jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bogdan Burlacu | Michael Kommenda | Gabriel Kronberger | Stephan Winkler | Michael Affenzeller
Source: arXiv (Cornell University) (preprint, 2022-06-13)
Field: Materials Science — Materials Chemistry

Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which allow to calculate the potential energy of an atomic system as a function of atomic coordinates and potentially other properties. First-principles-based ab initio potentials can reach arbitrary levels of accuracy, however their aplicability is limited by their high computational cost. Machine learning (ML) has recently emerged as an effective way to offset the high computational costs of ab initio atomic potentials by replacing expensive models with highly efficient surrogates trained on electronic structure data. Among a plethora of current methods, symbolic regression (SR) is gaining traction as a powerful "white-box" approach for discovering functional forms of interatomic potentials. This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results. A genetic programming-based approach for modeling atomic potentials from raw data (consisting of snapshots of atomic positions and associated potential energy) is presented and empirically validated on ab initio electronic structure data. Interatomic potential ; Ab initio ; Computer science ; Symbolic regression ; Electronic structure ; Statistical physics ; Artificial intelligence ; Genetic programming ; Chemistry ; Computational chemistry ; Physics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: S. M. Fraser | U. Shumlak
Source: 2022 IEEE International Conference on Plasma Science (ICOPS) (article, 2022-05-22)
Field: Materials Science — Materials Chemistry

Using Machine Learning to Generate a Surrogate Model of Plasma-Surface Interactions Plasma-surface interactions are an important effect in laboratory plasmas, but too complicated to be modelled directly in plasma simulations. However, plasma surface interactions can be modelled by Transport and Range of Ions in Matter (TRIM) simulations based on a binary collision approximation of energetic ions impinging on a stationary material. In this work artificial neural networks are used to generate a model of TRIM simulations for the energy-angular distribution of ions observed at the boundary of a five-moment simulation of the shear flow stabilized (SFS) z-pinch fusion experiment at the University of Washington for graphite and tungsten walls. The trained network then approximates plasma-surface interactions for conditions relevant to the SFS z-pinch fusion experiment. Connecting this model to a plasma simulation as a boundary condition promises to account for plasma-surface interactions for minimal computational expense. In anticipation of this, this network is shown to accurately model TRIM results for the parameters it was trained on. Plasma ; Trim ; Ion ; Tungsten ; Moment (physics) ; Physics ; Mechanics ; Atomic physics ; Computational physics ; Materials science ; Computer science ; Classical mechanics ; Nuclear physics Fusion materials and technologies ; Magnetic confinement fusion research ; Laser-Plasma Interactions and Diagnostics
Authors: Lenz Fiedler | Zhandos A. Moldabekov | Xuecheng Shao | Kaili Jiang | Tobias Dornheim | Marcelo Antonio Pavanello | Attila Cangi
Source: arXiv (Cornell University) (preprint, 2022-06-08)
Field: Materials Science — Materials Chemistry

Accelerating Equilibration in First-Principles Molecular Dynamics with Orbital-Free Density Functional Theory We introduce a practical hybrid approach that combines orbital-free density functional theory (DFT) with Kohn-Sham DFT for speeding up first-principles molecular dynamics simulations. Equilibrated ionic configurations are generated using orbital-free DFT for subsequent Kohn-Sham DFT molecular dynamics. This leads to a massive reduction of the simulation time without any sacrifice in accuracy. We assess this finding across systems of different sizes and temperature, up to the warm dense matter regime. To that end, we use the cosine distance between the time series of radial distribution functions representing the ionic configurations. Likewise, we show that the equilibrated ionic configurations from this hybrid approach significantly enhance the accuracy of machine-learning models that replace Kohn-Sham DFT. Our hybrid scheme enables systematic first-principles simulations of warm dense matter that are otherwise hampered by the large numbers of atoms and the prevalent high temperatures. Moreover, our finding provides an additional motivation for developing kinetic and noninteracting free energy functionals for orbital-free DFT. Density functional theory ; Molecular dynamics ; Orbital-free density functional theory ; Statistical physics ; Kinetic energy ; Ionic bonding ; Hybrid functional ; Warm dense matter ; Series (stratigraphy) ; Physics ; Materials science ; Computer science ; Classical mechanics ; Quantum mechanics ; Ion Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Jia Fu | Zhitao Wan | Zhangzhang Yang | Li Liu | Qunchao Fan | Feng Xie | Yi Zhang | Jie Ma
Source:  (preprint, 2022-02-28)
Field: Chemistry — Physical and Theoretical Chemistry

