jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hari Narayan Bhargaw | Samarth Singh | B. A. Botre | S. A. Akbar | S. A. R. Hashmi | Poonam Sinha
Source: IEEE Sensors Journal (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

Deep Neural Network-Based Physics-Inspired Model of Self-Sensing Displacement Estimation for Antagonistic Shape Memory Alloy Actuator The paper introduces a self-sensing feature that utilizes differential resistance measurement of an antagonistic Shape Memory Alloy (SMA) actuator to estimate linear displacement. The external position sensor used for control feedback becomes extraneous while utilizing an electrical resistance as feedback. The self-sensing capability provides additional advantages such as the overall reduction in size, weight, and interface complexity of an actuator. SMA actuator wires were used in the antagonistic configuration for the bi-directional actuation of the targeted application. A Deep Neural Network (DNN) having Long Short Term Memory (LSTM) layers were used to estimate the dynamic relation of resistance and displacement. A novel DNN model was developed for estimation purposes inspired by the physics-based description of the SMA actuation phenomenon. Accordingly, DNN was developed using the first and last layers as LSTM and a middle layer of feedforward neural network. Estimation results are presented with performance evaluation in terms of the R-squared index and mean absolute error (MAE) of the proposed model; also, the model has been compared with the generic LSTM 2-layered model. Actuator ; Artificial neural network ; SMA* ; Displacement (psychology) ; Feed forward ; Shape-memory alloy ; Control theory (sociology) ; Computer science ; Position (finance) ; Artificial intelligence ; Feature (linguistics) ; Mean squared error ; Feedforward neural network ; Reduction (mathematics) ; Algorithm ; Engineering ; Control engineering ; Mathematics ; Control (management) Shape Memory Alloy Transformations ; Aeroelasticity and Vibration Control ; Smart Materials for Construction
Authors: Xiaohao Cao | Yanjing He | Zhengqing Zhang | Yuxiu Sun | Qi Han | Yandong Guo | Chongli Zhong
Source: Chemical Research in Chinese Universities (article, 2022-01-14)
Field: Materials Science — Materials Chemistry

Predicting of Covalent Organic Frameworks for Membrane-based Isobutene/1,3-Butadiene Separation: Combining Molecular Simulation and Machine Learning Membrane ; Selectivity ; Covalent bond ; Covalent organic framework ; Chemistry ; Adsorption ; Membrane technology ; Biochemical engineering ; Chemical engineering ; Organic chemistry ; Engineering Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Mario Krenn | Qianxiang Ai | Senja Barthel | Nessa Carson | Angelo Frei | Nathan C. Frey | Pascal Friederich | Théophile Gaudin | Alberto Alexander Gayle | Kevin Maik Jablonka | Rafael F. Lameiro | Dominik Lemm | Alston Lo | Seyed Mohamad Moosavi | José Manuel Nápoles-Duarte | AkshatKumar Nigam | Robert Pollice | Kohulan Rajan | Ulrich Schatzschneider | Philippe Schwaller | Marta Skreta | Berend Smit | Felix Strieth‐Kalthoff | Chong Sun | Gary Tom | Guido Falk von Rudorff | Andrew Z. Wang | Andrew J. P. White | Adamo Young | Rose Yu | Alán Aspuru‐Guzik
Source: White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (preprint, 2022-03-31)
Field: Materials Science — Materials Chemistry

SELFIES and the future of molecular string representations Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science. Cheminformatics ; Computer science ; Artificial intelligence ; Interpretability ; Popularity ; String (physics) ; Context (archaeology) ; Representation (politics) ; Interpretation (philosophy) ; Interface (matter) ; Data science ; Cognitive science ; Chemistry ; Programming language ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Pedro Antonio Santos-Flórez | Howard Yanxon | Byungkyun Kang | Yansun Yao | Qiang Zhu
Source: Physical Review Letters (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics In this Letter, we present a framework that combines machine learning potential (MLP) and metadynamics to investigate solid-solid phase transition. Based on the spectral descriptors and neural networks regression, we develop a scalable MLP model to warrant an accurate interpolation of the energy surface where two phases coexist. Applying it to the simulation of B4-B1 phase transition of GaN under 50 GPa with different model sizes, we observe sequential change of the phase transition mechanism from collective modes to nucleation and growths. When the size is at or below 128 000 atoms, the nucleation and growth appear to follow a preferred direction. At larger sizes, the nuclei occur at multiple sites simultaneously and grow to microstructures by passing the critical size. The observed change of the atomistic mechanism manifests the importance of statistical sampling with large system size in phase transition modeling. Nucleation ; Metadynamics ; Phase transition ; Materials science ; Statistical physics ; Chemical physics ; Crystal (programming language) ; Phase (matter) ; Interpolation (computer graphics) ; Condensed matter physics ; Computer science ; Physics ; Molecular dynamics ; Artificial intelligence ; Thermodynamics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Microstructure and mechanical properties
Authors: Guozheng Fan | Adam McSloy | Bálint Aradi | ChiYung Yam | Thomas Frauenheim
Source: The Journal of Physical Chemistry Letters (article, 2022-10-21)
Field: Materials Science — Materials Chemistry

