jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiahui Chen | Jing Zhang | Zhijun Wang | Xiao Han | Yuxiao Zhang
Source: Materials & Design (article, 2022-09-16)
Field: Materials Science — Materials Chemistry

Self-adaptable materials structure descriptor based on graph attention network for machine learning Machine learning has shown great potential in bridging materials structures and properties. However, efficient and general descriptors for materials structures are still missing. This work dedicates to establishing an efficient and self-adaptable materials structure descriptor based on the graph attention network (GAT). In the GAT descriptor generation, the concrete structure is represented by graphic atomic clusters (atoms as nodes, edges as bonds) consisting of one central atom and the surroundings. A multi-head-GAT is applied to gather the short- and long-distance topological information from the graphical atomic clusters to generate dimension-flexible descriptors regardless of atom number; the GAT descriptors are contracted and help avoiding overfitting during the training process theoretically. Further verification of the GAT descriptors is carried out by performing the phase classification, energy prediction, and potential energy surface (PES) prediction task for Al-Cu alloys with complex precipitates and defects. The GAT descriptors adapt themselves for cross tasks without manual intervention and produce accurate predictions that agree with the density functional theory (DFT) results. The GAT descriptors are also testified as differentiable, universal, and precise on structural characterization containing point-, line-, and planar- defects. Further applications will be found for the materials design field demanding accurate structure descriptions. Overfitting ; Bridging (networking) ; Computer science ; Maxima and minima ; Atom (system on chip) ; Artificial intelligence ; Materials science ; Machine learning ; Topology (electrical circuits) ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xiyuan Wang | Muhan Zhang
Source: arXiv (Cornell University) (preprint, 2022-08-01)
Field: Materials Science — Materials Chemistry

Graph Neural Network with Local Frame for Molecular Potential Energy Surface Modeling molecular potential energy surface is of pivotal importance in science. Graph Neural Networks have shown great success in this field. However, their message passing schemes need special designs to capture geometric information and fulfill symmetry requirement like rotation equivariance, leading to complicated architectures. To avoid these designs, we introduce a novel local frame method to molecule representation learning and analyze its expressivity. Projected onto a frame, equivariant features like 3D coordinates are converted to invariant features, so that we can capture geometric information with these projections and decouple the symmetry requirement from GNN design. Theoretically, we prove that given non-degenerate frames, even ordinary GNNs can encode molecules injectively and reach maximum expressivity with coordinate projection and frame-frame projection. In experiments, our model uses a simple ordinary GNN architecture yet achieves state-of-the-art accuracy. The simpler architecture also leads to higher scalability. Our model only takes about 30% inference time and 10% GPU memory compared to the most efficient baselines. Computer science ; Equivariant map ; ENCODE ; Invariant (physics) ; Projection (relational algebra) ; Topology (electrical circuits) ; Theoretical computer science ; Inference ; Frame (networking) ; Reference frame ; Scalability ; Degenerate energy levels ; Algorithm ; Representation (politics) ; Artificial intelligence ; Mathematics ; Pure mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Chia‐Hao Lee | Abid Khan | Yue Zhang | M. Abir Hossain | Arend M. van der Zande | Bryan K. Clark | Pinshane Y. Huang
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Automated Acquisition and Deep Learning of 2D Materials on the Million-Atom Scale Journal Article Automated Acquisition and Deep Learning of 2D Materials on the Million-Atom Scale Get access Chia-Hao Lee, Chia-Hao Lee Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Abid Khan, Abid Khan Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Yue Zhang, Yue Zhang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar M Abir Hossain, M Abir Hossain Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Arend van der Zande, Arend van der Zande Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Bryan Clark, Bryan Clark Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Pinshane Huang Pinshane Huang Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA Corresponding author: pyhuang@illinois.edu Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 3062–3063, https://doi.org/10.1017/S1431927622011424 Published: 01 August 2022 Library science ; Art history ; History ; Computer science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides
Authors: Kamal Choudhary | Brian DeCost | Lily Major | K. Butler | Jeyan Thiyagalingam | Francesca Tavazza
Source: arXiv (Cornell University) (preprint, 2022-09-12)
Field: Materials Science — Materials Chemistry

Unified Graph Neural Network Force-field for the Periodic Table Classical force fields (FF) based on machine learning (ML) methods show great potential for large scale simulations of materials. MLFFs have hitherto largely been designed and fitted for specific systems and are not usually transferable to chemistries beyond the specific training set. We develop a unified atomisitic line graph neural network-based FF (ALIGNN-FF) that can model both structurally and chemically diverse materials with any combination of 89 elements from the periodic table. To train the ALIGNN-FF model, we use the JARVIS-DFT dataset which contains around 75000 materials and 4 million energy-force entries, out of which 307113 are used in the training. We demonstrate the applicability of this method for fast optimization of atomic structures in the crystallography open database and by predicting accurate crystal structures using genetic algorithm for alloys. Force field (fiction) ; Computer science ; Table (database) ; Artificial neural network ; Graph ; Periodic table ; Set (abstract data type) ; Algorithm ; Theoretical computer science ; Artificial intelligence ; Data mining ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jiace Sun | Lixue Cheng | Thomas F. Miller
Source: CaltechAUTHORS (California Institute of Technology) (preprint, 2022-05-31)
Field: Materials Science — Materials Chemistry

Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree-Fock computations. A molecular-orbital-based (MOB) pairwise decomposition of the correlation part of the dipole moment is applied, and these pair dipole moments could be further regressed as a universal function of molecular orbitals (MOs). The dipole MOB features consist of the energy MOB features and their responses to electric fields. An interpretable and rotationally equivariant Gaussian process regression (GPR) with derivatives algorithm is introduced to learn the dipole moment more efficiently. The proposed problem setup, feature design, and ML algorithm are shown to provide highly-accurate models for both dipole moment and energies on water and fourteen small molecules. To demonstrate the ability of MOB-ML to function as generalized density-matrix functionals for molecular dipole moments and energies of organic molecules, we further apply the proposed MOB-ML approach to train and test the molecules from the QM9 dataset. The application of local scalable GPR with Gaussian mixture model unsupervised clustering (GMM/GPR) scales up MOB-ML to a large-data regime while retaining the prediction accuracy. In addition, compared with literature results, MOB-ML provides the best test MAEs of 4.21 mDebye and 0.045 kcal/mol for dipole moment and energy models, respectively, when training on 110000 QM9 molecules. The excellent transferability of the resulting QM9 models is also illustrated by the accurate predictions for four different series of peptides. Dipole ; Moment (physics) ; Artificial intelligence ; Molecular orbital ; Computational chemistry ; Statistical physics ; Kriging ; Gaussian ; Chemistry ; Physics ; Computer science ; Algorithm ; Machine learning ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Haipan Xiang | Li Tian | Yong Li | Hongwei Song
Source: The Journal of Physical Chemistry A (article, 2022-01-06)
Field: Materials Science — Materials Chemistry

Energy- and Local-Gradient-Based Neural Network Method for Accurately Describing Long-Range Interaction: Application to the H<sub>2</sub> + CO<sup>+</sup> Reaction The long-range interaction plays an important role in theoretically describing ion-molecule reaction. However, most energy-based neural network fitting methods usually introduce spurious long-range interactions. In this work, we propose an energy- and local-gradient-based neural network (ELGNN) method to fit potential energy surfaces (PESs). <i>K</i>-means clustering is employed to divide the whole configuration space into three regions: reactant asymptotic region, interaction region, and product asymptotic region. In the interaction region, only the energies of sampled points are computed, while in the asymptotic regions, the gradients of partially sampled configurations are calculated as well, and both the energies and energy gradients (if necessary) are used to fit long-range interactions. These regions are joined together by switching functions. The ELGNN method is first applied to fit the PES of the H<sub>2</sub> + CO<sup>+</sup> reaction, which has significant long-range interactions. It is found that the ELGNN method works better than the energy-based NN method in describing long-range interactions. The dynamics and kinetics of the reaction are then investigated on the new PES. Spurious relationship ; Range (aeronautics) ; Chemistry ; Work (physics) ; Energy (signal processing) ; Artificial neural network ; Statistical physics ; Cluster analysis ; Potential energy ; Biological system ; Physics ; Atomic physics ; Computer science ; Thermodynamics ; Quantum mechanics ; Mathematics ; Materials science ; Statistics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Hrishikesh Bale | Maadhav Kothari | Andy Holwell | Michael W. Phaneuf | Simon Gray | Johnathan Legget
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Metals and Alloys