Combining ab initio and machine learning method to improve prediction performance of diatomic vibrational energies Through the comprehensive analysis of ab initio and experimental results of a large number of diatomic systems, the systematic deviation of ab initio method in vibrational energies prediction caused by physical/mathematical simplification is located. A joint ab initio and machine learning method based on information across molecules is proposed to deal with the problem. Starting from an ab initio model, and then systematically modifying it through machine learning, the vibrational energies prediction of many diatomic systems (SiC, HBr, NO, PC, N, SiO, O, ClF, etc.) have been improved, and significantly surpassed the more complex ab initio model. In addition to the improvement of accuracy, the new method also greatly reduces the computational expense, and is applicable for the systems without experimental data. Ab initio ; Diatomic molecule ; Ab initio quantum chemistry methods ; Computational chemistry ; Artificial intelligence ; Statistical physics ; Machine learning ; Atomic physics ; Chemistry ; Computer science ; Molecule ; Physics ; Quantum mechanics Various Chemistry Research Topics ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Zhongwei Chen | Boran Yang | Nannan Song | Tingting Chen | Qingwu Zhang | Changxin Li | Juncheng Jiang | Tao Chen | Yuan Yu | Lian X. Liu
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

An Interpretable Machine Learning Model to Guide the Design of Organic Phosphorus-Containing Flame Retardants for Improving the Limiting Oxygen Index of Epoxy Resins Limiting oxygen index ; Epoxy ; Limiting ; Fire retardant ; Index (typography) ; Phosphorus ; Oxygen ; Computer science ; Process engineering ; Materials science ; Chemical engineering ; Chemistry ; Artificial intelligence ; Organic chemistry ; Engineering ; Composite material ; Mechanical engineering Flame retardant materials and properties ; Epoxy Resin Curing Processes ; Synthesis and properties of polymers
Authors: Gunjan Mukherjee | Arpitam Chatterjee | Bipan Tudu
Source: International Journal of Computational Intelligence Studies (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

A multi-channel convolutional neural network driven computer vision system towards identification of species and maturity stage of banana fruits: case studies with Martaman and Singapuri banana Banana is a widely consumed fruit worldwide due to its high nutritional values. The nutritional impact of banana considerably varies with the species and ripening stages. The existing practices of both species and ripening stage detection of banana are performed majorly by human experience and chemical analysis. This paper presents a computer vision-based system development to identify banana species and corresponding ripening stages simultaneously. The proposed system is less expensive, non-invasive and faster with potential to provide up to 98% accuracy. The work utilises multi-channel convolutional neural network (CNN) architecture to classify the species and ripening stage in a single go. The results of the proposed system was assessed against different popular metrics and found competitive to the existing techniques. This technique can be also integrated to a handheld device or mobile app in future for firsthand assessment by the consumers and sellers in the market. Ripening ; Convolutional neural network ; Computer science ; Identification (biology) ; Maturity (psychological) ; Stage (stratigraphy) ; Artificial intelligence ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Kory Burns | Jordan A. Hachtel | Anikeya Aditya | Nitish Baradwaj | Ankit Mishra | Aiichiro Nakano | Rajiv Kalia | Eric L Jang | Ryan Schoell | Khalid Hattar | Assel Atikaliyeva
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-12-10)
Field: Materials Science — Materials Chemistry