Obtaining Electronic Properties of Molecules through Combining Density Functional Tight Binding with Machine Learning We have introduced a machine learning workflow that allows for optimizing electronic properties in the density functional tight binding method (DFTB). The workflow allows for the optimization of electronic properties by generating two-center integrals, either by training basis function parameters directly or by training a spline model for the diatomic integrals, which are then used to build the Hamiltonian and the overlap matrices. Using our workflow, we have managed to obtain improved electronic properties, such as charge distributions, dipole moments, and approximated polarizabilities. While both machine learning approaches enabled us to improve on the electronic properties of the molecules as compared with existing DFTB parametrizations, only by training on the basis function parameters we were able to obtain consistent Hamiltonians and overlap matrices in the physically reasonable ranges or to improve on multiple electronic properties simultaneously. Workflow ; Dipole ; Tight binding ; Hamiltonian (control theory) ; Computer science ; Diatomic molecule ; Electronic structure ; Statistical physics ; Computational chemistry ; Artificial intelligence ; Machine learning ; Chemistry ; Mathematics ; Physics ; Molecule ; Quantum mechanics ; Mathematical optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Hadi Abroshan | H. Shaun Kwak | Yuling An | Christopher T. Brown | Anand Chandrasekaran | Paul Winget | Mathew D. Halls
Source: Frontiers in Chemistry (article, 2022-01-17)
Field: Materials Science — Materials Chemistry

Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics Data-driven methods are receiving increasing attention to accelerate materials design and discovery for organic light-emitting diodes (OLEDs). Machine learning (ML) has enabled high-throughput screening of materials properties to suggest new candidates for organic electronics. However, building reliable predictive ML models requires creating and managing a high volume of data that adequately address the complexity of materials' chemical space. In this regard, active learning (AL) has emerged as a powerful strategy to efficiently navigate the search space by prioritizing the decision-making process for unexplored data. This approach allows a more systematic mechanism to identify promising candidates by minimizing the number of computations required to explore an extensive materials library with diverse variables and parameters. In this paper, we applied a workflow of AL that accounts for multiple optoelectronic parameters to identify materials candidates for hole-transport layers (HTL) in OLEDs. Results of this work pave the way for efficient screening of materials for organic electronics with superior efficiencies before laborious simulations, synthesis, and device fabrication. OLED ; Workflow ; Electronics ; Computer science ; Organic electronics ; Chemical space ; Process (computing) ; Throughput ; Nanotechnology ; Materials science ; Wireless ; Engineering ; Telecommunications ; Drug discovery ; Electrical engineering ; Bioinformatics ; Database Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Organic Light-Emitting Diodes Research
Authors: Alexander E. Mayer | M V Lekanov | Natalya A. Grachyova | E. V. Fomin
Source: Metals (article, 2022-02-25)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Model of Elastic—Plastic Deformation of Copper for Application to Shock Wave Problem Molecular dynamics (MD) simulations explored the deformation behavior of copper single crystal under various axisymmetric loading paths. The obtained MD dataset was used for the development of a machine-learning-based model of elastic–plastic deformation of copper. Artificial neural networks (ANNs) approximated the elastic stress–strain relation in the form of tensor equation of state, as well as the thresholds of homogeneous nucleation of dislocations, phase transition and the beginning of spall fracture. The plastic part of the MD curves was used to calibrate the dislocation plasticity model by means of the probabilistic Bayesian algorithm. The developed constitutive model of elastic–plastic behavior can be applied to simulate the shock waves in thin copper samples under dynamic impact. Materials science ; Plasticity ; Isotropy ; Nucleation ; Viscoplasticity ; Mechanics ; Dislocation ; Constitutive equation ; Deformation (meteorology) ; Shock (circulatory) ; Copper ; Shock wave ; Composite material ; Structural engineering ; Finite element method ; Thermodynamics ; Metallurgy ; Physics ; Optics ; Engineering Microstructure and mechanical properties ; High-Velocity Impact and Material Behavior ; Microstructure and Mechanical Properties of Steels
Authors: Yoonbeom Park | Kyoungah Cho | Sangsig Kim
Source: ACS Applied Materials & Interfaces (article, 2022-02-25)
Field: Materials Science — Materials Chemistry

