jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anke Stoll | Peter Benner
Source: GAMM-Mitteilungen (preprint, 2021-03-01)
Field: Materials Science — Materials Chemistry

Machine learning for material characterization with an application for predicting mechanical properties Abstract Currently, the growth of material data from experiments and simulations is expanding beyond processable amounts. This makes the development of new data‐driven methods for the discovery of patterns among multiple lengthscales and time‐scales and structure‐property relationships essential. These data‐driven approaches show enormous promise within materials science. The following review covers machine learning (ML) applications for metallic material characterization. Many parameters associated with the processing and the structure of materials affect the properties and the performance of manufactured components. Thus, this study is an attempt to investigate the usefulness of ML methods for material property prediction. Material characteristics such as strength, toughness, hardness, brittleness, or ductility are relevant to categorize a material or component according to their quality. In industry, material tests like tensile tests, compression tests, or creep tests are often time consuming and expensive to perform. Therefore, the application of ML approaches is considered helpful for an easier generation of material property information. This study also gives an application of ML methods on small punch test (SPT) data for the determination of the property ultimate tensile strength for various materials. A strong correlation between SPT data and tensile test data was found which ultimately allows to replace more costly tests by simple and fast tests in combination with ML. Characterization (materials science) ; Ductility (Earth science) ; Brittleness ; Toughness ; Machine learning ; Ultimate tensile strength ; Property (philosophy) ; Material properties ; Test data ; Materials science ; Computer science ; Artificial intelligence ; Creep ; Mechanical engineering ; Engineering ; Composite material ; Nanotechnology Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Li Zhong | Dennis Hoppe | Naweiluo Zhou | Oleksandr Shcherbakov
Source:  (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Hybrid workflow of Simulation and Deep Learning on HPC: A Case Study for Material Behavior Determination Nowadays, machine learning (ML), especially deep learning(DL) methods, provide ever more real-life solutions. However, the lack of training data is often a crucial issue for these learning algorithms, the performance accuracy of which relies on the amount and the quality of the available data. This is particularly true when applying ML/DL based methods for specific areas e.g. material characteristics identification, as it requires huge cost of time and manual power getting observational data from real life. In the mean while, simulations on HPC have already been commonly used in computational science due to the fact that it has the ability of generating sufficient and noise free data, which can be used for training the ML/DL based models. However, in order to achieve accurate simulation results the input parameters usually have to be determined and validated by a large number of tests. Furthermore, the evaluation and validation of such input parameters for the simulation often require a deep understanding of the domain specific knowledge, software and programming skills, which can in turn be solved by ML/DL based methods. In this paper, a novel hybrid workflow combining a multi-task neural network and the simulation on high performance computers(HPC) is proposed, which can address the problem of data sparsity and reduce the demand for expertise, resources, and time in determining the validated parameters for simulation. This work is demonstrated through experiments on determination of material behaviors, and the results prove a promising performance (MSE = 0.0386) through this workflow. Workflow ; Computer science ; Artificial neural network ; Machine learning ; Deep learning ; Artificial intelligence ; Task (project management) ; Domain (mathematical analysis) ; Software ; Noise (video) ; Computer engineering ; Database ; Systems engineering Machine Learning in Materials Science ; Image Processing and 3D Reconstruction ; Model Reduction and Neural Networks
Authors: Xin Bai | Xin Guo | Linjun Wang
Source: ChemRxiv (preprint, 2021-09-13)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach to Calculate Electronic Couplings between Quasi-Diabatic Molecular Orbitals: The Case of DNA Diabatization of one-electron states in flexible molecular aggregates is a great challenge due to the presence of surface crossings between molecular orbital (MO) levels and the complex interaction between MOs of neighboring molecules. In this work, we present an efficient machine learning approach to calculate electronic couplings between quasi-diabatic MOs without the need of nonadiabatic coupling calculations. Using MOs of rigid molecules as references, the MOs that can be directly regarded to be quasi-diabatic in molecular dynamics are selected out, state tracked, and phase corrected. On the basis of this information, artificial neural networks are trained to characterize the structure-dependent onsite energies of quasi-diabatic MOs and the inter-molecular electronic couplings. A representative sequence of DNA is systematically studied as an illustration. Smooth time evolution of electronic couplings in all base pairs is obtained with quasi-diabatic MOs. Especially, our method can calculate electronic couplings between different quasi-diabatic MOs independently, and thus possesses unique advantages in many applications. Diabatic ; Molecular orbital ; Electronic structure ; Coupling (piping) ; Molecular dynamics ; Atomic orbital ; Molecule ; Work (physics) ; Chemistry ; Physics ; Electron ; Chemical physics ; Statistical physics ; Molecular physics ; Computational chemistry ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Spectroscopy and Quantum Chemical Studies
Authors: Johannes Allotey | Keith T. Butler | Jeyan Thiyagalingam
Source: The Journal of Chemical Physics (preprint, 2021-10-12)
Field: Materials Science — Materials Chemistry

Entropy-based active learning of graph neural network surrogate models for materials properties Graph neural networks trained on experimental or calculated data are becoming an increasingly important tool in computational materials science. Networks once trained are able to make highly accurate predictions at a fraction of the cost of experiments or first-principles calculations of comparable accuracy. However, these networks typically rely on large databases of labeled experiments to train the model. In scenarios where data are scarce or expensive to obtain, this can be prohibitive. By building a neural network that provides confidence on the predicted properties, we are able to develop an active learning scheme that can reduce the amount of labeled data required by identifying the areas of chemical space where the model is most uncertain. We present a scheme for coupling a graph neural network with a Gaussian process to featurize solid-state materials and predict properties including a measure of confidence in the prediction. We then demonstrate that this scheme can be used in an active learning context to speed up the training of the model by selecting the optimal next experiment for obtaining a data label. Our active learning scheme can double the rate at which the performance of the model on a test dataset improves with additional data compared to choosing the next sample at random. This type of uncertainty quantification and active learning has the potential to open up new areas of materials science, where data are scarce and expensive to obtain, to the transformative power of graph neural networks. Artificial neural network ; Computer science ; Machine learning ; Artificial intelligence ; Gaussian process ; Graph ; Gaussian ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Sanket Kadulkar | Michael Howard | Thomas M. Truskett | Venkat Ganesan
Source: ChemRxiv (preprint, 2021-04-15)
Field: Materials Science — Materials Chemistry

Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation transport parameterized using a multiscale modeling strategy. We implement deep learning approaches to quantitatively link the diffusivity of cations to the spatial arrangement of the nanoparticles. We then integrate the trained CNN model with a topology optimization algorithm for accelerated discovery of nanoparticle morphologies that exhibit optimal cation diffusivities at a specified nanoparticle loading, and we investigate the ability of the CNN model to quantitatively account for the influence of interparticle spatial correlations on cation diffusivity. Finally, using data-driven approaches, we explore how simple descriptors of nanoparticle morphology correlate with cation diffusivity, thus providing a physical rationale for the observed optimal microstructures. The results of this study highlight the capability of CNNs to serve as surrogate models for structure--property relationships in composites with monodisperse spherical particles, which can in turn be used with inverse methods to discover morphologies that produce optimal target properties. Convolutional neural network ; Thermal diffusivity ; Nanoparticle ; Multiscale modeling ; Materials science ; Electrolyte ; Computer science ; Kinetic Monte Carlo ; Biological system ; Inverse ; Artificial neural network ; Monte Carlo method ; Nanotechnology ; Artificial intelligence ; Chemistry ; Physics ; Thermodynamics ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Electrostatics and Colloid Interactions
Authors: Yuxiang Chen | Chuanlei Liu | Yang An | Yue Lou | Yang Zhao | Cheng Qian | Hao Jiang | Kongguo Wu | Xianghui Zhang | Hui Sun | Di Wu | Boxiong Shen | Fahai Cao
Source: ChemRxiv (preprint, 2021-11-25)
Field: Chemistry — Analytical Chemistry

Intelligent Molecular Identification for High Performance Organosulfide Capture Using Active Machine Learning Algorithm Machine learning and computer-aided approaches significantly accelerate molecular design and discovery in scientific and industrial fields increasingly relying on data science for efficiency. The typical method used is supervised learning which needs huge datasets. Semi-supervised machine learning approaches are effective to train unlabeled data with improved modeling performance, whereas they are limited by the accumulation of prediction errors. Here, to screen solvents for removal of methyl mercaptan, a type of organosulfur impurities in natural gas, we constructed a computational framework by integrating molecular similarity search and active learning methods, namely, molecular active selection machine learning (MASML). This new model framework identifies the optimal molecules set by molecular similarity search and iterative addition to the training dataset. Among all 126,068 compounds in the initial dataset, 3 molecules were identified to be promising for methyl mercaptan (MeSH) capture, including benzylamine (BZA), p-methoxybenzylamine (PZM), and N,N-diethyltrimethylenediamine (DEAPA). Further experiments confirmed the effectiveness of our modeling framework in efficient molecular design and identification for capturing methyl mercaptan, in which DEAPA presents a Henry's law constant 89.4% lower than that of methyl diethanolamine (MDEA). Machine learning ; Artificial intelligence ; Computer science ; Transferability ; Identification (biology) ; Active learning (machine learning) ; Similarity (geometry) ; Molecular descriptor ; Algorithm ; Chemistry ; Quantitative structure–activity relationship Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Sajjad Husain Mir
Source: ECS Journal of Solid State Science and Technology (article, 2021-02-24)
Field: Materials Science — Materials Chemistry

Perspective—Accelerated Discovery of Organic-Inorganic Hybrid Materials via Machine Learning Hybrid organic-inorganic nanomaterials have ushered new and multifunctional applications in the fields but not limited to, Internet of Things (IoT), microelectronics, optical materials, housing, environment, transport, health and diagnosis, energy, and energy storage. However, fast discovery of organic-inorganic nanomaterials has an inherent challenge, because the conventional trial-and-error strategies are incompetent when millions of potential materials are processed. Machine learning (ML) aims to expedite screening of the hybrid materials based on the end applications. Therefore, employing machine-learning methods will support future experiments in material discovery in such a way that there are fewer chances of error and misinterpretations. Microelectronics ; Nanomaterials ; Nanotechnology ; Materials science ; Internet of Things ; Hybrid material ; Computer science ; Process engineering ; Systems engineering ; Engineering ; Embedded system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Prathik R. Kaundinya | Kamal Choudhary | Surya R. Kalidindi
Source: arXiv (Cornell University) (preprint, 2021-05-24)
Field: Materials Science — Materials Chemistry

Machine learning approaches for feature engineering of the crystal\n structure: Application to the prediction of the formation energy of cubic\n compounds In this study, we present a novel approach along with the needed\ncomputational strategies for efficient and scalable feature engineering of the\ncrystal structure in compounds of different chemical compositions. This\napproach utilizes a versatile and extensible framework for the quantification\nof a three-dimensional (3-D) voxelized crystal structure in the form of 2-point\nspatial correlations of multiple atomic attributes and performs principal\ncomponent analysis to extract the low-dimensional features that could be used\nto build surrogate models for material properties of interest. An application\nof the proposed feature engineering framework is demonstrated on a case study\ninvolving the prediction of the formation energies of crystalline compounds\nusing two vastly different surrogate model building strategies - local Gaussian\nprocess regression and neural networks. Specifically, it is shown that the top\n25 features (i.e., principal component scores) identified by the proposed\nframework serve as good regressors for the formation energy of the crystalline\nsubstance for both model building strategies.\n Principal component analysis ; Feature (linguistics) ; Computer science ; Kriging ; Artificial intelligence ; Component (thermodynamics) ; Scalability ; Surrogate model ; Crystal structure prediction ; Artificial neural network ; Energy (signal processing) ; Gaussian ; Crystal (programming language) ; Feature engineering ; Machine learning ; Process (computing) ; Gaussian process ; Crystal structure ; Pattern recognition (psychology) ; Algorithm ; Deep learning ; Chemistry ; Mathematics ; Computational chemistry ; Crystallography ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Weilin Guan | Hasitha Jeewana Hewakuruppu | Edwin A. Peraza Hernandez
Source: ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems (article, 2021-09-14)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Modeling and Design Optimization of Hybrid Shape Memory Alloy Axial Actuators Abstract Shape memory alloys (SMAs) are materials that can generate and recover moderate inelastic strains through temperature modulation. This characteristic enables SMAs to function as lightweight and compact thermomechanical actuators. The synthesis of SMA actuators for systems with specific requirements on their actuation path (displacement stroke vs. temperature) currently relies on complex and expensive material processing and characterization. This paper first presents a geometric approach for synthesizing novel SMA axial actuators, termed as hybrid SMA actuators, whose dimensions (length and cross-sectional area) and material distribution are modulated to approximate a target actuation path. Through the combination of multiple SMA wire sections in series, the hybrid SMA actuators can exhibit actuation paths not achievable by using single monolithic SMA wires. The paper then presents a machine learning-assisted framework for the surrogate modeling of hybrid SMA actuators. This approach allows for the prediction of their actuation path without the use of structural simulations leveraging numerical implementations of constitutive models, allowing for simplified and computationally efficient modeling and circumventing convergence issues. A surrogate model consisting of an ensemble of binary decision trees is trained using data obtained via a design of experiments performed using structural simulations. A validation test using 5000 design samples for hybrid SMA actuators with two sections demonstrates R2 values of 0.99983 and 0.99979 for the actuation displacement during heating and cooling, respectively. The evaluation time for the validation samples using the trained surrogate model is less than 8 minutes, while the evaluation time using structural simulations is 59 minutes. Finally, a surrogate-based optimization approach is demonstrated through the synthesis of hybrid SMA actuators capable of exhibiting prescribed displacement vs. temperature target actuation paths. SMA* ; Shape-memory alloy ; Actuator ; Surrogate model ; Computer science ; Displacement (psychology) ; Smart material ; Path (computing) ; Convergence (economics) ; Control theory (sociology) ; Mechanical engineering ; Materials science ; Artificial intelligence ; Algorithm ; Engineering ; Machine learning Shape Memory Alloy Transformations ; Structural Engineering and Vibration Analysis ; Innovations in Concrete and Construction Materials
Authors: Eshan Joshi | Samuel O. Somuyiwa | Hossein Z. Jooya
Source: arXiv (Cornell University) (preprint, 2021-08-22)
Field: Materials Science — Materials Chemistry

Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations Tackling molecular optimization problems using conventional computational methods is challenging, because the determination of the optimized configuration is known to be an NP-hard problem. Recently, there has been increasing interest in applying different deep-learning techniques to benchmark molecular optimization tasks. In this work, we implement a graph-convolutional method to classify molecular structures using the equilibrium and non-equilibrium configurations provided in the QM7-X data set. Atomic forces are encoded in graph vertices and the substantial suppression in the total force magnitude on the atoms in the optimized structure is learned for the graph classification task. We demonstrate the results using two different graph pooling layers and compare their respective performances. Pooling ; Computer science ; Graph ; Benchmark (surveying) ; Molecular graph ; Theoretical computer science ; Algorithm ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ruben Staub | Stephan N. Steinmann
Source: ChemRxiv (preprint, 2021-11-17)
Field: Materials Science — Materials Chemistry

Replacing Chemical Intuition by Machine Learning: a Mixed Design of Experiments - Reinforcement Learning Approach to the Construction of Training Sets for Model Hamiltonians Model Hamiltonians based on the so-called cluster expansion (CE), which consist of a linear fit of parameters corresponding to geometric patterns, provide an efficient and rigorous means to quickly evaluate the energy of diverse arrangements of adsorbate mixtures on reactive surfaces as typically relevant for heterogeneous catalysis. However, establishing the model Hamiltonian is a tedious task, requiring the construction and optimization of many geometries. Today, most of these geometries are constructed by hand, based on chemical intuition or random choices. Hence, the quality of the training set is unlikely to be optimal and its construction is not reproducible. Herein, we propose a reformulation of the construction of the training set as a strategy-based game, aiming at an efficient exploration of the relevant patterns constituting the model Hamiltonian. Based on this reformulation, we exploit a typical active learning solution for machine-learning such a strategy game: an upper confidence tree (UCT) based framework. However, in contrast to standard games, evaluating the true score is computationally expensive, as it requires a costly geometry optimization. Hence, we augment the UCT with a pre-exploration step inspired by the variance-based Design of Experiments (DoE) methods. This novel mixed UCT+DoE framework allows to automatically construct a well adapted training set, minimizing computational cost and user-intervention. As a proof of principle, we apply our UCT+DoE approach on the CO oxidation reaction on Pd(111), for which a relevant model Hamiltonian has been established previously. The results demonstrate the effectiveness of the custom built UCT and its significant benefits on a DoE-based approach. Computer science ; Reinforcement learning ; Intuition ; Machine learning ; Exploit ; Artificial intelligence ; Mathematical optimization ; Hamiltonian (control theory) ; Proof of concept ; Mathematics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Topic Modeling