jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yi-Fan Hou | Fuchun Ge | Pavlo O. Dral
Source: ChemRxiv (preprint, 2022-10-19)
Field: Materials Science — Materials Chemistry

Explicit learning of derivatives with the KREG and pKREG models on the example of accurate representation of molecular potential energy surfaces The KREG and pKREG models were proven to enable accurate learning of multidimensional single-molecule surfaces of quantum chemical properties such as ground-state potential energies, excitation energies, and oscillator strengths. These models are based on kernel ridge regression (KRR) with the Gaussian kernel function and employ relative-to-equilibrium (RE) global molecular descriptor, while pKREG is designed to enforce invariance under atom permutations with a permutationally invariant kernel. Here we extend these two models to also explicitly include the derivative information from the training data into the model which greatly improves their accuracy. We demonstrate on the example of learning potential energies and energy gradients that KREG and pKREG models are better or on par with state-of-the-art machine learning models. We also found that in challenging cases both energy and energy gradient labels should be learned to properly model potential energy surfaces and learning only energies or gradients is insufficient. The models’ open-source implementation is freely available in the MLatom package for general-purpose atomistic machine learning simulations which can be also performed on the MLatom@XACS cloud computing service. Computer science ; Invariant (physics) ; Potential energy ; Statistical physics ; Kernel (algebra) ; Artificial intelligence ; Gaussian ; Representation (politics) ; Ground state ; Theoretical computer science ; Physics ; Quantum mechanics ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Abdelghani Lakhdar | Aziz Moumen | Zineb Laabid | Khalifa Mansouri
Source: E3S Web of Conferences (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Prediction of The Behavior of Bio-Loaded Flexible PVC by Chicken Feathers with Artificial Neural Networks The infinite needs of humanity in several fields give birth to several innovations, in materials sciences, these needs are summed up in the creation of new composite materials. Obtaining a composite material from recycled material and a bio-load first of all makes it possible to recover the recycled material and to have new composite material. PVC is one of the most used plastics in recent years, the addition, after recycling, of a bio-load in the form of chicken feathers which are just lost in nature or incinerated, allows the birth of new composite material. In this article, we use neural networks which appear to be among the essential methods to solve and model complex systems, and in particular when it comes to non-linear problems. This method will be used in comparison with the finite element method to find the most adequate method which makes it possible to better model the behaviors of PVC bio-loaded by chicken feathers, and then predict the behaviors with different percentages of the bio-load. We took the results of the experiments carried out, with those of the study of finite elements, and with the results obtained by the neural networks which will be presented in this article, to find the model which makes it possible to better present and predict the behavior of the recycled PVC bio-loaded with an adequate percentage which improves the mechanical characteristics of this type of composite material. The error between the experimental results and those obtained by the neural networks is very small compared to the differences between the two experimental results and those obtained by the finite elements. Which proves that the neural network model is an important computer tool for modeling and predicting the results of mechanical experiments Artificial neural network ; Composite number ; Feather ; Finite element method ; Computer science ; Materials science ; Composite material ; Biochemical engineering ; Mechanical engineering ; Process engineering ; Structural engineering ; Engineering ; Artificial intelligence ; Ecology ; Biology Machine Learning in Materials Science
Authors: Bryce Hedelius | Fabian B. Fuchs | Dennis Della Corte
Source: arXiv (Cornell University) (preprint, 2022-01-03)
Field: Materials Science — Materials Chemistry

Learning Small Molecule Energies and Interatomic Forces with an Equivariant Transformer on the ANI-1x Dataset Accurate predictions of interatomic energies and forces are essential for high quality molecular dynamic simulations (MD). Machine learning algorithms can be used to overcome limitations of classical MD by predicting ab initio quality energies and forces. SE(3)-equivariant neural network allow reasoning over spatial relationships and exploiting the rotational and translational symmetries. One such algorithm is the SE(3)-Transformer, which we adapt for the ANI-1x dataset. Our early experimental results indicate through ablation studies that deeper networks - with additional SE(3)-Transformer layers - could reach necessary accuracies to allow effective integration with MD. However, faster implementations of the SE(3)-Transformer will be required, such as the recently published accelerated version by Milesi. Transformer ; Ab initio ; Computer science ; Homogeneous space ; Artificial neural network ; Equivariant map ; Artificial intelligence ; Algorithm ; Physics ; Statistical physics ; Voltage ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: James Chapman | Kyoung E. Kweon | Yakun Zhu | Kyle Bushick | L. B. Bayu Aji | Christopher A. Colla | Nir Goldman | Nathan Keilbart | Roger Qui | Tae Wook Heo | Brandon C. Wood
Source: arXiv (Cornell University) (preprint, 2022-08-23)
Field: Materials Science — Materials Chemistry

