jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yilin Yang | Siddarth K. Achar | John R. Kitchin
Source:  (preprint, 2021-08-09)
Field: Materials Science — Materials Chemistry

Evaluation of the Degree of Rate Control via Automatic Differentiation The degree of rate control quantitatively identifies the kinetically relevant (sometimes known as rate-limiting) steps of a complex reaction network. This concept relies on derivatives which are commonly implemented numerically, e.g. with finite differences. Numerical derivatives are tedious to implement, and can be problematic, and unstable or unreliable. In this work, we demonstrate the use of automatic differentiation in the evaluation of the degree of rate control. Automatic differentiation libraries are increasingly available through modern machine learning frameworks. Compared to the finite differences, automatic differentiation provides solutions with higher accuracy with lower computational cost. Furthermore, we illustrate a hybrid local-global sensitivity analysis method, the distributed evaluation of local sensitivity analysis (DELSA), to assess the importance of kinetic parameters over an uncertain space. This method also benefits from automatic differentiation to obtain high-quality results efficiently. Automatic differentiation ; Degree (music) ; Sensitivity (control systems) ; Limiting ; Computer science ; Space (punctuation) ; Control (management) ; Quality (philosophy) ; Work (physics) ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Mathematics ; Engineering Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; Thermal and Kinetic Analysis
Authors: Mohan Surya Raja Elapolu | Md. Imrul Reza Shishir | Alireza Tabarraei
Source:  (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

Applied Machine Learning Method to Predict Crack Propagation Path in Polycrystalline Graphene Sheet Abstract A machine learning model is developed to predict the crack propagation path in polycrystalline graphene sheets. The dataset used for training the machine learning (ML) model is obtained from the molecular dynamics (MD) simulations. A training set of 700 samples has been used to train the ML model. Each training sample consists of an input image which contains the information of the initial configuration of precracked polycrystalline graphene sheet and an output image which contains the information of crack path. After training, the ML model can predict the crack propagation path in polycrystalline graphene sheet instantaneously, thus avoiding the computational costs involved with MD simulations. Graphene ; Crystallite ; Path (computing) ; Materials science ; Computer science ; Set (abstract data type) ; Artificial intelligence ; Composite material ; Algorithm ; Nanotechnology ; Metallurgy Machine Learning in Materials Science ; Graphene research and applications ; Fuel Cells and Related Materials
Authors: Peijun Xu | Xiaohong Mou | Qiuhan Guo | Ting Fu | Hong Ren | Guiyan Wang | Yan Li | Guohui Li
Source: Chinese Journal of Chemical Physics (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Coarse-grained molecular dynamics study based on TorchMD The coarse grained (CG) model implements the molecular dynamics simulation by simplifying atom properties and interaction between them. Despite losing certain detailed information, the CG model is still the first-thought option to study the large molecule in long time scale with less computing resource. The deep learning model mainly mimics the human studying process to handle the network input as the image to achieve a good classification and regression result. In this work, the TorchMD, a MD framework combining the CG model and deep learning model, is applied to study the protein folding process. In 3D collective variable (CV) space, the modified find density peaks algorithm is applied to cluster the conformations from the TorchMD CG simulation. The center conformation in different states is searched. And the boundary conformations between clusters are assigned. The string algorithm is applied to study the path between two states, which are compared with the end conformations from all atoms simulations. The result shows that the main phenomenon of protein folding with TorchMD CG model is the same as the all-atom simulations, but with a less simulating time scale. The workflow in this work provides another option to study the protein folding and other relative processes with the deep learning CG model. Molecular dynamics ; Computer science ; Folding (DSP implementation) ; Process (computing) ; Scale (ratio) ; Workflow ; Atom (system on chip) ; Statistical physics ; Path (computing) ; Algorithm ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Р. Р. Исмагилов | А. Б. Логинов | Sergei Malykhin | Victor I. Kleshch | A. N. Obraztsov
Source: Instruments and Experimental Techniques (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Analysis of Low-Temperature Plasma by Optical Emission Spectroscopy with Spatial Scanning An automated device is described that makes it possible to obtain maps of the spatial distribution of the composition and intensity of the components of the optical emission spectra of an activated gaseous medium, as well as to conduct thermometry using machine-learning methods based on a quick comparison of calculated and experimentally recorded radiation spectra. The interpreted Python programming language with a set of specialized libraries was used both to automate the registration of spectra and to analyze them using machine-learning methods. The apparatus was tested based on the example of a methane-hydrogen gas medium activated by a direct current discharge in an apparatus for the plasma–chemical deposition of carbon nanomaterials. The software of the automated apparatus allows one to supplement the measurement procedure by including additional software modules without changing the program code of the main control program. Python (programming language) ; Software ; Computer science ; Plasma ; Materials science ; Spectral line ; Spectroscopy ; Emission spectrum ; Analytical Chemistry (journal) ; Physics ; Chemistry ; Programming language Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites ; Laser-induced spectroscopy and plasma
Authors: Maud Parrot | Hamza Tajmouati | Vinícius Barros Ribeiro da Silva | Brian Ross Atwood | Robin Fourcade | Yann Gasthon-Mathé | Nicolas Do Huu | Quentin Perron
Source: ChemRxiv (preprint, 2021-12-20)
Field: Materials Science — Materials Chemistry

