jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: R. Mortazavi | S. S. Mortazavi | Alicia Troncoso
Source: Engineering With Computers (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Wrapper-based feature selection using regression trees to predict intrinsic viscosity of polymer Feature selection ; Linear regression ; Regression ; Bayesian multivariate linear regression ; Artificial intelligence ; Context (archaeology) ; Support vector machine ; Decision tree ; Regression analysis ; A priori and a posteriori ; Machine learning ; Proper linear model ; Tree (set theory) ; Multivariate statistics ; Computer science ; Mathematics ; Multivariate adaptive regression splines ; Artificial neural network ; Data mining ; Statistics Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Zhengyuan Shen | Yangzesheng Sun | Timothy P. Lodge | J. Ilja Siepmann
Source: The Journal of Physical Chemistry B (article, 2021-05-14)
Field: Materials Science — Materials Chemistry

Development of a PointNet for Detecting Morphologies of Self-Assembled Block Oligomers in Atomistic Simulations Molecular simulations with atomistic or coarse-grained force fields are a powerful approach for understanding and predicting the self-assembly phase behavior of complex molecules. Amphiphiles, block oligomers, and block polymers can form mesophases with different ordered morphologies describing the spatial distribution of the blocks, but entirely amorphous nature for local packing and chain conformation. Screening block oligomer chemistry and architecture through molecular simulations to find promising candidates for functional materials is aided by effective and straightforward morphology identification techniques. Capturing 3-dimensional periodic structures, such as ordered network morphologies, is hampered by the requirement that the number of molecules in the simulated system and the shape of the periodic simulation box need to be commensurate with those of the resulting network phase. Common strategies for structure identification include structure factors and order parameters, but these fail to identify imperfect structures in simulations with incorrect system sizes. Building upon pioneering work by DeFever et al. [<i>Chem. Sci.</i> <b>2019</b>, <i>10</i>, 7503-7515] who implemented a PointNet (i.e., a neural network designed for computer vision applications using point clouds) to detect local structure in simulations of single-bead particles and water molecules, we present a PointNet for detection of nonlocal ordered morphologies of complex block oligomers. Our PointNet was trained using atomic coordinates from molecular dynamics simulation trajectories and synthetic point clouds for ordered network morphologies that were absent from previous simulations. In contrast to prior work on simple molecules, we observe that large point clouds with 1000 or more points are needed for the more complex block oligomers. The trained PointNet model achieves an accuracy as high as 0.99 for globally ordered morphologies formed by linear diblock, linear triblock, and 3-arm and 4-arm star-block oligomers, and it also allows for the discovery of emerging ordered patterns from nonequilibrium systems. Block (permutation group theory) ; Molecular dynamics ; Materials science ; Computer science ; Chemical physics ; Nanotechnology ; Biological system ; Statistical physics ; Chemistry ; Physics ; Computational chemistry ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Scott D. Midgley | Said Hamad | Keith T. Butler | Ricardo Grau‐Crespo
Source: The Journal of Physical Chemistry Letters (article, 2021-05-25)
Field: Materials Science — Materials Chemistry

Bandgap Engineering in the Configurational Space of Solid Solutions via Machine Learning: (Mg,Zn)O Case Study Computer simulations of alloys' properties often require calculations in a large space of configurations in a supercell of the crystal structure. A common approach is to map density functional theory results into a simplified interaction model using so-called cluster expansions, which are linear on the cluster correlation functions. Alternative descriptors have not been sufficiently explored so far. We show here that a simple descriptor based on the Coulomb matrix eigenspectrum clearly outperforms the cluster expansion for both total energy and bandgap energy predictions in the configurational space of a MgO-ZnO solid solution, a prototypical oxide alloy for bandgap engineering. Bandgap predictions can be further improved by introducing non-linearity via gradient-boosted decision trees or neural networks based on the Coulomb matrix descriptor. Band gap ; Coulomb ; Cluster expansion ; Cluster (spacecraft) ; Space (punctuation) ; Materials science ; Matrix (chemical analysis) ; Density functional theory ; Statistical physics ; Topology (electrical circuits) ; Condensed matter physics ; Computer science ; Physics ; Computational chemistry ; Mathematics ; Chemistry ; Thermodynamics ; Quantum mechanics ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Non-Destructive Testing Techniques
Authors: Robert Bell | Peter A. Beaucage | Marc J. Murphy | Aine Connolly | Ulrich Wiesner | David S. Ginley | R. B. van Dover | Michael O. Thompson
Source: Chemistry of Materials (article, 2021-06-06)
Field: Materials Science — Materials Chemistry