Hydrogen in Disordered Titania: Connecting Local Chemistry, Structure, and Stoichiometry through Accelerated Exploration Hydrogen incorporation in native surface oxides of metal alloys often controls the onset of metal hydriding, with implications for materials corrosion and hydrogen storage. A key representative example is titania, which forms as a passivating layer on a variety of titanium alloys for structural and functional applications. These oxides tend to be structurally diverse, featuring polymorphic phases, grain boundaries, and amorphous regions that generate a disparate set of unique local environments for hydrogen. Here, we introduce a workflow that can efficiently and accurately navigate this complexity. First, a machine learning force field, trained on ab initio molecular dynamics simulations, was used to generate amorphous configurations. Density functional theory calculations were then performed on these structures to identify local oxygen environments, which were compared against experimental observations. Second, to classify subtle differences across the disordered configuration space, we employ a graph-based sampling procedure. Finally, local hydrogen binding energies are computed using exhaustive density functional theory calculations on representative configurations. We leverage this methodology to show that hydrogen binding energetics are described by local oxygen coordination, which in turn is affected by stoichiometry. Together these results imply that hydrogen incorporation and transport in TiO$_x$ can be tailored through compositional engineering, with implications for improving performance and durability of titanium-derived alloys in hydrogen environments. Hydrogen ; Density functional theory ; Amorphous solid ; Chemical physics ; Materials science ; Stoichiometry ; Molecular dynamics ; Ab initio ; Hydrogen storage ; Titanium ; Leverage (statistics) ; Chemistry ; Computational chemistry ; Nanotechnology ; Crystallography ; Computer science ; Physical chemistry ; Metallurgy ; Machine learning Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Hydrogen Storage and Materials
Authors: Aleksandr Cherniaev | Riley Carriere
Source: WIT transactions on the built environment (article, 2022-08-22)
Field: Materials Science — Materials Chemistry

NEW PREDICTIVE MODELS FOR THE BALLISTIC LIMIT OF SPACECRAFT SANDWICH PANELS SUBJECTED TO HYPERVELOCITY IMPACT Cell size, foil thickness, and the material of the core, influence the ballistic performance of honeycombcore sandwich panels (HCSP) in the case of hypervelocity impact (HVI) by orbital debris. Two predictive models that account for this influence have been developed in this study: a dedicated ballistic limit equation (BLE) and an artificial neural network (ANN) trained to predict the outcomes of HVI on HCSP. The BLE is a modified version of the Whipple shield BLE and demonstrated excellent accuracy in predicting the ballistic limits of HCSP, when tested against a new set of simulation data, with the discrepancy ranging from 1.13% to 5.58% only. The ANN was developed using MATLAB's Deep Learning Toolbox framework and was trained utilizing the same HCSP HVI database as that employed for the BLE fitting and demonstrated a very good predictive accuracy, when tested against a set of simulation data not previously used in the training of the network, with the discrepancy ranging from 0.67% to 7.27%. Hypervelocity ; Ballistic limit ; Ranging ; Space debris ; Spacecraft ; Limit (mathematics) ; Aerospace engineering ; Computer science ; MATLAB ; Artificial neural network ; Set (abstract data type) ; Physics ; Projectile ; Artificial intelligence ; Engineering ; Mathematics High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Transportation Safety and Impact Analysis
Authors: Jonathan Balasingham | Viktor Zamaraev | Vitaliy Kurlin
Source: arXiv (Cornell University) (preprint, 2022-12-19)
Field: Materials Science — Materials Chemistry