Integrating Synthetic Accessibility with AI-based Generative Drug Design Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule synthesizability, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic feasibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). After a comparison of RScore with other synthetic scores from the literature, we describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables to obtain more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on https://github.com/iktos/generation-under-synthetic- constraint. Retrosynthetic analysis ; Computer science ; Python (programming language) ; Program synthesis ; Source code ; Generative grammar ; Code generation ; Pipeline (software) ; Programming language ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Chukwunonso Chukwuzuloke Okoye | Okechukwu D. Onukwuli | M. I. Ejimofor | Chinenye Faith Okey‐Onyesolu
Source: International Journal of Advanced engineering Management and Science (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Modelling and Optimal Viscometry Formulation Evaluation of a Modified Green Based Self-Healing Automotive Paint This research successfully converted periwinkle shells, an agrowaste of regenerative resource, to an additive for the formulation of a self-healing car paint. The periwinkle shell was deproteinized, demineralised and deacetylyzed to produce chitosan. The parent chitosan sample in a series of reactions was modified by graft copolymerization with maleic anhydride and methyl methacrylate to yield a chitosan derivate (chitosan-g-maleic anhydride-g-(methyl methacrylate) copolymer (modified chitosan). A 2 4 full factorial central composite rotatable experimental design and a three-layered (4:n:1) feed-forward architecture of artificial neural network trained by the Levenberg-Marquardt backpropagation algorithm was employed to model and predict the process variables. The physicochemical properties of the paint formulations were studied using standard methods. Fourier Transform Infrared was used to identify the functional groups of the samples under investigation. Grafting efficiency (GE %) of 75.3% and grafting yield (G %) of 76.25% indicates effective graft copolymerization of methyl methacrylate on to the maleilated chitosan. The functional groups revealed by the FTIR spectra confirmed the graft copolymerization of maleilated chitosan with methyl methacrylate to yield a chitosan derivate (chitosan-gmaleic anhydride-g-(methyl methacrylate) copolymer and also identified the presence of the modified chitosan in the self healing paint. The drying time, adhesion and chemical resistance properties measured were within acceptable limits. ANOVA results show that quadratic equation best describes the viscometry behaviour of the paint formulation. The binder and solvent showed significant effect on the response. The AAD (%) and coefficient of determination values obtained for RSM and ANN show that the two techniques predicted to a great extent the viscometry behaviour of the system, however, ANN model marginally outperformed the RSM model. An optimal response of 99.85mPa.s was recorded at 70.24g binder, 79.99g solvent, 15.72g pigment and 3g additives. Viscometer ; Automotive industry ; Self-healing ; Materials science ; Mathematics ; Viscosity ; Engineering ; Composite material ; Medicine Polymer composites and self-healing
Authors: Anup Das | Andrew Friskop | Paulo Flores | C. Igathinathane | Jithin Mathew | Zhao Zhang
Source: 2021 ASABE Annual International Virtual Meeting, July 12-16, 2021 (article, 2021-07-12)
Field: Chemistry — Analytical Chemistry