Performance Prediction of Hybrid Energy Harvesting Devices Using Machine Learning In this study, we used machine learning to predict the output power of hybrid energy devices (HEDs) comprising photovoltaic cells (PVCs) and thermoelectric generators (TEGs). For the five types of HEDs, eight different machine learning models were trained and tested with experimental data; the HED each had different interface materials between the PVCs and the TEGs. An artificial neural network (ANN) model, which is the most appropriate model, predicted the correlation between HED performance and interface material properties. The ANN model demonstrated that the output power of the HED with a carbon paste interface material at an irradiance of 1000 W/m<sup>2</sup> was 2.6% higher than that of a PVC alone. Materials science ; Artificial neural network ; Interface (matter) ; Energy (signal processing) ; Photovoltaic system ; Energy harvesting ; Power (physics) ; Thermoelectric generator ; Computer science ; Automotive engineering ; Machine learning ; Thermoelectric effect ; Composite material ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Energy Harvesting in Wireless Networks ; Innovative Energy Harvesting Technologies
Authors: Trushal Sardhara | Roland C. Aydin | Yong Li | Nicolas Piché | Raynald Gauvin | Christian J. Cyron | Martin Ritter
Source: Frontiers in Materials (article, 2022-02-28)
Field: Materials Science — Surfaces, Coatings and Films

Training Deep Neural Networks to Reconstruct Nanoporous Structures From FIB Tomography Images Using Synthetic Training Data Focused ion beam (FIB) tomography is a destructive technique used to collect three-dimensional (3D) structural information at a resolution of a few nanometers. For FIB tomography, a material sample is degraded by layer-wise milling. After each layer, the current surface is imaged by a scanning electron microscope (SEM), providing a consecutive series of cross-sections of the three-dimensional material sample. Especially for nanoporous materials, the reconstruction of the 3D microstructure of the material, from the information collected during FIB tomography, is impaired by the so-called shine-through effect . This effect prevents a unique mapping between voxel intensity values and material phase (e.g., solid or void). It often substantially reduces the accuracy of conventional methods for image segmentation. Here we demonstrate how machine learning can be used to tackle this problem. A bottleneck in doing so is the availability of sufficient training data. To overcome this problem, we present a novel approach to generate synthetic training data in the form of FIB-SEM images generated by Monte Carlo simulations. Based on this approach, we compare the performance of different machine learning architectures for segmenting FIB tomography data of nanoporous materials. We demonstrate that two-dimensional (2D) convolutional neural network (CNN) architectures processing a group of adjacent slices as input data as well as 3D CNN perform best and can enhance the segmentation performance significantly. Focused ion beam ; Tomography ; Convolutional neural network ; Artificial intelligence ; Materials science ; Artificial neural network ; Computer science ; Segmentation ; Voxel ; Pattern recognition (psychology) ; Optics ; Ion Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Kishalay Das | Bidisha Samanta | Pawan Goyal | Seung‐Cheol Lee | Satadeep Bhattacharjee | Niloy Ganguly
Source: npj Computational Materials (article, 2022-03-18)
Field: Materials Science — Materials Chemistry

CrysXPP: An explainable property predictor for crystalline materials Abstract We present a deep-learning framework, CrysXPP, to allow rapid and accurate prediction of electronic, magnetic, and elastic properties of a wide range of materials. CrysXPP lowers the need for large property tagged datasets by intelligently designing an autoencoder, CrysAE. The important structural and chemical properties captured by CrysAE from a large amount of available crystal graphs data helped in achieving low prediction errors. Moreover, we design a feature selector that helps to interpret the model’s prediction. Most notably, when given a small amount of experimental data, CrysXPP is consistently able to outperform conventional DFT. A detailed ablation study establishes the importance of different design steps. We release the large pre-trained model CrysAE. We believe by fine-tuning the model with a small amount of property-tagged data, researchers can achieve superior performance on various applications with a restricted data source. Property (philosophy) ; Autoencoder ; Computer science ; Range (aeronautics) ; Feature (linguistics) ; Deep learning ; Artificial intelligence ; Data mining ; Machine learning ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: An Chen | Zhilong Wang | Xu Zhang | Letian Chen | Hu Xu | Yanqiang Han | Junfei Cai | Zhen Zhou | Jinjin Li
Source: Chemistry of Materials (article, 2022-06-14)
Field: Materials Science — Materials Chemistry