Material Property Prediction using Graphs based on Generically Complete Isometry Invariants The structure-property hypothesis says that the properties of all materials are determined by an underlying crystal structure. The main obstacle was the ambiguity of conventional crystal representations based on incomplete or discontinuous descriptors that allow false negatives or false positives. This ambiguity was resolved by the ultra-fast Pointwise Distance Distribution (PDD), which distinguished all periodic structures in the world's largest collection of real materials (Cambridge Structural Database). The state-of-the-art results in property predictions were previously achieved by graph neural networks based on various graph representations of periodic crystals, including the Crystal Graph with vertices at all atoms in a crystal unit cell. This work adapts the Pointwise Distance Distribution for a simpler graph whose vertex set is not larger than the asymmetric unit of a crystal structure. The new Distribution Graph reduces mean-absolute-error by 0.6\%-12\% while having 44\%-88\% of the number of vertices when compared to the crystal graph when applied on the Materials Project and Jarvis-DFT datasets using CGCNN and ALIGNN. Methods for hyper-parameters selection for the graph are backed by the theoretical results of the Pointwise Distance Distribution and are then experimentally justified. Graph ; Invariant (physics) ; Crystal (programming language) ; Homogeneous space ; Representation (politics) ; Translation (biology) ; Dual graph ; Computer science ; Mathematics ; Discrete mathematics ; Combinatorics ; Algorithm ; Planar graph ; Geometry ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Xuhao Wan | Wei Yü | Huan Niu | Xiting Wang | Zhaofu Zhang | Yuzheng Guo
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Revealing the Oxygen Reduction/Evolution Reaction Activity Origin of Carbon-Nitride-Related Single-Atom Catalysts: Quantum Chemistry in Artificial Intelligence Catalysis ; Nitride ; Quantum chemistry ; Carbon nitride ; Carbon fibers ; Chemistry ; Oxygen reduction reaction ; Atom (system on chip) ; Reduction (mathematics) ; Chemical engineering ; Materials science ; Photochemistry ; Nanotechnology ; Reaction mechanism ; Physical chemistry ; Computer science ; Organic chemistry ; Engineering ; Mathematics ; Parallel computing ; Photocatalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Mads-Peter Verner Christiansen | Nikolaj Rønne | Bjørk Hammer
Source: The Journal of Chemical Physics (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

Atomistic global optimization X: A Python package for optimization of atomistic structures Modeling and understanding properties of materials from first principles require knowledge of the underlying atomistic structure. This entails knowing the individual chemical identity and position of all atoms involved. Obtaining such information for macro-molecules, nano-particles, and clusters and for the surface, interface, and bulk phases of amorphous and solid materials represents a difficult high-dimensional global optimization problem. The rise of machine learning techniques in materials science has, however, led to many compelling developments that may speed up structure searches. The complexity of such new methods has prompted a need for an efficient way of assembling them into global optimization algorithms that can be experimented with. In this paper, we introduce the Atomistic Global Optimization X (AGOX) framework and code as a customizable approach that enables efficient building and testing of global optimization algorithms. A modular way of expressing global optimization algorithms is described, and modern programming practices are used to enable that modularity in the freely available AGOX Python package. A number of examples of global optimization approaches are implemented and analyzed. This ranges from random search and basin-hopping to machine learning aided approaches with on-the-fly learnt surrogate energy landscapes. The methods are applied to problems ranging from supported clusters over surface reconstructions to large carbon clusters and metal-nitride clusters incorporated into graphene sheets. Python (programming language) ; Global optimization ; Computer science ; Optimization problem ; Computational science ; Theoretical computer science ; Mathematical optimization ; Algorithm ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Malcolm Dearg | Henry Hoddinott | Yubiao Niu | Richard E. Palmer | Thomas J. A. Slater
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Classification of Metal Nanoclusters Using Convolutional Neural Networks Journal Article Classification of Metal Nanoclusters Using Convolutional Neural Networks Get access Malcolm Dearg, Malcolm Dearg Cardiff Catalysis Institute, School of Chemistry, Cardiff University, Cardiff, UKePSIC, Diamond Light Source, Oxfordshire, UK Corresponding author: DeargM@cardiff.ac.uk, SlaterT2@cardiff.ac.uk Search for other works by this author on: Oxford Academic Google Scholar Henry P Hoddinott, Henry P Hoddinott Nanomaterials Lab, Swansea University, Bay Campus, Swansea, UK Search for other works by this author on: Oxford Academic Google Scholar Yubiao Niu, Yubiao Niu Nanomaterials Lab, Swansea University, Bay Campus, Swansea, UK Search for other works by this author on: Oxford Academic Google Scholar Richard E Palmer, Richard E Palmer Nanomaterials Lab, Swansea University, Bay Campus, Swansea, UK Search for other works by this author on: Oxford Academic Google Scholar Thomas JA Slater Thomas JA Slater Cardiff Catalysis Institute, School of Chemistry, Cardiff University, Cardiff, UKePSIC, Diamond Light Source, Oxfordshire, UKNanomaterials Lab, Swansea University, Bay Campus, Swansea, UK Corresponding author: DeargM@cardiff.ac.uk, SlaterT2@cardiff.ac.uk Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 3000–3001, https://doi.org/10.1017/S1431927622011230 Published: 01 August 2022 Bay ; Library science ; Engineering ; History ; Computer science ; Archaeology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Computational Drug Discovery Methods
Authors: Houssam Metni | Ichiro Takeuchi | Valentin Stanev
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Predicting the Superconducting Critical Temperature in Transition Metal Carbides and Nitrides Using Machine Learning Carbide ; Superconductivity ; Nitride ; Materials science ; Condensed matter physics ; Metallurgy ; Nanotechnology ; Physics Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Advanced Materials Characterization Techniques
Authors: Minwoo Lee | Young-Sin Choi | Do-Hun Kwon | Eun‐Ji Cha | Hee-Bok Kang | Jae-In Jeong | Seok-Jae Lee | Hwi-Jun Kim
Source: Archives of Metallurgy and Materials (article, 2022-11-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Prediction of Optimized Metalloid Content in Fe-Si-B-P Amorphous Alloys Using Artificial Intelligence Algorithm Artificial intelligence operated with machine learning was performed to optimize the amount of metalloid elements (Si, B, and P) subjected to be added to a Fe-based amorphous alloy for enhancement of soft magnetic properties. The effect of metalloid elements on magnetic properties was investigated through correlation analysis. Si and P were investigated as elements that affect saturation magnetization while B was investigated as an element that affect coercivity. The coefficient of determination R2 (coefficient of determination) obtained from regression analysis by learning with the Random Forest Algorithm (RFR) was 0.95 In particular, the R2 value measured after including phase information of the Fe-Si-B-P ribbon increased to 0.98. The optimal range of metalloid addition was predicted through correlation analysis method and machine learning. Metalloid ; Materials science ; Coercivity ; Amorphous metal ; Amorphous solid ; Artificial neural network ; Machine learning ; Ribbon ; Alloy ; Saturation (graph theory) ; Algorithm ; Artificial intelligence ; Metallurgy ; Mathematics ; Computer science ; Condensed matter physics ; Physics ; Composite material ; Metal ; Crystallography ; Chemistry Magnetic Properties and Applications ; Metallic Glasses and Amorphous Alloys ; Microstructure and Mechanical Properties of Steels
Authors: Zhiqin Chao | Manyu Xiao | Xingang Luan
Source: 2022 2nd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) (article, 2022-02-01)
Field: Materials Science — Ceramics and Composites