Using aerial imagery coupled with machine learning to assess Goss's Wilt disease severity in field corn <b><sc>Abstract.</sc></b> Goss's Wilt has been identified as one of the most yield-limiting diseases in corn production in North Dakota (ND), with reported yield losses of up to 50%. Chemical applications (e.g., hydroxides and citric acid) at the proper corn growth stage can minimize or even avoid yield losses. A key aspect for chemical applications is to assess the level of infestation and the severity of the disease across a field. The conventional assessment is completed through field visits and visual observations, which can be time consuming (inefficient), subjective, and can lead to inaccuracy due to evaluator‘s fatigue. Current technological advancement of unmanned aerial systems (UASs) incorporated with machine learning (ML) techniques provides a new approach for disease severity assessment. However, the correlation between ML and human-based assessment has not been well investigated. In this study, we collected UAS imagery presenting different severity levels from the same corn fields. Shortly after the UAS flights, plant pathologists surveyed the field plots and visually rated the Goss‘s Wilt severity of each plot. A dataset containing 200 images was prepared from the UAS imagery and then fed into different ML algorithms. To be specific, different textural, color, and area features were extracted and fed into different ML algorithms, followed by the performance of different ML algorithms compared. Random forest (RF) classifier achieved highest precision (72.30%), recall (72.50%) and F-score (72.39%) than other classifiers including Naive bayes, Support vector machine, Feedforward artificial neural network, Logistic regression, Stochastic gradient descent, and Ada boost. Hue value and infected leaves area are important factors for identifying Goss Wilt severity. Therefore, the developed automatic Goss‘s Wilt disease severity detection based on UAS imagery coupled with RF classifier can be a potential tool to aid farmers in monitoring Goss‘s Wilt disease. Artificial intelligence ; Random forest ; Machine learning ; Naive Bayes classifier ; Support vector machine ; Logistic regression ; Computer science ; Limiting ; Aerial image ; Deep learning ; Classifier (UML) ; Engineering ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Chandler A. Becker | James A. Warren
Source: IEEE Signal Processing Magazine (review, 2021-12-28)
Field: Materials Science — Materials Chemistry

Progress in Materials Data Availability and Application: A review Materials researchers are generating data in ever-increasing volumes, and these data are, in turn, fueling the discovery and design of new materials. These changes are enabled by advances in data capture via laboratory information management systems (LIMS), high-throughput experiment and computation, improved data collection capabilities, automated experiment selection and execution, and new analytical methods based on artificial intelligence (AI) and machine learning (ML) approaches. Data infrastructure is being developed with increasing numbers of repositories as well as data-sharing and standardization efforts. Computer science ; Standardization ; Throughput ; Data collection ; Data management ; Data sharing ; Selection (genetic algorithm) ; Data science ; Group method of data handling ; Data mining ; Machine learning ; Operating system Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Scientific Computing and Data Management
Authors: Stephen Lam | Qing‐Jie Li | R. G. Ballinger | Charles Forsberg | Ju Li
Source: ChemRxiv (preprint, 2021-01-13)
Field: Materials Science — Materials Chemistry

Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potentials Lithium-based molten salts have attracted significant attention due to their applications in energy storage, advanced fission reactors and fusion devices. Lithium fluorides and particularly 66.6%LiF-33.3¾F2 (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of desirable heat-transfer and neutron-absorption characteristics. For nuclear salts, the range of possible local structures, compositions, and thermodynamic conditions presents significant challenges in atomistic modeling. In this work, we demonstrate that atom-centered neural network interatomic potentials (NNIP) provide a fast and accurate method for performing molecular dynamics of molten salts. For LiF, these potentials are able to accurately model dimer interactions, crystalline solids under deformation, semi-crystalline LiF near the melting point and liquid LiF at high temperatures. For Flibe, NNIPs accurately predicts the structures and dynamics at normal operating conditions, high temperature-pressure conditions, and in the crystalline solid phase. Furthermore, we show that NNIP-based molecular dynamics of molten salts are scalable to reach long timescales (e.g., nanosecond) and large system sizes (e.g., 105 atoms), while maintaining ab initio accuracy and providing more than three orders of magnitude of computational speedup for calculating structure and transport properties. Molecular dynamics ; Lithium (medication) ; Melting point ; Materials science ; Chemical physics ; Atom (system on chip) ; Interatomic potential ; Molten salt ; Work (physics) ; Thermodynamics ; Chemistry ; Computational chemistry Graphite, nuclear technology, radiation studies ; Machine Learning in Materials Science ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Md. Imrul Reza Shishir | Mohan Surya Raja Elapolu | Alireza Tabarraei
Source:  (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

A Deep Convolutional Neural Network-Based Method to Predict Accurate Fracture Strength of Poly-Crystalline Graphene Abstract A data-driven deep convolution neural network model is used to predict the fracture properties of polycrystalline graphene from the atomic resolution image. A large dataset is prepared using molecular dynamic simulations and atomic resolution image of polycrystalline graphene. The results show that our model after training can predict the fracture properties of polycrystalline graphene with high accuracy. Graphene ; Convolutional neural network ; Crystallite ; Convolution (computer science) ; Materials science ; Fracture (geology) ; Image (mathematics) ; Artificial neural network ; Computer science ; Resolution (logic) ; Artificial intelligence ; Context (archaeology) ; Composite material ; Nanotechnology ; Geology ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Amitava Choudhury
Source: Research Square (preprint, 2021-04-19)
Field: Materials Science — Materials Chemistry