Deformation Field Controlled Moire Patterns in MoS2 Homobilayers Ultrathin MoS<sub>2</sub> has shown remarkable characteristics at the atomic scale with immutable disorder to weak external stimuli. Ion beam irradiation unlocks the potential to selectively tune the size, concentration, and morphology of defects produced at the site of impact . Combining experiment, atomistic simulation and deep learning, we show that irradiation-induced defects can induce a rotation-dependent moiré pattern in vertically-stacked homobilayers of MoS<sub>2 </sub>by deforming the atomically thin material and exciting surface acoustic waves (SAWs). Additionally, by probing the intrinsic defects and atomic environment, we see a direct correlation to the stress contribution that lattice disorder and various polymorphs have. The method introduced in this paper sheds light on how engineering defects in the lattice gives full control on the angular mismatch in van der Waals (vdW) solids. Moiré pattern ; Deformation (meteorology) ; Materials science ; Field (mathematics) ; Composite material ; Structural engineering ; Physics ; Optics ; Engineering ; Mathematics 2D Materials and Applications
Authors: Diki Purnawati | Paul Rossener Regonia | Juan Paolo Bermundo | Kazushi Ikeda | Yukiharu Uraoka
Source: SID Symposium Digest of Technical Papers (article, 2022-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

31‐2: <i>Student Paper:</i> Fermi Level Prediction of Solution‐processed Ultra‐wide Band gap a‐Ga <sub>2</sub> O <sub>x</sub> via Supervised Machine Learning Models This work presents machine learning (ML) assisted Fermi level prediction of solution‐processed ultra‐wide bandgap (UWB) amorphous gallium oxide (a‐Ga 2 O x ) which can significantly accelerate the fabrication of semiconducting UWB a‐Ga 2 O x ‐based material for future display application. Different models such as Kernel Ridge Regression (KRR), Support Vector Regression (SVR) and Random Forest Regression (RFR) were trained with empirical features, including experimental thickness, annealing temperature and environment during the solution‐processed UWB a‐Ga 2 O x film fabrication. This work is a big step towards rapid and cost‐effective optimization method of fabricating UWB a‐Ga 2 O x ‐based devices. Annealing (glass) ; Support vector machine ; Fabrication ; Gallium ; Amorphous solid ; Random forest ; Materials science ; Band gap ; Simulated annealing ; Artificial intelligence ; Machine learning ; Computer science ; Optoelectronics ; Chemistry ; Metallurgy ; Crystallography Ga2O3 and related materials ; ZnO doping and properties ; Advanced Photocatalysis Techniques
Authors: Mehmet Cagri Kaymak | Ali Rahnamoun | Kurt A. O’Hearn | Adri C. T. van Duin | Kenneth M. Merz | Hasan Metin Aktulga
Source:  (preprint, 2022-01-03)
Field: Materials Science — Materials Chemistry

JAX-ReaxFF: A Gradient Based Framework for Extremely Fast Optimization of Reactive Force Fields Molecular dynamics (MD) simulations facilitate the study of physical and chemical processes of interest. Traditional classical MD models lack reactivity to explore several important phenomena; while quantum mechanical (QM) models can be used for this purpose, they come with steep computational costs. The reactive force field (ReaxFF) model bridges the gap between these approaches by incorporating dynamic bonding and polarizability. To achieve realistic simulations using ReaxFF, model parameters must be optimized against high fidelity training data, typically with QM accuracy. Existing parameter optimization methods for ReaxFF consist of black-box techniques using genetic algorithms or Monte-Carlo methods. Due to the stochastic behavior of these methods, the optimization process can require millions of error evaluations for complex parameter fitting tasks, significantly hampering the rapid development of high quality parameter sets. In this work, we present JAX ReaxFF, a novel software tool that leverages modern machine learning infrastructure to enable extremely fast optimization of ReaxFF parameters. By calculating gradients of the loss function using the JAX library, we are able to utilize highly effective local optimization methods, such as the limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) and Sequential Least Squares Programming (SLSQP) methods. As a result of the performance portability of JAX, JAX-ReaxFF can execute efficiently on multi-core CPUs, GPUs (or even TPUs). By leveraging the gradient information and modern hardware accelerators, we are able to decrease parameter optimization time for ReaxFF from days to mere minutes. JAX-ReaxFF framework can also serve as a sandbox environment for domain scientists to explore customizing the ReaxFF functional form for more accurate modeling. ReaxFF ; Computer science ; Force field (fiction) ; Molecular dynamics ; Software portability ; Chemistry ; Computational chemistry ; Artificial intelligence Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Advanced Memory and Neural Computing
Authors: Ercin C. Duran | Irene Azaceta | Adam Kerrigan | Vlado K. Lazarov | Alexander S. Eggeman
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Utilising Unsupervised Machine Learning on Correlated EDS and 4DSTEM Data for Investigating the Structural Ordering Within Co2FeSi Thin Films Journal Article Utilising Unsupervised Machine Learning on Correlated EDS and 4DSTEM Data for Investigating the Structural Ordering Within Co2FeSi Thin Films Get access Ercin Duran, Ercin Duran Department of Materials, University of Manchester, Manchester, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Irene Azaceta, Irene Azaceta Department of Physics, University of York, York, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Adam Kerrigan, Adam Kerrigan Department of Physics, University of York, York, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Vlado Lazarov, Vlado Lazarov Department of Physics, University of York, York, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Alexander Eggeman Alexander Eggeman Department of Materials, University of Manchester, Manchester, United Kingdom Corresponding author: alexander.eggeman@manchester.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 418–419, https://doi.org/10.1017/S1431927622002379 Published: 01 August 2022 Kingdom ; Library science ; Art history ; Media studies ; History ; Sociology ; Computer science ; Geology Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Advanced Thermoelectric Materials and Devices
Authors: Tran Doan Huan
Source: Nature Computational Science (article, 2022-05-19)
Field: Materials Science — Materials Chemistry

Accelerating quantum molecular simulations Monte Carlo method ; Quantum Monte Carlo ; Statistical physics ; Variational Monte Carlo ; Monte Carlo molecular modeling ; Computer science ; Dynamic Monte Carlo method ; Artificial neural network ; Monte Carlo method in statistical physics ; Hybrid Monte Carlo ; Quantum ; Physics ; Mathematical optimization ; Applied mathematics ; Markov chain Monte Carlo ; Mathematics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Abdourahman Khaireh-Walieh | Alexandre Arnoult | Sébastien Plissard | Peter R. Wiecha
Source: arXiv (Cornell University) (preprint, 2022-10-07)
Field: Materials Science — Surfaces, Coatings and Films

Monitoring MBE substrate deoxidation via RHEED image-sequence analysis by deep learning Reflection high-energy electron diffraction (RHEED) is a powerful tool in molecular beam epitaxy (MBE), but RHEED images are often difficult to interpret, requiring experienced operators. We present an approach for automated surveillance of GaAs substrate deoxidation in MBE reactors using deep learning based RHEED image-sequence classification. Our approach consists of an non-supervised auto-encoder (AE) for feature extraction, combined with a supervised convolutional classifier network. We demonstrate that our lightweight network model can accurately identify the exact deoxidation moment. Furthermore we show that the approach is very robust and allows accurate deoxidation detection during months without requiring re-training. The main advantage of the approach is that it can be applied to raw RHEED images without requiring further information such as the rotation angle, temperature, etc. Reflection high-energy electron diffraction ; Electron diffraction ; Molecular beam epitaxy ; Classifier (UML) ; Artificial intelligence ; Computer science ; Reflection (computer programming) ; Autoencoder ; Materials science ; Deep learning ; Pattern recognition (psychology) ; Diffraction ; Optics ; Epitaxy ; Physics ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Eun-Ah Kim | Krishnanand Mallayya | Joshua Straquadine | Matthew Krogstad | Maja D. Bachmann | Anisha G. Singh | R. Osborn | Stephan Rosenkranz | I. R. Fisher
Source: Research Square (preprint, 2022-10-14)
Field: Materials Science — Materials Chemistry