Rapid Identification of Synthetic Routes to Functional Metastable Phases Using X-ray Probed Laser Anneal Mapping (XPLAM) Time–Temperature Quench Maps Many material systems have known or predicted functional phases that are metastable at standard temperature and pressure. While substantial advances have been made in the high-throughput and combinatorial synthesis of materials with a range of stoichiometries, investigation of thermal processing remains largely the domain of iterative uniform anneals or static gradients. Here we develop X-ray probed laser anneal mapping (XPLAM), a high throughput technique coupling spatially resolved X-ray diffraction with microsecond to millisecond laser gradient anneals to produce temperature–dwell–transformation (TDT) diagrams of the phase as a function of quench time and temperature. In addition to showing regimes where specific metastable phases form preferentially, TDT diagrams provide insight into the submillisecond kinetics of solid–solid phase transitions. This is a unique tool for mapping reaction pathways for metastable phases. As a first demonstration of XPLAM, we study Bi2O3, which has a rich set of polytypes, including the δ-phase with an exceptionally high oxygen ion conductivity. We demonstrate the first annealing-driven synthesis of room temperature δ-Bi2O3. We expect XPLAM to prove a powerful technique for rapid identification of synthetic routes to metastable phases and to generate the exhaustive data sets required for machine learning-guided exploration of materials processing. Metastability ; Materials science ; Microsecond ; Phase diagram ; Millisecond ; Annealing (glass) ; Diffraction ; Atmospheric temperature range ; Phase (matter) ; Chemical physics ; Chemistry ; Thermodynamics ; Optics ; Physics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Catalysis and Oxidation Reactions
Authors: Jonathan Godwin | Michael Schaarschmidt | Alexander L. Gaunt | Álvaro Sánchez‐González | Yulia Rubanova | Petar Veličković | James Kirkpatrick | Peter Battaglia
Source: arXiv (Cornell University) (preprint, 2021-06-15)
Field: Materials Science — Materials Chemistry

Very Deep Graph Neural Networks Via Noise Regularisation. Graph Neural Networks (GNNs) perform learned message passing over an input graph, but conventional wisdom says performing more than handful of steps makes training difficult and does not yield improved performance. Here we show the contrary. We train a deep GNN with up to 100 message passing steps and achieve several state-of-the-art results on two challenging molecular property prediction benchmarks, Open Catalyst 2020 IS2RE and QM9. Our approach depends crucially on a novel but simple regularisation method, which we call ``Noisy Nodes'', in which we corrupt the input graph with noise and add an auxiliary node autoencoder loss if the task is graph property prediction. Our results show this regularisation method allows the model to monotonically improve in performance with increased message passing steps. Our work opens new opportunities for reaping the benefits of deep neural networks in the space of graph and other structured prediction problems. Computer science ; Deep neural networks ; Graph ; Message passing ; Autoencoder ; Artificial neural network ; Monotonic function ; Theoretical computer science ; Noise (video) ; Artificial intelligence ; Machine learning ; Distributed computing ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Florian Fricke | Marcelo Brandalero | Sascha Liehr | Simon Kern | Klas Meyer | Stefan Kowarik | Robin Hierzegger | Stephan Westerdick | Michael Maiwald | Michael Hübner
Source: IEEE Transactions on Emerging Topics in Computing (article, 2021-12-06)
Field: Chemistry — Analytical Chemistry

Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control. Computer science ; Artificial neural network ; Automation ; Context (archaeology) ; Process (computing) ; Mass spectrometry ; Process control ; Artificial intelligence ; Chemistry ; Engineering ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Chemical Sensor Technologies
Authors: Itzik Malkiel | Michael Mrejen | Lior Wolf | Haim Suchowski
Source: Optics Letters (article, 2021-03-23)
Field: Materials Science — Materials Chemistry

Inverse design of unparametrized nanostructures by generating images from spectra Recently, there has been an increasing number of studies applying machine learning techniques for the design of nanostructures. Most of these studies train a deep neural network (DNN) to approximate the highly nonlinear function of the underlying physical mapping between spectra and nanostructures. At the end of training, the DNN allows an on-demand design of nanostructures, i.e., the model can infer nanostructure geometries for desired spectra. While these approaches have presented a new paradigm, they are limited in the complexity of the structures proposed, often bound to parametric geometries. Here we introduce spectra2pix, which is a DNN trained to generate 2D images of the target nanostructures. By predicting an image, our model architecture is not limited to a closed set of nanostructure shapes, and can be trained for the design of a much wider space of geometries. We show, for the first time, to the best of our knowledge, a successful generalization ability, by designing completely unseen shapes of geometries. We attribute the successful generalization to the ability of a pixel-wise architecture to learn local properties of the meta-material, therefore mimicking faithfully the underlying physical process. Importantly, beyond synthetical data, we show our model generalization capability on real experimental data. Computer science ; Generalization ; Artificial neural network ; Parametric statistics ; Nanostructure ; Artificial intelligence ; Set (abstract data type) ; Pixel ; Inverse problem ; Algorithm ; Machine learning ; Mathematics ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Photonic Crystals and Applications
Authors: Yeongdong Lee | Jongyeong Lee | Handolsam Chung | Jaemin Kim | Zonghoon Lee
Source: ACS Omega (article, 2021-08-10)
Field: Materials Science — Materials Chemistry

In Situ Scanning Transmission Electron Microscopy Study of MoS<sub>2</sub> Formation on Graphene with a Deep-Learning Framework Atomic-scale information is essential for understanding and designing unique structures and properties of two-dimensional (2D) materials. Recent developments in in situ transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) enable research to provide abundant insights into the growth of nanomaterials. In this study, 2D MoS<sub>2</sub> is synthesized on a suspended graphene substrate inside a TEM column through thermolysis of the ammonium tetrathiomolybdate (NH<sub>4</sub>)<sub>2</sub>MoS<sub>4</sub> precursor at 500 °C. To avoid misinterpretation of the in situ STEM images, a deep-learning framework, DeepSTEM, is developed. The DeepSTEM framework successfully reconstructs an object function in atomic-resolution STEM imaging for accurate determination of the atomic structure and dynamic analysis. In situ STEM imaging with DeepSTEM enables observation of the edge configuration, formation, and reknitting progress of MoS<sub>2</sub> clusters with the formation of a mirror twin boundary. The synthesized MoS<sub>2</sub>/graphene heterostructure shows various twist angles, as revealed by atomic-resolution TEM. This deep-learning framework-assisted in situ STEM imaging provides atomic information for in-depth studies on the growth and structure of 2D materials and shows the potential use of deep-learning techniques in 2D material research. Graphene ; Scanning transmission electron microscopy ; Materials science ; Nanotechnology ; Transmission electron microscopy ; Atomic units ; Substrate (aquarium) ; In situ ; Chemistry ; Physics 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Mahboube Mohamadi | Seyedemad Alavitabari | Mortaza Aliasghary
Source: Polymer Bulletin (article, 2021-05-29)
Field: Materials Science — Polymers and Plastics

Prediction of mechanical and thermal properties in bronze-filled polyamide 66 composites using artificial neural network Materials science ; Composite material ; Polyamide ; Ultimate tensile strength ; Izod impact strength test ; Scanning electron microscope ; Shore durometer ; Artificial neural network ; Thermal stability ; Bronze ; Metallurgy ; Computer science Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Tribology and Wear Analysis
Authors: Nima Zoghipour | Gökhan Atay | Yusuf Kaynak
Source: Procedia CIRP (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Modeling and optimization of drilling operation of lead-free brass alloys considering various cutting tool geometries and copper content Machinability, stable productivity, and quality of the components are crucial factors in manufacturing industry. In industrial applications, when cutting parameters and conditions are determined considering experiences gained by engineers which does not resolve the whole problems faced with in case cutting tools and their geometries are involved. Thus, it requires further knowledge that is built through systematic and scientific research. This study presents the results of an experimental investigation of a drilling process of hot forged lead-free brass alloys by using a form cutting tool. Experimental data on cutting forces, dimensional accuracy, and surface quality of the holes is presented considering the tools with different geometries, feed rates and rotational speeds on hot forged lead-free brass alloys with various copper content. Artificial neural networks modelling, and genetic algorithm-based optimization methods have been used to predict and optimize the machining process and related responses. The computational results demonstrated that the developed model and optimization procedure can notably improve the efficiency of the process by reducing the production cost with the obtained optimum machining conditions. Brass ; Machinability ; Machining ; Drilling ; Mechanical engineering ; Process (computing) ; Quality (philosophy) ; Genetic algorithm ; Artificial neural network ; Process engineering ; Manufacturing engineering ; Engineering ; Materials science ; Copper ; Metallurgy ; Computer science ; Artificial intelligence ; Machine learning Metallurgy and Material Science ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: J. Brandstetter | Rob Hesselink | Elise van der Pol | Erik J. Bekkers | Max Welling
Source: arXiv (Cornell University) (preprint, 2021-10-06)
Field: Materials Science — Materials Chemistry

