jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akshay Krishna Sheshadri | Samarth Singh | B. A. Botre | Hari Narayan Bhargaw | S. A. Akbar | Poonam Jangid | S. A. R. Hasmi
Source: AIP conference proceedings (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

AI models for prediction of displacement and temperature in shape memory alloy (SMA) wire Shape Memory Alloys (SMAs) are a unique class of smart materials that have the ability to recover their shape on temperature stimuli. During this transformation, hysteresis and non-linear behavior can be observed and open-loop control design is inadequate for tracking control of these actuators. This is a major setback for the design and development of any SMA device. The main hurdle is that this nonlinearity cannot be modeled effectively even by 3rd degree differential equations. Additionally, the apparatus used for measurement of strain recovery during SMA actuation includes linear variable differential transducer (LVDT), which is rather bulky and expensive and does not let it fully utilize the potential of SMA applications in miniaturized devices. This research work presents a method to eliminate the bulky position sensor by introducing an Artificial Neural Networks (ANN) to compensate for the non-linearity. Various researchers have attempted to model the behavior of SMAs using ANN techniques but these models had a high RMS error. In this paper, a more complex neural network is developed to model SMA's behavior. This architecture models for (i) displacement prediction (ii) temperature prediction. The results of present research not only demonstrate the effectiveness of Neural Networks for prediction of displacement and temperature of the SMA but also show how the proposed architectures have a much lesser error as compared to earlier models and are much more effective at modeling SMAs. SMA* ; Shape-memory alloy ; Artificial neural network ; Actuator ; Displacement (psychology) ; Control theory (sociology) ; Nonlinear system ; Computer science ; Hysteresis ; Linearity ; Linear variable differential transformer ; Smart material ; Artificial intelligence ; Engineering ; Algorithm ; Materials science ; Electronic engineering ; Voltage ; Transformer ; Control (management) ; Physics Shape Memory Alloy Transformations
Authors: Carsten G. Staacke | Hendrik H. Heenen | Christoph Scheurer | Gábor Csányi | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2021-07-26)
Field: Materials Science — Materials Chemistry

On the role of long-range electrostatics in machine-learned interatomic potentials for complex battery materials Modeling complex energy materials such as solid-state electrolytes (SSEs) realistically at the atomistic level strains the capabilities of state-of-the-art theoretical approaches. On one hand, the system sizes and simulation time scales required are prohibitive for first-principles methods like density functional theory (DFT). On the other hand, parameterizations for empirical potentials are often not available and these potentials may ultimately lack the desired predictive accuracy. Fortunately, modern machine learning (ML) potentials are increasingly able to bridge this gap, promising first-principles accuracy at a much reduced computational cost. However, the local nature of these ML potentials typically means that long-range contributions arising, e.g., from electrostatic interactions are neglected. Clearly, such interactions can be large in polar materials like electrolytes, however. Herein, we investigate the effect that the locality assumption of ML potentials has on lithium mobility and defect formation energies in the SSE Li$_7$P$_3$S$_{11}$. We find that neglecting long-range electrostatics is unproblematic for the description of lithium transport in the isotropic bulk. In contrast (field-dependent) defect formation energies are only adequately captured by a hybrid potential combining ML and a physical model of electrostatic interactions. Broader implications for ML based modelling of energy materials are discussed. Electrostatics ; Range (aeronautics) ; Statistical physics ; Isotropy ; Battery (electricity) ; Electrolyte ; Density functional theory ; Computer science ; Field (mathematics) ; Physics ; Materials science ; Chemistry ; Computational chemistry ; Thermodynamics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science
Authors: Gael Donval | Calum Hand | James Hook | Emiko Dupont | Malena Sabaté Landman | Melina A. Freitag | Matthew J. Lennox | Tina Düren
Source: ChemRxiv (preprint, 2021-05-10)
Field: Materials Science — Materials Chemistry