Processing Technique Selection for Steels Based on Mechanical Properties Using Machine Learning Framework Abstract Prediction of process route from materials with a desired set of property is one of the fundamental issues in the perspective of the materials design aspect. The parameter space is often too large to be bound since there are too many possibilities. However, in the areas with limited theoretical access, artificial learning techniques can be attempted by using the available data of the candidate materials. In this study, a computational method has been proposed to predict the different process routes of steel constituted taking composition and desired mechanical properties as inputs. First of all, historical data of the actual rolling process was collected, cleaned, and integrated. Further, the dataset is divided into four different classes based on the rolling process data. Then, to find out the essential characteristics of variables, feature correlation among various features has been calculated. A state-of-art machine learning prediction methods such as logistic regression, K- nearest neighbor, Support vector machine, and the random forest are studied to implement the prediction model. In order to avoid the overfitting of the model, k fold cross-validation is applied to the model, and achieve a realistic prediction result with an accuracy of 97%. The F1-score of the classification model is 0.86, and the kappa score is 0.95, which comply that the model has excellent learning and speculation ability and the precise forecast of steel process routes based on the given input parameters. Selection (genetic algorithm) ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Advanced Materials Characterization Techniques
Authors: Tim Hsu | Nathan Keilbart | Stephen E. Weitzner | James Chapman | Penghao Xiao | Tuan Anh Pham | Siyao Qiu | Xiao Chen | Brandon C. Wood
Source: arXiv (Cornell University) (preprint, 2021-09-23)
Field: Materials Science — Materials Chemistry

Efficient, Interpretable Atomistic Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy Prediction. Graph neural networks (GNNs) are attractive for learning properties of atomic structures thanks to their intuitive, physically informed graph encoding of atoms and bonds. However, conventional GNN encodings do not account for angular information, which is critical for describing complex atomic arrangements in disordered materials, interfaces, and molecular distortions. In this work, we extend the recently proposed ALIGNN encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d), and we apply the model to capture the structures of aqua copper complexes for spectroscopy prediction. This simple extension is shown to lead to a memory-efficient graph representation capable of capturing the full geometric information of atomic structures. Specifically, the ALIGNN-d encoding is a sparse yet equally expressive representation compared to the dense, maximally-connected graph, in which all bonds are encoded. We also explore model interpretability based on ALIGNN-d by elucidating the relative contributions of individual structural components to the optical response of the copper complexes. Lastly, we briefly discuss future developments to validate the computational efficiency and to extend the interpretability of ALIGNN-d. Interpretability ; Dihedral angle ; Graph ; Representation (politics) ; Computer science ; Encoding (memory) ; Molecular graph ; Artificial neural network ; Graph drawing ; Theoretical computer science ; Algorithm ; Topology (electrical circuits) ; Artificial intelligence ; Mathematics ; Physics ; Combinatorics ; Molecule ; Hydrogen bond ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Kevin Ryczko | Sebastian J. Wetzel | Roger G. Melko | Isaac Tamblyn
Source: arXiv (Cornell University) (preprint, 2021-04-12)
Field: Materials Science — Materials Chemistry

Orbital-Free Density Functional Theory with Small Datasets and Deep Learning We use voxel deep neural networks to predict the kinetic energy densities and functional derivatives of kinetic energies for the Thomas-Fermi model and Kohn-Sham density functional theory calculations. We show that an electron density can be found self-consistently for a graphene lattice without any projection scheme using a voxel deep neural network trained with the Thomas-Fermi model. Additionally, we show that we can predict the kinetic energy of a graphene lattice within chemical accuracy after training from only 2 density functional theory calculations. This accuracy is maintained to arbitrary system size with linear scaling. Furthermore, we demonstrate an alternative, functional derivative-free, Monte Carlo based orbital free density functional theory algorithm to calculate an accurate 2-electron density in a double inverted Gaussian potential with a machine-learned kinetic energy functional. Orbital-free density functional theory ; Density functional theory ; Kinetic energy ; Statistical physics ; Gaussian ; Functional derivative ; Lattice (music) ; Linear scale ; Physics ; Thomas–Fermi model ; Time-dependent density functional theory ; Energy functional ; Scaling ; Quantum mechanics ; Electron ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions