jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ethan Berger | Zhong‐Peng Lv | Hannu‐Pekka Komsa
Source: arXiv (Cornell University) (preprint, 2022-09-30)
Field: Materials Science — Materials Chemistry

Raman Spectra of Titanium Carbide MXene from Machine-Learning Force Field Molecular Dynamics MXenes represent one of the largest class of 2D materials with promising applications in many fields and their properties tunable by the surface group composition. Raman spectroscopy is expected to yield rich information about the surface composition, but the interpretation of measured spectra has proven challenging. The interpretation is usually done via comparison to simulated spectra, but there are large discrepancies between the experimental and earlier simulated spectra. In this work, we develop a computational approach to simulate Raman spectra of complex materials that combines machine-learning force-field molecular dynamics and reconstruction of Raman tensors via projection to pristine system modes. The approach can account for the effects of finite temperature, mixed surfaces, and disorder. We apply our approach to simulate Raman spectra of titanium carbide MXene and show that all these effects must be included in order to properly reproduce the experimental spectra, in particular the broad features. We discuss the origin of the peaks and how they evolve with surface composition, which can then be used to interpret experimental results. MXenes ; Raman spectroscopy ; Spectral line ; Force field (fiction) ; Titanium carbide ; Materials science ; Molecular dynamics ; Characterization (materials science) ; Raman scattering ; Carbide ; Chemical physics ; Nanotechnology ; Chemistry ; Computational chemistry ; Computer science ; Physics ; Optics ; Artificial intelligence ; Composite material ; Quantum mechanics MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Seiji Suga | Eisuke F. Sato | Mayu Fujii | Hiroki Tanaka | Koichi Mitsudo
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Chemistry — Organic Chemistry

(Digital Presentation) Electrochemical Cyanosilylation of Carbonyl Compounds: Machine Learning-Assisted Exploration of Suitable Reaction Conditions Cyanosilylation of carbonyl compounds provides protected cyanohydrins, which can easily be converted into various compounds such as amino alcohols, amides, esters, and carboxylic acids. We herein describe an electrochemically initiated cyanosilylation of carbonyl compounds using trimethylsilyl cyanide as the sole carbon source and its application to a microflow reactor. To optimize suitable reaction conditions, we demonstrate the machine learning-assisted exploration. Machine learning well supported us to adjust the current and flow rate at the same time and identify the conditions needed to achieve the best productivity. Cyanohydrin ; Trimethylsilyl cyanide ; Trimethylsilyl ; Chemistry ; Electrochemistry ; Cyanide ; Flow chemistry ; Organic chemistry ; Combinatorial chemistry ; Computer science ; Catalysis ; Electrode Sulfur-Based Synthesis Techniques ; Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods
Authors: Ankush Sood
Source: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (article, 2022-05-01)
Field: Materials Science — Materials Chemistry

EDAML 2022 Invited Speaker 1: Application of Machine Learning in High Level Synthesis Traditionally high-level optimizations like CSA and sharing have been done technology independent. Doing word level optimizations PPA aware require accurate models for power, timing and area and that needs mapping to technology library and iterations which are runtime intensive and not suitable for multimillion instance designs. In this talk, we discuss how machine learning could help make the right power/area/delay tradeoffs early in the synthesis flow not sacrificing on turnaround time. Computer science ; Computer architecture ; Turnaround time ; Power (physics) ; Power flow ; Computer engineering ; Artificial intelligence ; Electric power system ; Operating system Machine Learning in Materials Science
Authors: Zhisen Meng | Yongchuan Wu | Enwei Liang | Wang, Zhao
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-09-01)
Field: Chemistry — Spectroscopy

Supporting Information for "Machine-learning identified molecular fragments of polycyclic aromatic hydrocarbons responsible for interstellar infrared emission bands" This is the supporting information for the article "Machine-learning identified molecular fragments of polycyclic aromatic hydrocarbons responsible for interstellar infrared emission bands". It contains: 1) In Supporting_Information_For_Molecular_Fragment_Table.pdf: Table: Molecular fragments responsible for infrared emission features of polycyclic aromatic hydrocarbons. 2) In Supporting_Information_For_Example_Code.pdf: An explanation of the file example_code 3) In example_code.zip: An example code of machine learning model and corresponding input files. Infrared ; Polycyclic aromatic hydrocarbon ; Interstellar medium ; Photochemistry ; Chemistry ; Astrophysics ; Physics ; Environmental chemistry ; Astronomy ; Galaxy Molecular Spectroscopy and Structure ; Analytical Chemistry and Chromatography ; Chemical Thermodynamics and Molecular Structure
Authors: Shashank Pathrudkar | Hsuan Ming Yu | Susanta Ghosh | Amartya Banerjee
Source: Physical review. B./Physical review. B (article, 2022-05-26)
Field: Materials Science — Materials Chemistry

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain Materials scientists from UCLA and Michigan Technological University have trained artificial intelligence (AI) to predict the electronic properties of nanoscale strands of building blocks of materials after they have been twisted, stretched, or compressed. Described as ``quasi-one-dimensional,'' this class of materials include nanotubes, nanoribbons, nanowires, and similar configurations of materials that are long and thin. The researchers use specialized quantum mechanical simulation techniques to produce the data that the AI models are trained on. Remarkably, the AI models, based on artificial neural networks, are able to discern hidden patterns in the simulations data and to predict accurately the complex electronic structure of the strained quasi-one-dimensional materials, based on very limited training. The researchers anticipate that such AI-assisted simulation capabilities of nanomaterials may lead to the discovery of new materials for energy storage technologies, quantum computing hardware, advanced sensors, and a range of other electronic devices. Electronic structure ; Electronic density ; Twist ; Statistical physics ; Computer science ; Materials science ; Physics ; Topology (electrical circuits) ; Density functional theory ; Mathematics ; Condensed matter physics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Topological Materials and Phenomena ; 2D Materials and Applications
Authors: Md Esharuzzaman Emu
Source: arXiv (Cornell University) (preprint, 2022-06-08)
Field: Materials Science — Materials Chemistry

Predictions of Electromotive Force of Magnetic Shape Memory Alloy (MSMA) Using Constitutive Model and Generalized Regression Neural Network Ferromagnetic shape memory alloys (MSMAs), such as Ni-Mn-Ga single crystals, can exhibit the shape memory effect due to an applied magnetic field at room temperature. Under a variable magnetic field and a constant bias stress loading, MSMAs have been used for actuation applications. This work introduced a new feature to the existing macroscale magneto-mechanical model for Ni-Mn-Ga single crystal. This model includes the fact that the magnetic easy axis in the two variants is not exactly perpendicular as observed by D silva et al. This offset helps explain some of the power harvesting capabilities of MSMAs. Model predictions are compared to experimental data collected on a Ni-Mn-Ga single crystal. The experiments include both stress-controlled loading with constant bias magnetic field load (which mimics power harvesting or sensing) and fieldcontrolled loading with constant bias compressive stress (which mimics actuation). Each type of test was performed at several different load levels, and the applied field was measured without the MSMA specimen present so that demagnetization does not affect the experimentally measured field as suggested by Eberle et al. Results show decent agreement between model predictions and experimental data. Although the model predicts experimental results decently, it does not capture all the features of the experimental data. In order to capture all the experimental features, finally, a generalized regression neural network (GRNN) was used to train the experimental data (stress, strain, magnetic field, and emf) so that it can make a reasonably better prediction. Magnetic field ; Electromotive force ; Materials science ; Stress (linguistics) ; Test data ; Demagnetizing field ; Artificial neural network ; Offset (computer science) ; Shape-memory alloy ; Mechanics ; Condensed matter physics ; Computer science ; Physics ; Magnetization ; Metallurgy ; Engineering ; Electrical engineering ; Artificial intelligence Shape Memory Alloy Transformations
Authors: Takumi Kuroda | Tomonari Mizoguchi | Hiromu Araki | Yasuhiro Hatsugai
Source: Journal of the Physical Society of Japan (preprint, 2022-03-25)
Field: Materials Science — Materials Chemistry