Investigation of Stress Corrosion Cracking in CMSX-4 Turbine Blade Alloys Using Deep Learning Assisted X-ray Microscopy Journal Article Investigation of Stress Corrosion Cracking in CMSX-4 Turbine Blade Alloys Using Deep Learning Assisted X-ray Microscopy Get access Hrishikesh Bale, Hrishikesh Bale Carl Zeiss Research Microscopy Solutions, Dublin, California Search for other works by this author on: Oxford Academic Google Scholar Maadhav Kothari, Maadhav Kothari Carl Zeiss Research Microscopy Solutions, Dublin, California Search for other works by this author on: Oxford Academic Google Scholar Andy Holwell, Andy Holwell Carl Zeiss Research Microscopy Solutions, Dublin, California Search for other works by this author on: Oxford Academic Google Scholar Michael Phaneuf, Michael Phaneuf Fibics Incorporated, Ottawa, Ontario, Canada Search for other works by this author on: Oxford Academic Google Scholar Simon Gray, Simon Gray Cranfield University, Cranfield, UK Search for other works by this author on: Oxford Academic Google Scholar Johnathan Legget Johnathan Legget Rolls Royce, Derby, UK Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 2052–2053, https://doi.org/10.1017/S1431927622007942 Published: 01 August 2022 Art history ; Art ; Engineering ; Library science ; Materials science ; Computer science Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Electron Microscopy Techniques and Applications ; High Temperature Alloys and Creep
Authors: Hongyu Yu | Liu, Boyu | Yang Zhong | Hong, Liangliang | Junyi Ji | Xu, Changsong | Xin-Gao Gong | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2022-11-21)
Field: Materials Science — Materials Chemistry

General time-reversal equivariant neural network potential for magnetic materials This study introduces time-reversal E(3)-equivariant neural network and SpinGNN++ framework for constructing a comprehensive interatomic potential for magnetic systems, encompassing spin-orbit coupling and noncollinear magnetic moments. SpinGNN++ integrates multitask spin equivariant neural network with explicit spin-lattice terms, including Heisenberg, Dzyaloshinskii-Moriya, Kitaev, single-ion anisotropy, and biquadratic interactions, and employs time-reversal equivariant neural network to learn high-order spin-lattice interactions using time-reversal E(3)-equivariant convolutions. To validate SpinGNN++, a complex magnetic model dataset is introduced as a benchmark and employed to demonstrate its capabilities. SpinGNN++ provides accurate descriptions of the complex spin-lattice coupling in monolayer CrI$_3$ and CrTe$_2$, achieving sub-meV errors. Importantly, it facilitates large-scale parallel spin-lattice dynamics, thereby enabling the exploration of associated properties, including the magnetic ground state and phase transition. Remarkably, SpinGNN++ identifies a new ferrimagnetic state as the ground magnetic state for monolayer CrTe2, thereby enriching its phase diagram and providing deeper insights into the distinct magnetic signals observed in various experiments. Equivariant map ; Condensed matter physics ; Ground state ; Lattice (music) ; Physics ; Ferrimagnetism ; Phase diagram ; Artificial neural network ; Heisenberg model ; Anisotropy ; Coupling (piping) ; Computer science ; Statistical physics ; Ferromagnetism ; Mathematics ; Materials science ; Quantum mechanics ; Phase (matter) ; Magnetic field ; Artificial intelligence ; Magnetization Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Magnetic properties of thin films
Authors: Hyunwook Jung | Lena Sauerland | Sina Stocker | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2022-09-23)
Field: Materials Science — Materials Chemistry

Machine-Learning Driven Global Optimization of Surface Adsorbate Geometries The adsorption energies of molecular adsorbates on catalyst surfaces are key descriptors in computational catalysis research. For the relatively large reaction intermediates frequently encountered, e.g., in syngas conversion, a multitude of possible binding motifs leads to complex potential energy surfaces (PES), however. This implies that finding the optimal structure is a difficult global optimization problem, which leads to significant uncertainty about the stability of many intermediates. To tackle this issue, we present a global optimization protocol for surface adsorbate geometries which trains a surrogate machine learning potential on-the-fly. The approach is applicable to arbitrary surface models and adsorbates and minimizes both human intervention and the number of required DFT calculations by iteratively updating the training set with configurations explored by the algorithm. We demonstrate the efficiency of this approach for a diverse set of adsorbates on the Rh(111) and (211) surfaces. Surface (topology) ; Adsorption ; Global optimization ; Potential energy surface ; Computer science ; Stability (learning theory) ; Set (abstract data type) ; Catalysis ; Chemistry ; Chemical physics ; Materials science ; Molecule ; Mathematics ; Machine learning ; Algorithm ; Geometry ; Physical chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Topic Modeling
Authors: Jan Goran T. Tomacruz | Karl Ezra Pilario | Miguel Francisco M. Remolona | Allan Abraham B. Padama | Joey D. Ocon
Source: SHILAP Revista de lepidopterología (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