Bragg glass signatures in PdxErTe3 with X-ray diffraction Temperature Clustering (X-TEC) TEC ; Diffraction ; X-ray ; Materials science ; X-ray crystallography ; Cluster analysis ; Optics ; Crystallography ; Geology ; Physics ; Chemistry ; Geophysics ; Computer science ; Artificial intelligence Phase-change materials and chalcogenides ; Optical properties and cooling technologies in crystalline materials ; Advanced Thermoelectric Materials and Devices
Authors: Gabriel Corrêa Veríssimo | Valtair Severino dos Santos | Philipe Oliveira Fernandes | Shoichi Ishida | Ryosuke Kojima | Yasushi Okuno | Jadson Castro Gertrudes | Vinícius Gonçalves Maltarollo
Source: International Journal of Quantitative Structure-Property Relationships (article, 2022-11-04)
Field: Chemistry — Organic Chemistry

GCN-Based Structure-Activity Relationship and DFT Studies of Staphylococcus aureus FabI Inhibitors The enoyl-[acyl-carrier-protein] reductase (FabI) is an important enzyme in the fatty acid metabolism of Gram-positive bacteria, such as Staphylococcus aureus. FabI is also a potential target for the development of novel antibacterials. Several machine learning-driven studies were reported to develop FabI inhibitors, describing robust and predictive models. Herein, the authors applied the kGCN, a graph convolutional network framework, to generate classification models to select potential S. aureus FabI inhibitors. The most predictive model showed robustness for both active and inactive class prediction, according to statistical validation. Finally, the chemical interpretation of the model was consistent with prior experimental and theoretical works. The SAR analysis highlighted the importance of the occupation of hydrophobic pockets and polar interactions with Tyr-156 and NADPH cofactor present in the FabI catalytic site by potential inhibitors. A density functional theory study endorsed the SAR, where the electrostatic surfaces were consistent with the expected interactions with the pocket. Staphylococcus aureus ; Robustness (evolution) ; Cofactor ; Enzyme ; Computational biology ; Reductase ; Biology ; Chemistry ; Biochemistry ; Bacteria ; Microbiology ; Gene Antimicrobial agents and applications ; Computational Drug Discovery Methods ; Antimicrobial Peptides and Activities
Authors: Nicolas Bertin | Fei Zhou
Source: arXiv (Cornell University) (preprint, 2022-08-05)
Field: Materials Science — Materials Chemistry

Accelerating discrete dislocation dynamics simulations with graph neural networks Discrete dislocation dynamics (DDD) is a widely employed computational method to study plasticity at the mesoscale that connects the motion of dislocation lines to the macroscopic response of crystalline materials. However, the computational cost of DDD simulations remains a bottleneck that limits its range of applicability. Here, we introduce a new DDD-GNN framework in which the expensive time-integration of dislocation motion is entirely substituted by a graph neural network (GNN) model trained on DDD trajectories. As a first application, we demonstrate the feasibility and potential of our method on a simple yet relevant model of a dislocation line gliding through an array of obstacles. We show that the DDD-GNN model is stable and reproduces very well unseen ground-truth DDD simulation responses for a range of straining rates and obstacle densities, without the need to explicitly compute nodal forces or dislocation mobilities during time-integration. Our approach opens new promising avenues to accelerate DDD simulations and to incorporate more complex dislocation motion behaviors. Bottleneck ; Dislocation ; Computer science ; Algorithm ; Artificial neural network ; Range (aeronautics) ; Statistical physics ; Graph ; Topology (electrical circuits) ; Physics ; Artificial intelligence ; Mathematics ; Materials science ; Theoretical computer science Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Topic Modeling
Authors: Kazuki Kobayashi | Zhenhua Pan | Yuya Nagai | ‍Kenji Katayama
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Identification of Dominant Factors Contributing to Photocurrent Density of Bivo4 Photoanodes Using Machine Learning Photocurrent ; Identification (biology) ; Materials science ; Nanotechnology ; Computer science ; Optoelectronics ; Biology ; Botany Machine Learning in Materials Science