jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Patrick Reiser | Manuel Konrad | Artem Fediai | Salvador León | Wolfgang Wenzel | Pascal Friederich
Source: Journal of Chemical Theory and Computation (preprint, 2021-05-04)
Field: Materials Science — Materials Chemistry

Analyzing Dynamical Disorder for Charge Transport in Organic Semiconductors via Machine Learning Organic semiconductors are indispensable for today's display technologies in the form of organic light-emitting diodes (OLEDs) and further optoelectronic applications. However, organic materials do not reach the same charge carrier mobility as inorganic semiconductors, limiting the efficiency of devices. To find or even design new organic semiconductors with higher charge carrier mobility, computational approaches, in particular multiscale models, are becoming increasingly important. However, such models are computationally very costly, especially when large systems and long timescales are required, which is the case to compute static and dynamic energy disorder, <i>i</i>.<i>e</i>., the dominant factor to determine charge transport. Here, we overcome this drawback by integrating machine learning models into multiscale simulations. This allows us to obtain unprecedented insight into relevant microscopic materials properties, in particular static and dynamic disorder contributions for a series of application-relevant molecules. We find that static disorder and thus the distribution of shallow traps are highly asymmetrical for many materials, impacting widely considered Gaussian disorder models. We furthermore analyze characteristic energy level fluctuation times and compare them to typical hopping rates to evaluate the importance of dynamic disorder for charge transport. We hope that our findings will significantly improve the accuracy of computational methods used to predict application-relevant materials properties of organic semiconductors and thus make these methods applicable for virtual materials design. Organic semiconductor ; Semiconductor ; Charge (physics) ; OLED ; Computer science ; Materials science ; Diode ; Charge carrier ; Semiconductor device ; Nanotechnology ; Optoelectronics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures
Authors: Ian Palmer | T. Nathan Mundhenk | Brian Gallagher | Yong Han
Source: arXiv (Cornell University) (preprint, 2021-11-05)
Field: Materials Science — Materials Chemistry

Explaining neural network predictions of material strength We recently developed a deep learning method that can determine the critical peak stress of a material by looking at scanning electron microscope (SEM) images of the material's crystals. However, it has been somewhat unclear what kind of image features the network is keying off of when it makes its prediction. It is common in computer vision to employ an explainable AI saliency map to tell one what parts of an image are important to the network's decision. One can usually deduce the important features by looking at these salient locations. However, SEM images of crystals are more abstract to the human observer than natural image photographs. As a result, it is not easy to tell what features are important at the locations which are most salient. To solve this, we developed a method that helps us map features from important locations in SEM images to non-abstract textures that are easier to interpret. Salient ; Artificial intelligence ; Artificial neural network ; Image (mathematics) ; Computer science ; Natural (archaeology) ; Observer (physics) ; Computer vision ; Pattern recognition (psychology) ; Natural materials ; Scanning electron microscope ; Geology ; Optics ; Materials science ; Physics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Advanced X-ray and CT Imaging
Authors: Qi Yuan | Filip Szczypiński | Kim E. Jelfs
Source: ChemRxiv (preprint, 2021-11-18)
Field: Materials Science — Materials Chemistry

Explainable Graph Neural Networks for Organic Cages The development of accurate and explicable machine learning models to predict the properties of topologically complex systems is a challenge in material science. Porous organic cages, a class of polycyclic molecular materials, have potential application in molecular separations, catalysis and encapsulation. For most applications of porous organic cages, having a permanent internal cavity in the absence of solvent, a property termed “shape persistency” is critical. Here, we report the development of Graph Neural Networks (GNNs) to predict the shape persistence of organic cages. Graph neural networks are a class of neural networks where the data, in our case that of organic cages, are represented by graphs. The performance of the GNN models was measured against a previously reported computational database of organic cages formed through a range of [4+6] reactions with a variety of reaction chemistries. The reported GNNs have an improved prediction accuracy and transferability compared to random forest predictions. Apart from the improvement in predictive power, we explored the explicability of the GNNs by computing the integrated gradient of the GNN input. The contribution of monomers and molecular fragments to the shape persistence of the organic cages could be quantitatively evaluated with integrated gradient. With the added explicability of the GNNs, it is possible not only to accurately predict the property of organic materials, but also to interpret the predictions of the deep learning models and provide structural insights to the discovery of future materials. Transferability ; Computer science ; Artificial neural network ; Organic molecules ; Artificial intelligence ; Graph ; Machine learning ; Biological system ; Biochemical engineering ; Chemistry ; Theoretical computer science ; Molecule ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Surface Chemistry and Catalysis
Authors: Melanie Vollmar
Source: IUCrJ (article, 2021-12-22)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of new materials with deep learning The use of deep learning and ideas from image recognition to accelerate data analysis and hence discovery of novel materials is described. A particular focus is given to the field of energy research. Deep learning ; Focus (optics) ; Field (mathematics) ; Energy (signal processing) ; Key (lock) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: J. Mangos | N. Birbilis
Source: arXiv (Cornell University) (preprint, 2021-05-31)
Field: Materials Science — Materials Chemistry

Aluminium Alloy Design and Discovery using Machine Learning The traditional design and development of metallic alloys has taken a hill-climbing approach to date, with incremental advances. Throughout the last century, aluminium (Al) alloy design has been essentially empirical and iterative, based on lessons learned from in service use and human experience. Incremental alloy development is costly, slow, and doesn't fully harness the data that exists in the field of Al-alloy metallurgy. In the present work, an attempt has been made to utilise a data science approach to develop a machine learning (ML) model for Al-alloy design. An objective-optimisation process has also been developed, to exploit the ML model, for user experience and practical application. A successful model was developed and presented herein, along with the open-access software. Alloy ; Aluminium ; Metallurgy ; Computer science ; Materials science ; Artificial intelligence Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Alain Tchagang | Ahmed H. Tewfik | Julio J. Valdés
Source: Research Square (preprint, 2021-02-23)
Field: Materials Science — Materials Chemistry

Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design Abstract Accumulation of molecular data obtained from quantum mechanics (QM) theories such as density functional theory (DFT QM ) make it possible for machine learning (ML) to accelerate the discovery of new molecules, drugs, and materials. Models that combine QM with ML (QM↔ML) have been very effective in delivering the precision of QM at the high speed of ML. In this study, we show that by integrating well-known signal processing (SP) techniques (i.e. short time Fourier transform, continuous wavelet analysis and Wigner-Ville distribution) in the QM↔ML pipeline, we obtain a powerful machinery (QM↔SP↔ML) that can be used for representation, visualization and forward design of molecules. More precisely, in this study, we show that the time-frequency- like representation of molecules encodes their structural, geometric, energetic, electronic and thermodynamic properties. This is demonstrated by using the new representation in the forward design loop as input to a deep convolutional neural networks trained on DFT QM calculations, which outputs the properties of the molecules. Tested on the QM9 dataset (composed of 133,855 molecules and 16 properties), the new QM↔SP↔ML model is able to predict the properties of molecules with a mean absolute error (MAE) below acceptable chemical accuracy (i.e. MAE &lt; 1 Kcal/mol for total energies and MAE &lt; 0.1 ev for orbital energies). Furthermore, the new approach performs similarly or better compared to other ML state-of-the-art techniques described in the literature. In all, in this study, we show that the new QM↔SP↔ML model represents a powerful technique for molecular forward design. All the codes and data generated and used in this study are available as supporting materials. The QM↔SP↔ML is also housed at the following website: https://github.com/TABeau/QM-SP-ML. Representation (politics) ; Computer science ; Signal processing ; Time–frequency representation ; SIGNAL (programming language) ; Artificial intelligence ; Speech recognition ; Digital signal processing ; Computer hardware ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Prathik R. Kaundinya | Kamal Choudhary | Surya R. Kalidindi
Source: Physical Review Materials (preprint, 2021-06-02)
Field: Materials Science — Materials Chemistry

Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds In this study, we present an approach (along with the needed computational strategies) for efficient and scalable feature engineering of the crystal structure in compounds of different chemical compositions. This approach utilizes a versatile and extensible framework for the quantification of a three-dimensional voxelized crystal structure in the form of 2-point spatial correlations of multiple atomic attributes and performs principal component analysis to extract the low-dimensional features that could be used to build surrogate models for material properties of interest. An application of the proposed feature engineering framework is demonstrated on a case study involving the prediction of the formation energies of crystalline compounds using two vastly different surrogate model building strategies; local Gaussian process regression and neural networks. Specifically, it is shown that the top 25 features (i.e., principal component scores) identified by the proposed framework serve as good regressors for the formation energy of the crystalline substance for both model building strategies. Principal component analysis ; Feature (linguistics) ; Artificial intelligence ; Scalability ; Component (thermodynamics) ; Kriging ; Computer science ; Surrogate model ; Feature engineering ; Artificial neural network ; Energy (signal processing) ; Crystal structure prediction ; Gaussian ; Machine learning ; Crystal (programming language) ; Process (computing) ; Crystal structure ; Deep learning ; Chemistry ; Mathematics ; Computational chemistry ; Crystallography ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Peter Bjørn Jørgensen | Arghya Bhowmik
Source: arXiv (Cornell University) (preprint, 2021-12-01)
Field: Materials Science — Materials Chemistry