Autonomous Exploration and Identification of High Performing Adsorbents using Active Learning MOFs and COFs are porous materials with a large variety of applications including gasstorage and separation. Synthesised in a modular fashion from distinct building blocks, anear in?nite number of structures can be constructed and the properties of the material canbe tailored for a speci?c application. While this modularity is a very attractive feature it alsoposes a challenge. Attempting to identify the best performing material(s) for a given appli-cation is experimentally intractable. Current research e?orts combine molecular simulationsand machine learning techniques to evaluate the simulated performance of hundreds of thou-sands of materials to identify top performing MOFs and COFs for a given application. Theseapproaches typically rely on moderated brute-force screening which is still resource-intensiveas typically between 70 - 100 % of the hundreds of thousands of materials must be simulatedto create a training set for the machine learning models used, restricting screening to rela-tively simple molecules. In this work we demonstrate our novel Bayesian mining approachto materials screening which allows 62 - 92 % of the top 100 porous materials for a range ofapplications to be readily identi?ed from large materials databases after only assessing lessthan one percent of all materials. This is a stark contrast to the 0 - 1 % achieved by conven-tional brute-force screening where porous materials are just chosen at random during a highthroughput screening. Through this accelerated virtual screening process, the identi?cation ofhigh performing materials can be used to more rapidly inform experimental e?orts and hencelead to an acceleration of the entire research and development pipeline of porous materials. Computer science ; Modular design ; Bayesian optimization ; Machine learning ; Modularity (biology) ; Identification (biology) ; Artificial intelligence Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Gongzhuang Peng | Tie Li | Xiang Zhai | Wenzheng Liu | Heming Zhang
Source: Advances in Complex Systems (article, 2021-10-09)
Field: Materials Science — Materials Chemistry

Knowledge-driven material design platform based on the whole-process simulation and modeling In order to realize the agility, collaboration and visualization of alloy material development process, a product development platform based on simulation and modeling technologies is established in this study. In this platform, the whole-process simulation module builds multi-level simulation models based on metallurgical mechanisms from the production line level, the thermo-mechanical coupling field level and the microstructure evolution level. The design knowledge management module represents the multi-source heterogeneous material design knowledge through ontology model, including customers’ requirement knowledge, material component knowledge, process design knowledge and quality inspection knowledge, and utilizes the case-based reasoning approach to reuse the knowledge. The data-driven modeling module applies machine learning algorithms to mine the relationships between product mechanical properties, material components, and process parameters from historical samples, and utilizes multi-objective optimization algorithms to find the optimal combination of process parameters. Application of the developed platform in actual steel mills shows that the proposed method helps to improve the efficiency of product design process. Reuse ; Computer science ; Process (computing) ; Ontology ; Component (thermodynamics) ; Product design ; Field (mathematics) ; Visualization ; Product (mathematics) ; Systems engineering ; Industrial engineering ; Data mining ; Engineering Machine Learning in Materials Science ; Metal Extraction and Bioleaching ; Metallurgical Processes and Thermodynamics
Authors: Chuanyi Yao | Zhenwei Zheng | Kuan Chen | Liangwan Wei | Liang Shen | Yinghua Lu | Enguo Fan
Source: Journal of Chemical Technology & Biotechnology (article, 2021-05-11)
Field: Chemistry — Spectroscopy

Using a machine learning model for the optimal design of simulated moving bed processes and its application to separate rebaudioside <scp>A</scp> and stevioside Abstract BACKGROUND Solving a mechanistic model of simulated moving bed (SMB) is extremely time consuming, therefore, it is inefficient for online control using the optimization method based on such a mechanistic model. To this end, the machine learning model can be regarded as a potential substitution to accelerate the optimization process. RESULTS Several machine learning algorithms were applied to predict the product purities under various operation conditions using two SMB processes as case studies: i.e., a sugar separation of rebaudioside A and stevioside, as well as an enantioseparation of 1,1′‐bi‐2‐naphthol racemate. The results indicate that the random forest (RF) model and the deep neural network (DNN) model provide a satisfactory accuracy with a mean absolute error (MAE) lower than 0.19% (RF) and 0.08% (DNN), respectively. During the optimization process to maximize the feed flowrate under specific purity requirements, among the two selected models, DNN model showed a better generalization ability than RF model and gave a feed flowrate 10% higher than the highest value in the training dataset, which was consistent with the result obtained by using the mechanistic model. The optimized operation conditions for sugar separation were verified experimentally and the achieved purities of rebaudioside A and stevioside were 99.2% and 98.8%, respectively. CONCLUSION The DNN model was successfully used to substitute the mechanistic model to reach a rapid optimization of SMB processes with an improved efficiency of 10 3 ~ 10 4 times. © 2021 Society of Chemical Industry (SCI). Stevioside ; Rebaudioside A ; Artificial neural network ; Generalization ; Sugar ; Process (computing) ; Computer science ; Artificial intelligence ; Biological system ; Mathematics ; Chemistry Analytical Chemistry and Chromatography ; Innovative Microfluidic and Catalytic Techniques Innovation ; Protein purification and stability
Authors: Jiace Sun | Lixue Cheng | Thomas F. Miller
Source: arXiv (Cornell University) (preprint, 2021-09-20)
Field: Materials Science — Materials Chemistry