A Machine Learning-Accelerated Density Functional Theory (ML-DFT) Approach for Predicting Atomic Adsorption Energies on Monometallic Transition Metal Surfaces for Electrocatalyst Screening The global mission to reduce fossil fuel consumption has led to the escalating demand for electrochemical energy storage (EES) devices such as fuel cells and batteries. Computational techniques like Density Functional Theory (DFT) have recently been coupled with Machine Learning (ML) for high-throughput material screening and discovery. Transition metal surfaces are popular electrocatalyst candidates, but predictive ML regression models have only been applied to select metals such as Pt and Cu. Additionally, characterizing the contributions of each feature is challenging, especially on black-box models. In this work, regression models were trained to predict the adsorption energies of carbon, hydrogen, and oxygen on 27 fcc (111) monometallic surfaces and applied model-agnostic interpretation methods to evaluate feature importance. Over 200 adsorption energies on transition metal surfaces were collected from Catalysis-hub.org, a surface reaction database. A dataset was constructed for each adsorbate, and was composed of ten surface atomic, surface electronic, and adsorbate properties collected from online databases and DFT calculations on adsorbate-free surfaces. Then, the fine-tuned random forest regression, Gaussian process regression, and artificial neural network models predicted atomic adsorption energies while permutation feature importance calculated feature contributions. All ML model accuracies were found to be competitive with those from literature, with Gaussian process regression reporting the lowest errors of the three models. Coordination number was also found to have the largest contributions for all models. ML-DFT methodologies such as this can be expanded to accommodate alloys and more adsorbates for a wider screening of potential EES materials. Density functional theory ; Adsorption ; Electrocatalyst ; Transition metal ; Materials science ; Metal ; Computational chemistry ; Physical chemistry ; Chemistry ; Catalysis ; Metallurgy ; Organic chemistry ; Electrochemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Satya Bulusu | Srivathsan Vasudevan
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

FPGA Accelerator for Machine Learning Interatomic Potential-Based Molecular Dynamics of Gold Nanoparticles Molecular dynamics (MD) simulations involve computations of forces between atoms and the total energy of the chemical systems. The scientific community is dependent on high-end servers for such computations that are generally sequential and highly power hungry, thereby restricting these computations in reaching experimentally relevant large systems. This work explores the concept of parallelization of the code and accelerating them by exploring the usage of high level synthesis (HLS) based Field Programmable Gate Array (FPGA). This work proposes a hardware and software based interface to implement parallel algorithms in an FPGA framework and communication between the software and hardware interface is implemented. The forces of Au 147 obtained through the ANN based interatomic potentials in the proposed model shows an acceleration of 1.5 times compared with an expensive server with several nodes. Taking this work forward can result in a lab-on-a-chip application and this would potentially be applied onto several large experimentally relevant chemical systems. Field-programmable gate array ; Computer science ; Computation ; Interface (matter) ; Software ; Computational science ; Acceleration ; Molecular dynamics ; Server ; Embedded system ; Parallel computing ; Operating system ; Algorithm ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Memory and Neural Computing
Authors: Muhammad Ary Murti | Casi Setianingsih | Eka Kusumawardhani | Renal Farhan
Source: International Journal of Advanced Computer Science and Applications (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Cedarwood Quality Classification using SVM Classifier and Convolutional Neural Network (CNN) Cedarwood is one of the most sought-after materials since it can be used to create a wide variety of household appliances. Other than its unique aroma, the product's quality is the most important selling attribute. Fiber patterns allow for a qualitative categorization of this wood. Traditionally, workers in the wood-processing business have relied solely on their eyesight to sort materials into several categories. As a result, there will be discrepancies in precision and efficiency, which will hurt the reputation of the regional wood sector. The answer to this issue is machine learning. In this study, we compare the performance of two different cedarwood quality classification systems where both systems use different machine learning methods namely Support Vector Machine (SVM) and Convolutional Neural Network (CNN). Each system will be sent images captured with a Logitech Brio 4K equipped with a joystick and ultrasonic sensors, labeled as belonging to one of five cedar classes (A, B, C, D, or E). In the initial method to learn the wood's pattern and texture, the Histogram of Oriented Gradient (HOG) will be used to identify the material. Meanwhile, the classification method uses a Support Vector Machine (SVM) which will be compared to find the best accuracy and time computation. The first system's experiment achieves 90 percent accuracy with a computation time of 1.40 seconds. For the second, we use a Convolutional Neural Network, a deep learning technique, to classify cedarwood (CNN). Extraction of features occurs in the convolution, activation, and pooling layers. Experimental results demonstrated a considerable enhancement, with an accuracy of 97% and a prediction speed of 0.56 seconds. Computer science ; Convolutional neural network ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Classifier (UML) ; Machine learning ; Artificial neural network ; Categorization ; sort ; Histogram of oriented gradients ; Histogram Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Spectroscopy and Chemometric Analyses