Graph neural networks for fast electron density estimation of molecules, liquids, and solids Electron density $\rho(\vec{r})$ is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features in $\rho(\vec{r})$ distribution and modifications in $\rho(\vec{r})$ are often used to capture critical physicochemical phenomena in functional materials and molecules at the electronic scale. Methods providing access to $\rho(\vec{r})$ of complex disordered systems with little computational cost can be a game changer in the expedited exploration of materials phase space towards the inverse design of new materials with better functionalities. We present a machine learning framework for the prediction of $\rho(\vec{r})$. The model is based on equivariant graph neural networks and the electron density is predicted at special query point vertices that are part of the message passing graph, but only receive messages. The model is tested across multiple data sets of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and LixNiyMnzCo(1-y-z)O2 lithium ion battery cathodes (NMC). For QM9 molecules, the accuracy of the proposed model exceeds typical variability in $\rho(\vec{r})$ obtained from DFT done with different exchange-correlation functional and show beyond the state of the art accuracy. The accuracy is even better for the mixed oxide (NMC) and electrolyte (EC) datasets. The linear scaling model's capacity to probe thousands of points simultaneously permits calculation of $\rho(\vec{r})$ for large complex systems many orders of magnitude faster than DFT allowing screening of disordered functional materials. Density functional theory ; Ground state ; Ethylene oxide ; Scaling ; Electrolyte ; Molecule ; Materials science ; Topology (electrical circuits) ; Physics ; Statistical physics ; Computer science ; Chemistry ; Computational chemistry ; Mathematics ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Andreas Kaup | Niklas Lenzen | Okyay Altay
Source: PAMM (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Neural Network Parameter Identification Based Constitutive Modeling of Superelastic Shape Memory Alloys Abstract Superelastic shape memory alloys (SMAs) are two‐phase polycrystal materials with hysteretic energy dissipation and deformation recovery capabilities. This feature opens numerous application possibilities, particularly using SMA wires in structural vibration control. However, vibrations, such as wind and earthquake induced, may occur in high rate regimes with alternating amplitudes. Experiments show that the thermodynamically coupled SMA behavior is highly sensitive to strain rate and amplitude. Accordingly, the prediction accuracy of the constitutive models depends notably on their thermodynamic parameters, such as latent heat, heat transfer coefficient and specific heat. The identification of these parameters requires extensive experiments. To circumvent this challenge, this study proposes for SMA wires a machine learning based parameter identification procedure using artificial neural networks (ANNs). In this approach, a recently improved uniaxial macroscopic constitutive SMA model is utilized as a forward physics based model to compute all possible responses in an expected parameter space. With the generated data, a multilayer ANN is trained as a data based inverse model. The Latin hypercube sampling and the backpropagation learning algorithms are applied. After training, the data based model is able to identify suitable thermodynamic parameters from SMA stress responses. With the identified parameters, the constitutive model replicates the strain rate and amplitude dependent SMA response effects. The experimental results are matched by the model with high accuracy. SMA* ; Artificial neural network ; Shape-memory alloy ; Constitutive equation ; Amplitude ; Vibration ; Materials science ; Dissipation ; Computer science ; Control theory (sociology) ; Biological system ; Mechanics ; Artificial intelligence ; Structural engineering ; Algorithm ; Engineering ; Physics ; Acoustics ; Thermodynamics ; Finite element method Shape Memory Alloy Transformations ; Elasticity and Material Modeling ; Elasticity and Wave Propagation
Authors: Hadi Abroshan | Shaun Kwak | Yuling An | Christopher T. Brown | Mathew D. Halls
Source:  (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Generative machine learning for accelerated discovery of OLED materials Development and characterization of novel OLED materials by traditional computational approaches are challenging owing to the complex factors that simultaneously influence the device performance. In this work, we will provide an overview of generative OLED materials discovery using the latest deep neural network formalism, and show an illustrative example to design novel OLED hole-transport materials. The outcome of the work will demonstrate the value of systematic and fundamental understanding of structure-property correlations that can lead to rational design of smart OLEDs with higher efficiency. OLED ; Generative grammar ; Computer science ; Formalism (music) ; Artificial neural network ; Artificial intelligence ; Materials science ; Nanotechnology ; Layer (electronics) Machine Learning in Materials Science ; Green IT and Sustainability
Authors: Hongyu Chen | Shigeru Yamaguchi | Yuya Morita | Hiroyasu Nakao | Xiangning Zhai | Y. Shimizu | Harunobu Mitsunuma | Motomu Kanai
Source: ChemRxiv (preprint, 2021-05-12)
Field: Chemistry — Inorganic Chemistry