Geometric and Physical Quantities Improve E(3) Equivariant Message Passing Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry. We introduce Steerable E(3) Equivariant Graph Neural Networks (SEGNNs) that generalise equivariant graph networks, such that node and edge attributes are not restricted to invariant scalars, but can contain covariant information, such as vectors or tensors. This model, composed of steerable MLPs, is able to incorporate geometric and physical information in both the message and update functions. Through the definition of steerable node attributes, the MLPs provide a new class of activation functions for general use with steerable feature fields. We discuss ours and related work through the lens of equivariant non-linear convolutions, which further allows us to pin-point the successful components of SEGNNs: non-linear message aggregation improves upon classic linear (steerable) point convolutions; steerable messages improve upon recent equivariant graph networks that send invariant messages. We demonstrate the effectiveness of our method on several tasks in computational physics and chemistry and provide extensive ablation studies. Equivariant map ; Covariant transformation ; Invariant (physics) ; Computer science ; Graph ; Theoretical computer science ; Message passing ; Topology (electrical circuits) ; Mathematics ; Pure mathematics ; Geometry ; Combinatorics ; Distributed computing Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Muhamad Amir Mohd Fadzil | Haslinda Zabiri | Adi Aizat Razali | Jamali Basar | Mohammad Syamzari Rafeen
Source: Energies (article, 2021-10-12)
Field: Chemistry — Analytical Chemistry

Base Oil Process Modelling Using Machine Learning The quality of feedstock used in base oil processing depends on the source of the crude oil. Moreover, the refinery is fed with various blends of crude oil to meet the demand of the refining products. These circumstances have caused changes of quality of the feedstock for the base oil production. Often the feedstock properties deviate from the original properties measured during the process design phase. To recalculate and remodel using first principal approaches requires significant costs due to the detailed material characterizations and several pilot-plant runs requirements. To perform all material characterization and pilot plant runs every time the refinery receives a different blend of crude oil will simply multiply the costs. Due to economic reasons, only selected lab characterizations are performed, and the base oil processing plant is operated reactively based on the feedback of the lab analysis of the base oil product. However, this reactive method leads to loss in production for several hours because of the residence time as well as time required to perform the lab analysis. Hence in this paper, an alternative method is studied to minimize the production loss by reacting proactively utilizing machine learning algorithms. Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost) models are developed and studied using historical data of the plant to predict the base oil product kinematic viscosity and viscosity index based on the feedstock qualities and the process operating conditions. The XGBoost model shows the most optimal and consistent performance during validation and a 6.5 months plant testing period. Subsequent deployment at our plant facility and product recovery analysis have shown that the prediction model has facilitated in reducing the production recovery period during product transition by 40%. Raw material ; Refinery ; Process engineering ; Support vector machine ; Process (computing) ; Production (economics) ; Decision tree ; Computer science ; Engineering ; Machine learning ; Waste management ; Chemistry Petroleum Processing and Analysis ; Mineral Processing and Grinding ; Fault Detection and Control Systems
Authors: Shiyi Qin | Tianyi Jin | Reid C. Van Lehn | Ví­ctor M. Zavala
Source: ChemRxiv (preprint, 2021-04-08)
Field: Materials Science — Materials Chemistry

Predicting Critical Micelle Concentrations for Surfactants using Graph Convolutional Neural Networks Surfactants are amphiphilic molecules that are widely used in consumer products, industrial processes, and biological applications. A critical property of a surfactant is the critical micelle concentration (CMC), which is the concentration at which surfactant molecules undergo cooperative self-assembly in solution. Notably, the primary method to obtain CMCs experimentally—tensiometry—is laborious and expensive. In this work, we show that graph convolutional neural networks (GCNs) can predict CMCs directly from the surfactant molecular structure. Specifically, we developed a GCN architecture that encodes the surfactant structure in the form of a molecular graph and trained it using experimental CMC data. We found that the GCN can predict CMCs with higher accuracy than previously proposed methods and that it can generalize to anionic, cationic, zwitterionic, and nonionic surfactants. Molecular saliency maps revealed how atom types and surfactant molecular substructures contribute to CMCs and found this to be in agreement with physical rules that correlate constitutional and topological information to CMCs. Following such rules, we proposed a small set of new surfactants for which experimental CMCs are not available; for these molecules, CMCs predicted with our GCN exhibited similar trends to those obtained from molecular simulations. These results provide evidence that GCNs can enable high-throughput screening of surfactants with desired self-assembly characteristics. Pulmonary surfactant ; Critical micelle concentration ; Cationic polymerization ; Micelle ; Molecule ; Graph ; Chemistry ; Amphiphile ; Molecular dynamics ; Computer science ; Biological system ; Materials science ; Combinatorial chemistry ; Computational chemistry ; Organic chemistry ; Polymer ; Theoretical computer science ; Copolymer ; Aqueous solution Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization