jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hossein Abedi | Ala Qattawi
Source: Research Square (preprint, 2023-08-28)
Field: Materials Science — Materials Chemistry

NiTiHfX Shape Memory Alloy Actuation Strain Prediction Abstract Data-driven techniques are used to predict the actuation strain (AS) of NiTiHfX shape memory alloy (SMA). A Machine Learning (ML) approach is used to overcome the high dimensional dependency of NiTiHfX AS on numerous factors, as well as the lack of fully known governing physics. Detailed data extraction on available experimental studies is performed to gather any related information about the actuation strain. The elemental composition, manufacturing approaches, thermal treatments, applied stress, and post-processing steps that are commonly used to process NiTiHfX and have an impact on the material AS are used as input parameters of the ML models. Since a broad data collection is performed the information for each input factor was sufficient for the use of the majority of the accessible information in the literature on NiTiHfX AS. Taking into account most of the regular NiTiHfX processing factors also enables the option of tuning additional characteristics of NiTiHfX in addition to the ASs. The work is unique as is the first to fully investigate the NiTiHfX AS prediction. To forecast the NiTiHfX ASs, a total of 901 data sets or 17119 data points were gathered, verified, and selected. Several machine-learning approaches were applied and joint to gather to guarantee robust modeling. The global model's overall determination factor (R 2 ) was 0.96, suggesting the viability of the proposed NN model. Such a model opens the possibility of intelligent material selection and processing to maximize the AS or shape memory effect of NiTiHf SMA. Shape-memory alloy ; Strain (injury) ; Alloy ; Materials science ; Composite material ; Medicine ; Anatomy Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science
Authors: Shyue Ping Ong | Ji Qi | Tsz Wai Ko | Brandon C. Wood | Tuan Anh Pham
Source: Research Square (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling Dimensionality reduction ; Stratified sampling ; Training (meteorology) ; Reduction (mathematics) ; Sampling (signal processing) ; Curse of dimensionality ; Artificial intelligence ; Computer science ; Machine learning ; Training set ; Statistics ; Mathematics ; Physics ; Meteorology ; Computer vision ; Geometry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nuclear Physics and Applications
Authors: Faranak Hatami | Mousa Moradi
Source: arXiv (Cornell University) (preprint, 2023-11-04)
Field: Materials Science — Materials Chemistry

Comparison of Different Machine Learning Approaches to Predict Viscosity of Tri-n-Butyl Phosphate Mixtures Using Experimental Data Tri-n-butyl phosphate (TBP) is a solvent that is commonly used in a variety of industries, including the nuclear and chemical industries, for its ability to dissolve and purify various inorganic acids and metals. It is often used in hydrometallurgical processes to separate and purify these substances. Machine learning models offer a promising alternative to traditional methods for predicting the viscosity of TBP mixtures. By training machine learning models on a dataset of viscosity measurements, it is possible to accurately predict the viscosity of TBP mixtures at different compositions, densities, and temperatures, which can save time and resources and reduce the risk of exposure to toxic solvents. This paper aimed at proposing Machine Learning (ML) techniques to automatically predict the viscosity of TBP mixtures using experimental data. For comparison peruses, we trained five different ML algorithms including Support Vector Regressor (SVR), Random Forest (RF), Logistic Regression (LR), Gradient Boosted Decision Trees (XGBoost), and Neural Network (NN). We collected a total of 511 measurements for TBP mixtures with temperature-based density, at different compositions, containing hexane, dodecane, cyclohexane, n-heptane, toluene, and ethylbenzene measured at temperatures of T= (288.15, 293.15, 298.15, 303.15, 308.15, 313.15, 318.15, 323.15, and 328.15) K. The results revealed that the NN model with 25 and 50 neurons in the hidden layers could achieve the best viscosity predictions for a system of TBP mixtures. The NN model outperformed other regular ML models in terms of Mean Square Error (MSE) of 0.157 % and adjusted R2 of 99.72 % on the test data set. This paper demonstrated that the NN model can be an appropriate option to accurately predict the viscosity of TBP + Ethylbenzene with a margin of deviation as low as 0.049 %. Viscosity ; Support vector machine ; Machine learning ; Artificial neural network ; Artificial intelligence ; Toluene ; Cyclohexane ; Mean squared error ; Solvent ; Heptane ; Algorithm ; Materials science ; Computer science ; Thermodynamics ; Chemistry ; Mathematics ; Statistics ; Organic chemistry ; Physics Thermal and Kinetic Analysis ; Extraction and Separation Processes ; Mineral Processing and Grinding
Authors: Haosheng Xu | Yadong Jiang | Huan Wang | Jing Wang
Source: arXiv (Cornell University) (preprint, 2023-06-25)
Field: Materials Science — Materials Chemistry

Discovering two-dimensional magnetic topological insulators by machine learning Topological materials with unconventional electronic properties have been investigated intensively for both fundamental and practical interests. Thousands of topological materials have been identified by symmetry-based analysis and ab initio calculations. However, the predicted magnetic topological insulators with genuine full band gaps are rare. Here we employ this database and supervisedly train neural networks to develop a heuristic chemical rule for electronic topology diagnosis. The learned rule is interpretable and diagnoses with a high accuracy whether a material is topological using only its chemical formula and Hubbard $U$ parameter. We next evaluate the model performance in several different regimes of materials. Finally, we integrate machine-learned rule with ab initio calculations to high-throughput screen for magnetic topological insulators in 2D material database. We discover 6 new classes (15 materials) of Chern insulators, among which 4 classes (7 materials) have full band gaps and may motivate for experimental observation. We anticipate the machine-learned rule here can be used as a guiding principle for inverse design and discovery of new topological materials. Topological insulator ; Topology (electrical circuits) ; Ab initio ; Heuristic ; Computer science ; Inverse ; Artificial intelligence ; Machine learning ; Physics ; Mathematics ; Condensed matter physics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Neural Networks and Applications ; Geochemistry and Geologic Mapping
Authors: Zoé Faure Beaulieu | Thomas C. Nicholas | John L. A. Gardner | Andrew L. Goodwin | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2023-05-09)
Field: Chemistry — Inorganic Chemistry

Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks Zeolitic imidazolate frameworks are widely thought of as being analogous to inorganic AB$_{2}$ phases. We test the validity of this assumption by comparing simplified and fully atomistic machine-learning models for local environments in ZIFs. Our work addresses the central question to what extent chemical information can be "coarse-grained" in hybrid framework materials. Zeolitic imidazolate framework ; Imidazolate ; Work (physics) ; Computer science ; Materials science ; Chemistry ; Metal-organic framework ; Adsorption ; Physics ; Thermodynamics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Rui Li | Tatiana Galochkina | Jean‐François Lemineur | Frédéric Kanoufi | Viacheslav Shkirskiy
Source: ChemRxiv (preprint, 2023-02-22)
Field: Materials Science — Materials Chemistry

Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy Operando wide-field optical microscopy imaging yields a wealth of information about the reactivity of metal interfaces, yet the data are often unstructured and challenging to process. In this study, we harness the power of unsupervised machine learning (ML) algorithms to analyze chemical reactivity images obtained dynamically by reflectivity microscopy in combination with ex situ scanning electron microscopy to identify and cluster the chemical reactivity of particles in Al alloy. The ML analysis uncovers three distinct clusters of reactivity from unlabeled datasets. A detailed examination of representative reactivity patterns confirms the chemical communication of generated OH- fluxes within particles, as supported by statistical analysis of size distribution and finite element modelling (FEM). The ML procedures also reveal statistically significant patterns of reactivity under dynamic conditions, such as pH acidification. The results align well with a numerical model of chemical communication, underscoring the synergy between data-driven ML and physics-driven FEM approaches. Reactivity (psychology) ; Finite element method ; Cluster (spacecraft) ; Microscopy ; Materials science ; Biological system ; Computer science ; Chemical species ; Optical microscope ; Nanotechnology ; Chemistry ; Scanning electron microscope ; Physics ; Optics ; Biology ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Gaurav Vishwakarma | Johannes Hachmann
Source: ChemRxiv (preprint, 2023-02-13)
Field: Materials Science — Materials Chemistry

Liquid Organic Hydrogen Carriers: High-throughput Screening of Homogeneous Catalysts In the times of an ever-increasing rate of global warming and rapidly depleting fossil fuels, renewable sources of energy are attracting vast attention and numerous efforts have been directed towards achieving a hydrogen-based economy over the past few years. However, the biggest technical challenge so far has been the development of materials and the required infrastructure for efficient storage and transportation of hydrogen. To this end, liquid organic hydrogen carriers (LOHCs) have been extensively studied as they provide a safer alternative to storing high-purity hydrogen using the existing fuel infrastructure. However, commercial applications of LOHCs are only feasible when expensive, noble metal catalysts are substituted with inexpensive but equally efficient alternatives. In this work, we employ our group's cyberinfrastructure for the data-driven discovery and design of novel catalysts for LOHCs. We screen a library of homogeneous Ir-based pincer catalysts for the dehydrogenation of perhydro-N-ethyl carbazole. We develop a computational protocol to evaluate these catalysts based on thermodynamic parameters calculated using Density Functional Theory. Next, we use this data to train machine learning models for predicting the Gibbs free energies of the reactions and analyze hidden structure-property relationships in these systems. Energy carrier ; Catalysis ; Hydrogen storage ; Hydrogen ; Computer science ; Dehydrogenation ; Density functional theory ; Process engineering ; Environmental science ; Chemistry ; Engineering ; Organic chemistry ; Computational chemistry Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Catalysis and Hydrodesulfurization Studies
Authors: Albert M Iskandarov
Source: Science and Technology of Advanced Materials Methods (article, 2023-07-28)
Field: Materials Science — Materials Chemistry

Strategy for building training set for neural-network potentials for ionic diffusion in solids: example for hydride-ion diffusion in LaHO Solid ionic conductors represent an important class of materials for many practical applications, such as fuel cells, batteries and hydrogen storage. Molecular dynamics (MD) modeling is a powerful tool to perform large-scale simulations to study ionic trajectories at the atomic level. However, since MD is critically dependent on the potential of interatomic interactions, it is of utter importance to have the potential that can successfully reproduce experimental aspects of ionic diffusion, such as conductivity and activation energy. In this paper, we present our strategy for building a training set for neural-network potentials (NNPs) that allow to attain a good agreement between MD and experimental results. To prove validity of the strategy, we developed NNP for LaHO, novel H− conductor and explicitly illustrated that the diffusion of the H− ions in LaHO is attributed to the defect formation and migration. Ionic bonding ; Diffusion ; Ionic conductivity ; Ion ; Conductor ; Hydride ; Chemical physics ; Artificial neural network ; Molecular dynamics ; Set (abstract data type) ; Statistical physics ; Scale (ratio) ; Atomic units ; Computer science ; Materials science ; Chemistry ; Nanotechnology ; Hydrogen ; Computational chemistry ; Thermodynamics ; Physics ; Physical chemistry ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear Physics and Applications
Authors: Juan Santiago Grassano | Jonathan A. Semelak | Ignacio Pickering | Roitberg Adrian | Mariano C. González Lebrero | Estrin Dario Ariel
Source: ChemRxiv (preprint, 2023-11-22)
Field: Materials Science — Materials Chemistry

Assessment of embedding schemes in a hybrid machine learning/classical potentials (ML/MM) scheme Machine Learning (ML) methods have reached high accuracy levels for the prediction of in vacuo molecular properties. However, the simulation of large systems through solely ML methods (like those based on neural network potentials) is still a challenge. In this context, one of the most promising frameworks for integrating ML schemes in the simulation of complex molecular systems are the so-called ML/MM methods. These multiscale approaches combine ML methods with classical forcefields (MM), in the same spirit as the succesful hybrid quantum mechanics-molecular mechanics methods (QM/MM). The key issue for such ML/MM methods is the adequate description of the coupling between the region of the system described by ML and the region described at the MM level. In the context of QM/MM schemes, the main ingredient of the interaction is electrostatic, and the state of the art is the so called electrostatic-embedding. In this study, we analyze the quality of simpler mechanical embedding-based approaches, specifically focusing on their application within a ML/MM framework utilizing atomic partial charges derived in vacuo. Taking as reference electrostatic embedding calculations performed at a QM(DFT)/MM level, we explore different atomic charges schemes, as well as a polarization correction computed using atomic polarizabilites. Our benchmark data set comprises a set of about 80k small organic structures from the ANI-1x database, solvated in water. The results suggest that the MBIS atomic charges yield the best agreement with the reference coupling energy. Remarkable enhancements are achieved by including a simple polarization correction. Embedding ; Computer science ; Benchmark (surveying) ; Electrostatics ; Context (archaeology) ; Set (abstract data type) ; Coupling (piping) ; Algorithm ; Statistical physics ; Physics ; Artificial intelligence ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Protein Structure and Dynamics ; Crystallography and molecular interactions
Authors: Aaron Liu | Myeongyeon Lee | Rahul Venkatesh | Jessica A. Bonsu | Ron Volkovinsky | J. Carson Meredith | Elsa Reichmanis | Martha A. Grover
Source: ChemRxiv (preprint, 2023-08-14)
Field: Materials Science — Materials Chemistry

Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors Polymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and parameter choices, many of which are sparsely reported. For example, the mobility of a polymer-based organic field-effect transistor (OFET) may vary by several orders of magnitude for a given polymer, as a plethora of parameters related to solution processing, interface design/surface treatment, thin-film deposition, post-processing, and measurement settings have a profound effect on the value of the final measurement. Incomplete contextual, experimental details hamper the availability of reusable data applicable for data-driven optimization, modeling (e.g., machine learning), and analysis of new organic devices. To curate organic device databases that contain reproducible and Findable, Accessible, Interoperable, and Reusable (FAIR) experimental data records, data ontologies that fully describe sample provenance and process history are required. However, standards for generating such process ontologies are not widely adopted for experimental materials domains. In this work, we design and implement an object-relational database for storing experimental records of OFETs. A data structure is generated by drawing on an international standard for batch process control (ISA-88) to facilitate the design. We then mobilize these representative data records, curated from literature and laboratory experiments, to enable data-driven learning of process-structure-property relationships. The work presented herein opens the door for the broader adoption of data management practices and design standards for both the organic electronics and the wider materials community. Computer science ; Ontology ; Electronics ; Organic electronics ; Process (computing) ; Transistor ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Analytical Chemistry and Sensors
Authors: Lucia Allara | Federica Bertolotti | Antonietta Guagliardi
Source: ChemRxiv (preprint, 2023-11-08)
Field: Materials Science — Materials Chemistry

A deep learning approach for quantum dots sizing from wide-angle X-ray scattering data Disclosing the full potential of functional nanomaterials requires the optimization of synthethic protocols and an effective size screening tool, aiming at efficiently triggering their size-dependent properties. Here we demonstrate the successful combination of a wide-angle X-ray total scattering approach with a deep learning classifier for directly sizing quantum dots in both colloidal and dry states. This work offers a compelling alternative to the lengthy process of deriving sizing curves from transmission electron microscopy coupled with spectroscopic measurements, especially in the ultra-small size regime, where traditional empirical functions exhibit larger discrepancies. The core of our algorithm is an all-convolutional neural network trained on Debye scattering equation X-ray simulations, incorporating atomistic models to capture structural and morphological features, and augmented with physics-informed perturbations to account for different predictable experimental conditions. The model performances are evaluated using both wide-angle X-ray total scattering simulations and experimental datasets collected on lead sulfide quantum dots, resulting in size classification accuracies surpassing 97%. With the developed deep learning size classifier, we overcome the need for calibration curves for quantum dots sizing and thanks to the unified modeling approach at the basis of the total scattering method implemented, we include simultaneously structural and microstructural aspects in the classification process, This algorithm can be complemented by incorporating input information from other experimental observations (e.g. small angle X-ray scattering data) and can be easily extended to other classes of nanocrystals, providing the nanoscience community with a powerful and broad tool to accelerate the development of novel functional (nano)materials. Sizing ; Scattering ; Quantum dot ; Computer science ; Small-angle scattering ; Small-angle X-ray scattering ; Deep learning ; Materials science ; Algorithm ; Computational physics ; Statistical physics ; Artificial intelligence ; Physics ; Nanotechnology ; Optics ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Iron oxide chemistry and applications
Authors: Xin Chen | Yucheng Ouyang | Zhenchuan Chen | Rongfen Lin | Xingyu Gao | Lifang Wang | Fang Li | Yin Liu | Honghui Shang | Haifeng Song
Source: arXiv (Cornell University) (preprint, 2023-10-12)
Field: Materials Science — Materials Chemistry

TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD. Scalability ; Computer science ; Computational science ; Supercomputer ; Tensor (intrinsic definition) ; Computation ; Interatomic potential ; Multi-core processor ; Formalism (music) ; Molecular dynamics ; Theoretical computer science ; Parallel computing ; Artificial intelligence ; Algorithm ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Advanced NMR Techniques and Applications
Authors: Lazar Novakovic | Ashkan Salamat | Keith V. Lawler
Source: arXiv (Cornell University) (preprint, 2023-01-25)
Field: Materials Science — Materials Chemistry

Machine learning using structural representations for discovery of high temperature superconductors The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local minima of relevance to investigate further, minimizing sample space. Utilizing machine learning methods can permit a deeper appreciation of correlations in higher order parameter space and be trained to behave as a predictive tool in the exploration of new materials. We have applied this approach in our search for new high temperature superconductors by incorporating models which can differentiate structural polymorphisms, in a pressure landscape, a critical component for understanding high temperature superconductivity. Our development of a representation for machine learning superconductivity with structural properties allows fast predictions of superconducting transition temperatures ($T_c$) providing a $r^2$ above 0.94. Superconductivity ; Maxima and minima ; Representation (politics) ; Relevance (law) ; Space (punctuation) ; Chemical space ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Statistical physics ; Condensed matter physics ; Mathematics ; Biology ; Bioinformatics ; Drug discovery Machine Learning in Materials Science ; X-ray Diffraction in Crystallography