Molecular Energy Learning Using Alternative Blackbox Matrix-Matrix Multiplication Algorithm for Exact Gaussian Process We present an application of the blackbox matrix-matrix multiplication (BBMM) algorithm to scale up the Gaussian Process (GP) training of molecular energies in the molecular-orbital based machine learning (MOB-ML) framework. An alternative implementation of BBMM (AltBBMM) is also proposed to train more efficiently (over four-fold speedup) with the same accuracy and transferability as the original BBMM implementation. The training of MOB-ML was limited to 220 molecules, and BBMM and AltBBMM scale the training of MOB-ML up by over 30 times to 6500 molecules (more than a million pair energies). The accuracy and transferability of both algorithms are examined on the benchmark datasets of organic molecules with 7 and 13 heavy atoms. These lower-scaling implementations of the GP preserve the state-of-the-art learning efficiency in the low-data regime while extending it to the large-data regime with better accuracy than other available machine learning works on molecular energies. Computer science ; Speedup ; Benchmark (surveying) ; Matrix (chemical analysis) ; Algorithm ; Matrix multiplication ; Transferability ; Process (computing) ; Gaussian ; Gaussian process ; Multiplication (music) ; Energy (signal processing) ; Scaling ; Computational science ; Artificial intelligence ; Parallel computing ; Machine learning ; Physics ; Mathematics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Rachid Ghritli | Yasunori Okano | Yuko Inatomi | Atsushi Sekimoto | S. Dost
Source: Journal of Crystal Growth (article, 2021-08-02)
Field: Materials Science — Materials Chemistry

Estimation of the diffusion coefficient of GaSb in InSb melt using Bayesian optimization and the ISS experimental results Diffusion ; Bayesian optimization ; Crystal growth ; Effective diffusion coefficient ; Materials science ; Crystal (programming language) ; Thermodynamics ; Chemistry ; Statistical physics ; Physics ; Computer science ; Mathematics ; Mathematical optimization Solidification and crystal growth phenomena ; nanoparticles nucleation surface interactions ; Advanced Semiconductor Detectors and Materials
Authors: Omair Rashed Abdulwareth Almanifi | Jee Kwan Ng | Anwar P. P. Abdul Majeed
Source: Mekatronika (article, 2021-07-31)
Field: Chemistry — Analytical Chemistry

The Classification of FTIR Plastic Bag Spectra via Label Spreading and Stacking Whereas plastics are a group of the most useful materials, widely used in all walks of life, the plastic waste that is produced daily poses a great threat towards wildlife and the planet as a whole. The use of biodegradable plastics is an important step in combating the plastic crisis. FTIR spectroscopy is a non-destructive method used for identifying different types of materials, however interpreting spectra produced by such spectrometers is both susceptible to human error, and time-consuming, not to mention that the industry suffers from a great of specialists, in the field of spectroscopy. Utilising machine learning as a method of filling the mentioned issue is suggested by this paper. Four pipelines were investigated, consisting of two machine learning algorithms, a stacked model that stacks the KNN, SVM and RF algorithms together, and Label spreading, as well as two different dimensionality reduction methods namely; SVD and UMAP. The pipelines studied seemed to show great predictivity at 100% classification accuracy acquired by the SVD-Stacked pipeline when data was sampled using an Agilent Cary 660 FTIR Spectrometer, and 99.18% by the same model when IDIR BP10 spectrometer was employed for sampling instead. The semi-supervised learning model (Label Spreading) seemed to achieve close enough accuracy at 99.82% in the case of the former dataset, and 97.54% for the latter, at a labelling rate of only 10% of the full datasets. Fourier transform infrared spectroscopy ; Spectrometer ; Computer science ; Artificial intelligence ; Pipeline (software) ; Support vector machine ; Pattern recognition (psychology) ; Machine learning ; Materials science ; Biological system ; Physics ; Optics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Fuzhan Rahmanian | Jackson Flowers | Dan Guevarra | Matthias H. Richter | Maximilian Fichtner | John M. Gregoire | Helge S. Stein
Source:  (preprint, 2021-10-13)
Field: Materials Science — Materials Chemistry

Enabling modular autonomous feedback-loops in materials science through hierarchical experimental laboratory automation and orchestration Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high-throughput characterization, automatic analysis, and machine learning. Within these MAPs, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein we present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. We demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software that are distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which we demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experiment orchestration, which to the best of our knowledge, is demonstrated for the first time herein. Orchestration ; Computer science ; Modular design ; Distributed computing ; Asynchronous communication ; Automation ; Software ; Scope (computer science) ; Implementation ; Interface (matter) ; Software engineering ; Computer architecture ; Human–computer interaction ; Systems engineering ; Operating system ; Programming language Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Rose K. Cersonsky | Benjamin A. Helfrecht | Edgar A. Engel | Sergei Kliavinek | Michele Ceriotti
Source: Machine Learning Science and Technology (preprint, 2021-05-06)
Field: Materials Science — Materials Chemistry

Improving sample and feature selection with principal covariates regression Abstract Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we focus on two popular subselection schemes applied to this end: CUR decomposition, derived from a low-rank approximation of the feature matrix, and farthest point sampling (FPS), which relies on the iterative identification of the most diverse samples and discriminating features. We modify these unsupervised approaches, incorporating a supervised component following the same spirit as the principal covariates (PCov) regression method. We show how this results in selections that perform better in supervised tasks, demonstrating with models of increasing complexity, from ridge regression to kernel ridge regression and finally feed-forward neural networks. We also present adjustments to minimise the impact of any subselection when performing unsupervised tasks. We demonstrate the significant improvements associated with PCov-CUR and PCov-FPS selections for applications to chemistry and materials science, typically reducing by a factor of two the number of features and samples required to achieve a given level of regression accuracy. Computer science ; Artificial intelligence ; Principal component regression ; Feature selection ; Regression ; Machine learning ; Covariate ; Kernel (algebra) ; Context (archaeology) ; Elastic net regularization ; Kernel regression ; Feature (linguistics) ; Supervised learning ; Regression analysis ; Principal component analysis ; Pattern recognition (psychology) ; Artificial neural network ; Statistics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Hong-Bin Ren | Lei Wang | Xi Dai
Source: Chinese Physics Letters (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System Kinetic energy (KE) functional is crucial to speed up density functional theory calculation. However, deriving it accurately through traditional physics reasoning is challenging. We develop a generally applicable KE functional estimator for a one-dimensional (1D) extended system using a machine learning method. Our end-to-end solution combines the dimensionality reduction method with the Gaussian process regression, and simple scaling method to adapt to various 1D lattices. In addition to reaching chemical accuracy in KE calculation, our estimator also performs well on KE functional derivative prediction. Integrating this machine learning KE functional into the current orbital free density functional theory scheme is able to provide us with expected ground state electron density. Density functional theory ; Estimator ; Computer science ; Curse of dimensionality ; Functional derivative ; Orbital-free density functional theory ; Dimensionality reduction ; Gaussian ; Gaussian process ; Scaling ; Kinetic energy ; Energy functional ; Energy (signal processing) ; Statistical physics ; Time-dependent density functional theory ; Applied mathematics ; Artificial intelligence ; Algorithm ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: L. S. R. Cavalcante | Luke L. Daemen | Nir Goldman | Adam J. Moulé
Source: Journal of Chemical Information and Modeling (article, 2021-08-27)
Field: Materials Science — Materials Chemistry

Davis Computational Spectroscopy Workflow—From Structure to Spectra We describe an automated workflow that connects a series of atomic simulation tools to investigate the relationship between atomic structure, lattice dynamics, materials properties, and inelastic neutron scattering (INS) spectra. Starting from the atomic simulation environment (ASE) as an interface, we demonstrate the use of a selection of calculators, including density functional theory (DFT) and density functional tight binding (DFTB), to optimize the structures and calculate interatomic force constants. We present the use of our workflow to compute the phonon frequencies and eigenvectors, which are required to accurately simulate the INS spectra in crystalline solids like diamond and graphite as well as molecular solids like rubrene. We have also implemented a machine-learning force field based on Chebyshev polynomials called the Chebyshev interaction model for efficient simulation (ChIMES) to improve the accuracy of the DFTB simulations. We then explore the transferability of our DFTB/ChIMES models by comparing simulations derived from different training sets. We show that DFTB/ChIMES demonstrates ∼100× reduction in computational expense while retaining most of the accuracy of DFT as well as yielding high accuracy for different materials outside of our training sets. The DFTB/ChIMES method within the workflow expands the possibilities to use simulations to accurately predict materials properties of increasingly complex structures that would be unfeasible with <i>ab initio</i> methods. Workflow ; Computer science ; Computational science ; Density functional theory ; Interatomic potential ; Chebyshev filter ; Ab initio ; Spectral line ; Statistical physics ; Molecular dynamics ; Algorithm ; Computational physics ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Electronic and Structural Properties of Oxides