Accelerated Mining of 2D Van der Waals Heterojunctions by Integrating Supervised and Unsupervised Learning The contact resistance of the metal–semiconductor interface at the source and drain increases as the injection of carriers decreases, degrading the device performance. Reducing the Schottky barrier height (SBH) at the metal–semiconductor interface is an effective way of reducing the contact resistance. Therefore, exploring Ohmic contact (no Schottky barrier) or low contact resistance two-dimensional (2D) semiconductor/metal heterojunctions is critical for developing high-performance electronic devices. We generate a comprehensive dataset from the periodic table consisting of 1092 potential 2D semiconductor/metal heterojunctions with good contact performances and demonstrate that the small interfacial dipole and the elimination of localized surface states are essential for designing advanced 2D metal–semiconductor systems with small SBHs. We use integrated supervised and unsupervised learning, as well as first-principles calculations, to screen 6 potential 2D van der Waals metal–semiconductor heterojunctions (BTe–NbSe2, Al2SO–Zn3C2, iAl2SO–Zn3C2, GaSe–NbS2, GaSe–NbSe2, and GeSe–VS2) with Ohmic contact and high tunneling probabilities from 1092 candidates. The proposed method takes less than 5 s to execute and is far superior to traditional first-principles calculations in both time and cost, demonstrating the superiority of using machine learning for screening materials and that unsupervised assisted algorithm can alleviate the problem of data scarcity to predict the behaviors of complex dynamical systems. Ohmic contact ; Heterojunction ; Semiconductor ; Schottky barrier ; Contact resistance ; van der Waals force ; Materials science ; Optoelectronics ; Dipole ; Quantum tunnelling ; Nanotechnology ; Engineering physics ; Computer science ; Physics ; Molecule ; Quantum mechanics 2D Materials and Applications ; MXene and MAX Phase Materials ; Machine Learning in Materials Science
Authors: Rania E. Hammam | Ahmed Solyman | Mohammed H. Alsharif | Peerapong Uthansakul | Mohanad A. Deif
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Biomaterials

Design of Biodegradable Mg Alloy for Abdominal Aortic Aneurysm Repair (AAAR) Using ANFIS Regression Model Abdominal aortic aneurysm (AAA) is among the most widespread and dangerous diseases that may cause death. Recently, Endovascular Aneurysm Repair outperformed open aortic surgery, since it is a safe and reliable technique where a stent graft system is placed within the aortic aneurysm. It was aimed to design an Mg biodegradable alloy with bio-friendly alloying elements that enhance the corrosion resistance and mechanical properties of the alloy for the design of stents for Abdominal Aortic Aneurysm (AAA) repair. Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed for the design of the Mg alloy and compared to other traditional machine learning regression models (Multiple Linear Regression (MLR) and Gradient Boosting (GB). The dataset utilized in this work consisted of 600 samples of Mg alloys that were collected from the mat web database and additional papers from Google Scholar. The results revealed the superior prediction capability of the ANFIS model since it attained maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> scores of 0.926, 0.958, and 0.988 for the prediction of UTS, YS, and Ductility respectively. Furthermore, the ANFIS model was capable of designing an Mg biodegradable alloy having a UTS, YS, and Ductility of 346.148 Mpa, 230.8 Mpa, and 15.4% respectively which are excellent mechanical properties satisfying vascular stents requirements The ANFIS model can be further applied to speed up the design of other alloys in the future for various medical applications, reducing the time, cost, and effort of large searching space. Adaptive neuro fuzzy inference system ; Abdominal aortic aneurysm ; Materials science ; Alloy ; Biomedical engineering ; Aneurysm ; Computer science ; Artificial intelligence ; Medicine ; Surgery ; Composite material ; Fuzzy logic ; Fuzzy control system Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Metal and Thin Film Mechanics
Authors: Anders Johansson | Yu Xie | Cameron J. Owen | Jin Soo | Lixin Sun | Jonathan Vandermause | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2022-04-26)
Field: Materials Science — Materials Chemistry

Micron-scale heterogeneous catalysis with Bayesian force fields from first principles and active learning Quantum-mechanically accurate reactive molecular dynamics (MD) at the scale of billions of atoms has been achieved for the heterogeneous catalytic system of H$_2$/Pt(111) using the FLARE Bayesian force field. This achievement provides accelerated time-to-solution from first principles, with Bayesian active learning enabling efficient and autonomous training of the machine learning model. The resulting model is then deployed in LAMMPS on GPUs using the Kokkos performance portability library. The Bayesian force field provides quantitative uncertainty of predictions on every atomic environment, critical for detecting configurations in large reactive simulations that are outside of the training set. Scaling benchmarks were performed using real-application MD of the H$_2$/Pt(111) heterogeneous catalysis on the Summit supercomputer, with simulations reaching 0.5 trillion atoms on 4556 GPU nodes. Software portability ; Supercomputer ; Force field (fiction) ; Bayesian probability ; Molecular dynamics ; Computer science ; Scale (ratio) ; Field (mathematics) ; Bayesian optimization ; Bayesian inference ; Set (abstract data type) ; Scaling ; Computational science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Parallel computing ; Materials science ; Computational chemistry ; Physics ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Quantum Computing Algorithms and Architecture