Investigation of Machine Learning Methods for Oxidation and Tensile Creep Properties of SiC/SiC Composites With the brilliant properties of the low density, high strength and fracture toughness at high temperatures, SiC fiber-reinforced SiC matrix composites are currently being applied for aircraft engine hot-section components. However, both the oxidation and creep resistance of SiC/SiC composites still needed to be improved under different stress, temperature and atmosphere. In order to alleviate the high cost of experimental data collection and the limited data resources, a novel numerical strategy based on machine learning method will be investigated with combination of both the current law of the physical model and the available experimental data. The data-driven analysis results show that in the case of a small amount of training data, neural network has better fitting effect than traditional data fitting methods for univariate fitting; In addition, it is better benefit for reflecting the influence law of multiple variables on the change of material residual strength and quality. Materials science ; Composite material ; Ultimate tensile strength ; Creep ; Artificial neural network ; Residual ; Residual stress ; Toughness ; Fiber ; Matrix (chemical analysis) ; Stress (linguistics) ; Computer science ; Machine learning ; Algorithm Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Advanced machining processes and optimization
Authors: Anh Tran | Kathryn Maupin | Theron Rodgers
Source:  (article, 2022-08-14)
Field: Materials Science — Materials Chemistry

Integrated Computational Materials Engineering With Monotonic Gaussian Processes Abstract Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting machine learning model requires significantly fewer data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on two different material datasets, where one experimental and one computational dataset is used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data is scarce and noisy or when the dimensionality is high, and monotonicity is where supported by strong physical reasoning. Monotonic function ; Extrapolation ; Machine learning ; Gaussian process ; Artificial intelligence ; Computer science ; Interpolation (computer graphics) ; Algorithm ; Gaussian ; Support vector machine ; Applied mathematics ; Mathematical optimization ; Mathematics ; Mathematical analysis ; Physics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Machine Learning and Algorithms