Machine Learning Study on the Flat-Band States Constructed by Molecular-Orbital Representation with Randomness We study the characteristic probability density distribution of random flat band models by machine learning. The models considered here are constructed on the basis of the molecular-orbital representation, which guarantees the existence of the macroscopically degenerate zero-energy modes even in the presence of randomness. We find that flat band states are successfully distinguished from conventional extended and localized states, indicating the characteristic feature of the flat band states. We also find that the flat band states can be detected when the target data are defined in the different lattice from the training data, which implies the universal feature of the flat band states constructed by the molecular-orbital representation. Randomness ; Degenerate energy levels ; Representation (politics) ; Density of states ; Feature (linguistics) ; Lattice (music) ; Statistical physics ; Physics ; Basis (linear algebra) ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science
Authors: Jong Youl Choi | Pei Zhang | Kshitij Mehta | A.J. Blanchard | Massimiliano Lupo Pasini
Source: arXiv (Cornell University) (preprint, 2022-07-22)
Field: Materials Science — Materials Chemistry

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing i) reduction of data loading time up to 4.2 times compared with a conventional method and ii) linear scaling performance for training up to 1,024 GPUs on both Summit and Perlmutter. Computer science ; Scalability ; Supercomputer ; Graph ; Convolutional neural network ; Parallel computing ; Semantic similarity ; Big data ; Deep learning ; Benchmark (surveying) ; Theoretical computer science ; Artificial intelligence ; Database ; Data mining Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Jong Youl Choi | Pei Zhang | Kshitij Mehta | Andrew E. Blanchard | Massimiliano Lupo Pasini
Source: Research Square (preprint, 2022-09-21)
Field: Materials Science — Materials Chemistry

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing i) reduction of data loading time up to 4.2 times compared with a conventional method and ii) linear scaling performance for training up to 1,024 GPUs on both Summit and Perlmutter. Computer science ; Scalability ; Supercomputer ; Convolutional neural network ; Graph ; Parallel computing ; Semantic similarity ; Big data ; Deep learning ; Benchmark (surveying) ; Theoretical computer science ; Artificial intelligence ; Database ; Data mining Machine Learning in Materials Science ; Advanced Graph Neural Networks ; CO2 Reduction Techniques and Catalysts
Authors: José Ferraz-Caetano
Source: ChemRxiv (preprint, 2022-04-06)
Field: Materials Science — Materials Chemistry

Current Outlooks on Machine Learning Methods for the Development of Industrial Homogeneous Catalytic Systems This brief perspective outlines the pivotal role of Machine Learning methods in the green, digital transition of industrial chemistry. The focus on homogenous catalysis highlights the recent methodologies in the development of industrial processes, including the design of new catalysts and enhancement of sustainable reaction conditions to lower production costs. We report several examples of Machine Learning assisted methodologies with the recent Data Science trends on innovation of industrial homogeneous organocatalytic systems. We also stress the current benefits, drawbacks and limitations towards the mass implementation of these Data Science methodologies. Homogeneous ; Computer science ; Perspective (graphical) ; Sustainable development ; Biochemical engineering ; Artificial intelligence ; Management science ; Data science ; Engineering ; Political science ; Mathematics Machine Learning in Materials Science
Authors: Xiaotong Liu | Pierre-Paul De Breuck | Linghui Wang | Gian‐Marco Rignanese
Source: arXiv (Cornell University) (preprint, 2022-04-21)
Field: Materials Science — Materials Chemistry