Data-Driven Catalyst Optimization for Stereodivergent Asymmetric Synthesis of α-Allyl Carboxylic Acids by Iridium/boron Hybrid Catalysis Asymmetric catalysis enabling divergent control of multiple stereocenters remains challenging in synthetic organic chemistry. While machine learning-based optimization of molecular catalysis is an emerging approach, data-driven catalyst design to achieve stereodivergent asymmetric synthesis producing multiple reaction outcomes, such as constitutional selectivity, diastereoselectivity, and enantioselectivity, is unprecedented. Here, we report the straightforward identification of asymmetric two-component iridium/boron hybrid catalyst systems for α-C-allylation of carboxylic acids. Structural optimization of the chiral ligands for iridium catalysts was driven by molecular field-based regression analysis with a dataset containing overall 32 molecular structures. The catalyst systems enabled selective access to all the possible isomers of chiral carboxylic acids bearing contiguous stereocenters. This stereodivergent asymmetric catalysis is applicable to late-stage structural modifications of drugs and their derivatives. Stereocenter ; Catalysis ; Combinatorial chemistry ; Enantioselective synthesis ; Chemistry ; Iridium ; Asymmetric hydrogenation ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Advanced Synthetic Organic Chemistry ; Machine Learning in Materials Science
Authors: M Sumanto | Muhamad Abdulkadir Martoprawiro | Atthar Luqman Ivansyah
Source: Journal of Physics Conference Series (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

The prediction of molecule atomization energy using neural network and extreme gradient boosting Abstract Machine Learning is an artificial intelligence system, where the system has the ability to learn automatically from experience without being explicitly programmed. The learning process from Machine Learning starts from observing the data and then looking at the pattern of the data. The main purpose of this process is to make computers learn automatically. In this study, we will use Machine Learning to predict molecular atomization energy. From various methods in Machine Learning, we use two methods namely Neural Network and Extreme Gradient Boosting. Both methods have several parameters that must be adjusted so that the predicted value of the atomization energy of the molecule has the lowest possible error. We are trying to find the right parameter values for both methods. For the neural network method, it is quite difficult to find the right parameter value because it takes a long time to train the model of the neural network to find out whether the model is good or bad, while for the Extreme Gradient Boosting method the time needed to train the model is shorter, so it is quite easy to find the right parameter values for the model. This study also looked at the effects of the modification on the dataset with the output transformation of normalization and standardization then removing molecules containing Br atoms and changing the entry in the Coulomb matrix to 0 if the distance between atoms in the molecule exceeds 2 angstrom. Artificial neural network ; Extreme learning machine ; Artificial intelligence ; Computer science ; Gradient boosting ; Boosting (machine learning) ; Machine learning ; Normalization (sociology) ; Energy (signal processing) ; Process (computing) ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Nasir Mehmood | Rashid Ahmad | Aqsa Gul | Anwar Zaman | G. Murtaza | Jamil Ahmad | F. Y. Khattak
Source: Computational Condensed Matter (article, 2021-10-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of lattice constants for the full-Heusler alloys by vector regression model and Artificial Neural Networks Lattice constant ; Ionic radius ; Artificial neural network ; WIEN2k ; Materials science ; Lattice (music) ; Density functional theory ; Statistical physics ; Regression analysis ; Thermodynamics ; Mathematics ; Computational chemistry ; Physics ; Chemistry ; Computer science ; Statistics ; Ion ; Artificial intelligence ; Quantum mechanics ; Local-density approximation Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Daniele Sirico | Giovanni Acampora | P. Maddalena | Felice Gesuele
Source: Conference on Lasers and Electro-Optics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Thickness identification of 2D materials by machine learning assisted optical microscopy We report a rapid and cost-effective method for the identification of the thickness of two-dimensional materials such as transition metal dichalcogenides. Our technique is based on the analysis of the optical contrast by means of machine learning algorithms and it is well suited for accurate characterization of 2D materials over large areas. Optical microscope ; Identification (biology) ; Characterization (materials science) ; Microscopy ; Computer science ; Materials science ; Contrast (vision) ; Artificial intelligence ; Nanotechnology ; Optics ; Composite material ; Physics ; Scanning electron microscope 2D Materials and Applications ; Nanoplatforms for cancer theranostics ; Quantum Dots Synthesis And Properties