be Used to Accelerate Sustainable Chemistry and Engineering? ADVERTISEMENT RETURN TO ISSUEEditorialNEXTCan Artificial Intelligence and Machine Learning Be Used to Accelerate Sustainable Chemistry and Engineering?Kevin C. LeonardKevin C. LeonardCenter for Environmentally Beneficial Catalysis Department of Chemical & Petroleum Engineering, The University of Kansas, Lawrence, Kansas 66045, United StatesMore by Kevin C. Leonardhttp://orcid.org/0000-0002-0172-3150, Faruque HasanFaruque HasanDepartment of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United StatesMore by Faruque Hasanhttp://orcid.org/0000-0001-9338-6069, Helen F. SneddonHelen F. SneddonMedicinal Chemistry, GSK Medicines Research Centre, Stevenage, Hertfordshire SG1 2NY, United KingdomMore by Helen F. Sneddon, and Fengqi YouFengqi YouSmith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14850, United StatesMore by Fengqi Youhttp://orcid.org/0000-0001-9609-4299Cite this: ACS Sustainable Chem. Eng. 2021, 9, 18, 6126–6129Publication Date (Web):May 10, 2021Publication History Received22 April 2021Published online10 May 2021Published inissue 10 May 2021https://pubs.acs.org/doi/10.1021/acssuschemeng.1c02741https://doi.org/10.1021/acssuschemeng.1c02741editorialACS PublicationsCopyright © Published 2021 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views7053Altmetric-Citations12LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-AlertscloseSUBJECTS:Algorithms,Catalysts,Chemical engineering and industrial chemistry,Computational chemistry,Machine learning Get e-Alerts Citation ; Library science ; Artificial intelligence ; Chemistry ; Computer science Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Various Chemistry Research Topics
Authors: Jiechun Liang | Shangqian Xu | Linfeng Hu | Yu Zhao | Xi Zhu
Source: Materials Chemistry Frontiers (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted low dielectric constant polymer discovery A machine learning method is combined with SMILES string processing to classify polymers based on their dielectric properties and generate new low dielectric polymers. The generated polymers are synthesized using a cloud lab. Dielectric ; Polymer ; Materials science ; String (physics) ; Cloud computing ; Composite material ; Computer science ; Optoelectronics ; Physics ; Theoretical physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Xingchen Dong | Hongwei Li | Zhutong Jiang | Theresa Grünleitner | İnci Güler | Jie Dong | Kun Wang | Michael H. Köhler | Martin Jakobi | Bjoern Menze | Ali K. Yetisen | Ian D. Sharp | Andreas V. Stier | Jonathan J. Finley | Alexander W. Koch
Source: ACS Nano (article, 2021-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

3D Deep Learning Enables Accurate Layer Mapping of 2D Materials Layered, two-dimensional (2D) materials are promising for next-generation photonics devices. Typically, the thickness of mechanically cleaved flakes and chemical vapor deposited thin films is distributed randomly over a large area, where accurate identification of atomic layer numbers is time-consuming. Hyperspectral imaging microscopy yields spectral information that can be used to distinguish the spectral differences of varying thickness specimens. However, its spatial resolution is relatively low due to the spectral imaging nature. In this work, we present a 3D deep learning solution called DALM (deep-learning-enabled atomic layer mapping) to merge hyperspectral reflection images (high spectral resolution) and RGB images (high spatial resolution) for the identification and segmentation of MoS<sub>2</sub> flakes with mono-, bi-, tri-, and multilayer thicknesses. DALM is trained on a small set of labeled images, automatically predicts layer distributions and segments individual layers with high accuracy, and shows robustness to illumination and contrast variations. Further, we show its advantageous performance over the state-of-the-art model that is solely based on RGB microscope images. This AI-supported technique with high speed, spatial resolution, and accuracy allows for reliable computer-aided identification of atomically thin materials. Hyperspectral imaging ; Materials science ; RGB color model ; Image resolution ; Microscopy ; Spectral imaging ; Chemical imaging ; Artificial intelligence ; Segmentation ; Computer science ; Optics ; Physics Ga2O3 and related materials ; Analytical Chemistry and Sensors ; 2D Materials and Applications
Authors: Xueying Yuan | Lifeng Li | Zenan Shi | Hong Liang | Shuhua Li | Zhiwei Qiao
Source: Advanced Powder Materials (article, 2021-12-20)
Field: Chemistry — Inorganic Chemistry

Molecular-fingerprint machine-learning-assisted design and prediction for high-performance MOFs for capture of NMHCs from air The capture of trace amounts of non-methane hydrocarbons (NMHCs) from air due to the toxicity of volatile organic compounds is a significant challenge. A total of 31399 hydrophobic metal–organic frameworks (MOFs) were first screened from 137953 hypothetical MOFs using high-throughput computational screening (HTCS), and their performance indices (adsorption capacity and selectivity) for the adsorption of NMHCs (C3–C6) were obtained by molecular simulations. The discovery of a “second peak” near twice the kinetic diameter of the corresponding NMHC provided more choices for excellent MOFs that adsorb NMHCs. Four machine learning (ML) classification and regression algorithms predicted the performance of MOFs, and the relative importance values of the six descriptors were determined. The combination of the Random Forests algorithm and Molecular ACCess Systems molecular fingerprint (MF) had an excellent predictive ability for MOFs. According to the performance, the fingerprint commonalities of the 100 top-performing MOFs were counted, and the excellent bits (EBs) that could promote the performance were defined. Finally, new substructures containing all of the EBs were designed for each NMHC to build a new MOF database. This work combined the HTCS, ML, and MF to provide a detailed insight into the design of efficient MOFs for adsorbing NMHCs. Fingerprint (computing) ; Artificial intelligence ; Computer science ; Environmental science Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Reactions ; Machine Learning in Materials Science
Authors: Octavian-Eugen Ganea | Lagnajit Pattanaik | Connor W. Coley | Regina Barzilay | Klavs F. Jensen | William H. Green | Tommi Jaakkola
Source: arXiv (Cornell University) (preprint, 2021-06-08)
Field: Materials Science — Materials Chemistry

GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements (e.g. torsion angles), separate optimization stages prone to error accumulation, and the need for structure fine-tuning based on approximate classical force-fields or computationally expensive methods such as metadynamics with approximate quantum mechanics calculations at each geometry. We propose GeoMol--an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers. Leveraging the power of message passing neural networks (MPNNs) to capture local and global graph information, we predict local atomic 3D structures and torsion angles, avoiding unnecessary over-parameterization of the geometric degrees of freedom (e.g. one angle per non-terminal bond). Such local predictions suffice both for the training loss computation, as well as for the full deterministic conformer assembly (at test time). We devise a non-adversarial optimal transport based loss function to promote diverse conformer generation. GeoMol predominantly outperforms popular open-source, commercial, or state-of-the-art machine learning (ML) models, while achieving significant speed-ups. We expect such differentiable 3D structure generators to significantly impact molecular modeling and related applications. Conformational isomerism ; Computer science ; Maxima and minima ; Differentiable function ; Computation ; Molecular dynamics ; Cheminformatics ; Artificial intelligence ; Statistical physics ; Algorithm ; Theoretical computer science ; Computational chemistry ; Mathematics ; Physics ; Chemistry ; Molecule ; Quantum mechanics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Félix Musil | Max Veit | Alexander Goscinski | Guillaume Fraux | Michael J. Willatt | Markus Stricker | Till Junge | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Efficient implementation of atom-density representations Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materials and molecules and to explore and visualize their chemical structures and compositions. Recently, it has become clear that many of the most effective representations share a fundamental formal connection. They can all be expressed as a discretization of n-body correlation functions of the local atom density, suggesting the opportunity of standardizing and, more importantly, optimizing their evaluation. We present an implementation, named librascal, whose modular design lends itself both to developing refinements to the density-based formalism and to rapid prototyping for new developments of rotationally equivariant atomistic representations. As an example, we discuss smooth overlap of atomic position (SOAP) features, perhaps the most widely used member of this family of representations, to show how the expansion of the local density can be optimized for any choice of radial basis sets. We discuss the representation in the context of a kernel ridge regression model, commonly used with SOAP features, and analyze how the computational effort scales for each of the individual steps of the calculation. By applying data reduction techniques in feature space, we show how to reduce the total computational cost by a factor of up to 4 without affecting the model's symmetry properties and without significantly impacting its accuracy. Formalism (music) ; Modular design ; Kernel (algebra) ; Context (archaeology) ; Representation (politics) ; Computation ; Equivariant map Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Carsten G. Staacke | Hendrik H. Heenen | Christoph Scheurer | Gábor Cśanyi | Karsten Reuter | Johannes T. Margraf
Source: ACS Applied Energy Materials (article, 2021-11-02)
Field: Materials Science — Materials Chemistry

On the Role of Long-Range Electrostatics in Machine-Learned Interatomic Potentials for Complex Battery Materials Modeling complex energy materials such as solid-state electrolytes (SSEs) realistically at the atomistic level strains the capabilities of state-of-the-art theoretical approaches. On one hand, the system sizes and simulation time scales required are prohibitive for first-principles methods such as the density functional theory. On the other hand, parameterizations for empirical potentials are often not available, and these potentials may ultimately lack the desired predictive accuracy. Fortunately, modern machine learning (ML) potentials are increasingly able to bridge this gap, promising first-principles accuracy at a much reduced computational cost. However, the local nature of these ML potentials typically means that long-range contributions arising, for example, from electrostatic interactions are neglected. Clearly, such interactions can be large in polar materials such as electrolytes, however. Herein, we investigate the effect that the locality assumption of ML potentials has on lithium mobility and defect formation energies in the SSE Li7P3S11. We find that neglecting long-range electrostatics is unproblematic for the description of lithium transport in the isotropic bulk. In contrast, (field-dependent) defect formation energies are only adequately captured by a hybrid potential combining ML and a physical model of electrostatic interactions. Broader implications for ML-based modeling of energy materials are discussed. Electrostatics ; Range (aeronautics) ; Statistical physics ; Isotropy ; Battery (electricity) ; Computer science ; Field (mathematics) ; Electrolyte ; Locality ; Physics ; Materials science ; Thermodynamics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fuel Cells and Related Materials
Authors: Andrew J. Lew | Markus J. Buehler
Source: Forces in Mechanics (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model Variational autoencoders (VAE) are machine learning models that can extract low dimensional representations of data from datasets of high complexity and volume. Importantly, they can be used for generative purposes to reconstruct complex data, such as images, from a low dimensional encoding of only a few variables. Long short-term memory (LSTM) neural networks are well suited to learning logical trajectory relationships within datasets. Using these two models in concert, we develop a VAE-LSTM approach to learn a classic mechanical materials design problem. Here, we focus on the compliance optimization of cantilever design, using a VAE to encode cantilever structures into a 2D latent space and a LSTM to learn trajectories in that latent space corresponding to the optimization process. Ultimately, we are able to clearly visualize the space of cantilever design, generate new design with extremely low density beyond the original dataset, and obtain optimal cantilever structures inspired by nature. We also demonstrate how the resulting designs can be manufactured using 3D printing, offering a rapid pathway from concept to prototype. The method we developed here can be generalized to other image-based datasets encapsulating changes from multiple factors. The ability offered by our approach to interpret complex behavior, via representations in simplified space, has great potential for application in the intelligent design and manufacturing of materials structure problems. Computer science ; Encoding (memory) ; Artificial intelligence ; Space (punctuation) ; Cantilever ; Focus (optics) ; Process (computing) ; ENCODE ; Trajectory ; Generative model ; Machine learning ; Generative grammar ; Engineering Machine Learning in Materials Science ; Topology Optimization in Engineering ; Manufacturing Process and Optimization
Authors: Nang Xuan Ho | Tien-Thinh Le | Minh Vuong Le
Source: Mechanics of Advanced Materials and Structures (article, 2021-09-07)
Field: Materials Science — Materials Chemistry

Development of artificial intelligence based model for the prediction of Young’s modulus of polymer/carbon-nanotubes composites In this paper, an Artificial Intelligence (AI) model is constructed for the behavior prediction, i.e. Young’s modulus, of polymer/carbon-nanotube (CNTs) composites. The AI is proposed to overcome the difficulties when studying the properties of novel composite materials, for example the time-consuming of experimental studies of resource-consuming of other numerical methods. Artificial Neural Network (ANN) model was chosen and optimized in architecture based on a parametric study. The main objective of this study is to firstly confirm that the proposed AI method performs well for nanocomposites and it can then be optimized in terms of computational time and resources in further studies. The obtained results have shown that the proposed model exhibits great performance in both training and testing phases, where the correlation coefficient is 0.986 for training part and 0.978 for the testing part. Carbon nanotube ; Modulus ; Artificial neural network ; Materials science ; Composite number ; Parametric statistics ; Composite material ; Nanocomposite ; Polymer ; Artificial intelligence ; Computer science ; Mathematics Carbon Nanotubes in Composites ; Smart Materials for Construction ; Machine Learning in Materials Science
Authors: Jiayan Xu | Xiaoming Cao | P. Hu
Source: Journal of Chemical Theory and Computation (article, 2021-06-08)
Field: Materials Science — Materials Chemistry

Accelerating Metadynamics-Based Free-Energy Calculations with Adaptive Machine Learning Potentials There is an increasing demand for free-energy calculations using ab initio molecular dynamics these days. Metadynamics (MetaD) is frequently utilized to reconstruct the free-energy surface, but it is often computationally intractable for the first-principles calculations. Machine learning potentials (MLPs) have become popular alternatives. However, the training could be a long and arduous process before using them in practical applications. To accelerate MetaD use with MLPs for the free-energy calculation in an easy manner, we propose the adaptive machine learning potential-accelerated metadynamics (AMLP-MetaD). In this method, the MLP in the form of a Gaussian approximation potential (GAP) can adapt itself based on its uncertainty estimation, which decides whether to accept the model prediction or recalculate it with a reference method (usually density functional theory) for further training during the MetaD simulation. We demonstrate that the free-energy landscape similar to the ab initio one can be obtained using AMLP-MetaD with a 10-time speedup. Moreover, the quality of the free-energy results can be deeply improved using Δ-MLP, which is the GAP-corrected density functional tight binding in our case. We exemplify this novel method with two model systems, CO adsorption on the Pt<sub>13</sub> cluster and the Pt(111) surface, which are of vital importance in heterogeneous catalysis. The successful application in these two tests highlights that our proposed method can be used in both cluster and periodic systems and for up to two collective variables. Metadynamics ; Computer science ; Energy landscape ; Ab initio ; Energy (signal processing) ; Density functional theory ; Artificial intelligence ; Ab initio quantum chemistry methods ; Statistical physics ; Machine learning ; Molecular dynamics ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Catalysis and Oxidation Reactions
Authors: Logan Ward | Ganesh Sivaraman | J. Gregory Pauloski | Yadu Babuji | Ryan Chard | Naveen K. Dandu | Paul C. Redfern | Rajeev S. Assary | Kyle Chard | Larry A. Curtiss | Rajeev Thakur | Ian Foster
Source:  (preprint, 2021-11-01)
Field: Materials Science — Materials Chemistry

Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform. Methods that use machine learning (ML) to create proxy models of simulations show particular promise for guiding ensembles but are challenging to deploy because of the need to coordinate dynamic mixes of simulation and learning tasks. We present Colmena, an open-source Python framework that allows users to steer campaigns by providing just the implementations of individual tasks plus the logic used to choose which tasks to execute when. Colmena handles task dispatch, results collation, ML model invocation, and ML model (re)training, using Parsl to execute tasks on HPC systems. We describe the design of Colmena and illustrate its capabilities by applying it to electrolyte design, where it both scales to 65536 CPUs and accelerates the discovery rate for high-performance molecules by a factor of 100 over unguided searches. Computer science ; Scalability ; Python (programming language) ; Implementation ; Machine learning ; Factor (programming language) ; Performance tuning ; Artificial intelligence ; Programming language ; Operating system Machine Learning in Materials Science ; Scientific Computing and Data Management ; Software Engineering Research