A simple denoising approach to exploit multi-fidelity data for machine learning materials properties Machine-learning models have recently encountered enormous success for predicting the properties of materials. These are often trained based on data that present various levels of accuracy, with typically much less high- than low-fidelity data. In order to extract as much information as possible from all available data, we here introduce an approach which aims to improve the quality of the data through denoising. We investigate the possibilities that it offers in the case of the prediction of the band gap relying on both limited experimental data and density-functional theory relying different exchange-correlation functionals (with an increasing amount of data as the accuracy of the functional decreases). We explore different ways to combine the data into training sequences and analyze the effect of the chosen denoiser. Finally, we analyze the effect of applying the denoising procedure several times until convergence. Our approach provides an improvement over existing methods to exploit multi-fidelity data. Exploit ; Fidelity ; Computer science ; Noise reduction ; Machine learning ; Convergence (economics) ; Artificial intelligence ; Simple (philosophy) ; Data mining ; Experimental data ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Sree Harsha Bhimineni | Tianhang Zhou | Saeed Mahmoodpour | Mrityunjay Singh | Wei Li | Saientan Bag | Ingo Sass | Florian Müller‐Plathe
Source: arXiv (Cornell University) (preprint, 2022-07-06)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Investigation of the Diffusion of Hydrogen in Brine by Performing Molecular Dynamics Simulation Deep saline aquifers are one of the best options for large-scale and long-term hydrogen storage. Predicting the diffusion coefficient of hydrogen molecules at the conditions of saline aquifers is critical for modelling hydrogen storage. The diffusion coefficient of hydrogen molecules in chloride brine with different cations ($\mathrm{Na}^+$, $\mathrm{K}^+$, $\mathrm{Ca}^{2+}$) containing up to 5 $\mathrm{mol/kg_{H_2O}}$ concentration is numerically investigated using molecular dynamics (MD) simulation. A wide range of pressure (1-218 atm) and temperature (298-648 K) conditions is applied to cover the realistic operational conditions of the aquifers. We find that the temperature, pressure and properties of ions (compositions and concentrations) affect the hydrogen diffusion coefficient. An Arrhenius behavior of the effect of temperature on the diffusion coefficient has been observed with the temperature independent parameters fitted using the ion concentration under constant pressure. However, it is noted that the pressure strongly affects the diffusive behavior of hydrogen at the high temperature ($\geq$ 400 K) regime, indicating the inaccuracy of the Arrhenius model. Hence, we combine the obtained MD results with four models of machine learning (ML), including linear regression (LR), random forest (RF), extra tree (ET) and gradient boosting (GB) to provide effective predictions on the hydrogen diffusion. The resultant combination of GB model with MD data predicts the diffusion of hydrogen more effectively as compared to the Arrhenius model and other ML models. Moreover, a $post hoc$ analysis (feature importance rank) has been performed to extract the correlation between physical descriptors and simulation results from ML models. Arrhenius equation ; Hydrogen ; Thermodynamics ; Arrhenius plot ; Diffusion ; Brine ; Molecular dynamics ; Effective diffusion coefficient ; Chemistry ; Materials science ; Analytical Chemistry (journal) ; Activation energy ; Physical chemistry ; Physics ; Computational chemistry ; Chromatography ; Organic chemistry Hydrogen Storage and Materials ; Chemical Synthesis and Characterization ; Groundwater flow and contamination studies
Authors: Ryo Kawada | Katsuhiro Endo | Daisuke Yuhara | Kenji Yasuoka
Source: arXiv (Cornell University) (preprint, 2022-02-02)
Field: Materials Science — Materials Chemistry

MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data. For the accurate prediction of MD-GAN, sufficient information on the dynamics of a part of the system should be included with the training data. Therefore, the selection of the part of the system is important for efficient learning. In a previous study, only one particle (or vector) of each molecule was extracted as part of the system. Therefore, we investigated the effectiveness of adding information from other particles to the learning process. In the experiment of the polyethylene system, when the dynamics of three particles of each molecule were used, the diffusion was successfully predicted using one-third of the time length of the training data, compared to the single-particle input. Surprisingly, the unobserved transition of diffusion in the training data was also predicted using this method. Diffusion ; Molecular dynamics ; Particle (ecology) ; Process (computing) ; Computer science ; Dynamics (music) ; Biological system ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics ; Chemistry ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; NMR spectroscopy and applications
Authors: Ethan P. Shapera | André Schleife
Source: Advanced Optical Materials (article, 2022-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Discovery of New Plasmonic Metals via High‐Throughput Machine Learning (Advanced Optical Materials 18/2022) Ethan P. Shapera and André Schleife have constructed, validated, and applied machine learning models to search an online database for potential new plasmonic materials (see article number 2200158). The authors predict three high-quality-factor metals: AlCu3 (bottom on the cover, plasma frequency = 9.0 eV), ZnCu (middle, plasma frequency = 9.59 eV), and ZnGa3 (top, plasma frequency = 10.64 eV). Furthermore, ZnCu is found to have no interband optical absorption up to 1.9 eV. [Cover image generated by Xinzhi Hu and Ye Lin.] Plasmon ; Cover (algebra) ; Materials science ; Plasma ; Absorption (acoustics) ; Throughput ; Optoelectronics ; Quality (philosophy) ; Nanotechnology ; Computer science ; Physics ; Telecommunications ; Composite material ; Wireless ; Mechanical engineering ; Quantum mechanics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis