jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siti Sarah Junian | Mohamad Zul Hilmey Makmud | Zuhair Jamain | Khairatun Najwa Mohd Amin | Jedol Dayou | Hazlee Azil Illias
Source: Energies (article, 2021-08-11)
Field: Materials Science — Materials Chemistry

Effect of Rice Husk Filler on the Structural and Dielectric Properties of Palm Oil as an Electrical Insulation Material This study uses natural fibre, which is moisture absorbent, as an innovative and economical filler for insulating oil. Rice husk (RH) is a natural fibre known to have water-absorbing properties and used as a supplementary cementitious material. This research utilises the water-absorbing properties of RH to improve the physicochemical and dielectric properties of insulating oil. RH was refined into a fine powder at a diameter of less than 63 µm. Palm oil (PO) was synthesised with RH at concentrations of 0 g/L, 0.01 g/L, and 0.1 g/L. The moisture-absorbent properties of RH were analysed by using Fourier-transform infrared (FTIR) spectroscopy. The particle size and distribution of RH in PO were also obtained using a scanning electron microscope (SEM) and a Zeta particle analyser. The breakdown voltage (BDV) strength of PO with RH was measured according to IEC 60156, and the dielectric frequency response was investigated in the range of 102–105 Hz. RH is proven to absorb moisture from PO, as the O–H band intensity at the 3350 cm−1 is increased from the experimental results. Therefore, the mean BDV and dielectric insulation properties of PO dispersed with RH also increased accordingly. Materials science ; Dielectric ; Husk ; Scanning electron microscope ; Composite material ; Fourier transform infrared spectroscopy ; Moisture ; Particle size ; Palm oil ; Zeta potential ; Chemistry ; Chemical engineering High voltage insulation and dielectric phenomena ; Advanced Cellulose Research Studies ; Power Transformer Diagnostics and Insulation
Authors: Michael Gastegger | Kristof T. Schütt | Klaus‐Robert Müller
Source: Chemical Science (preprint, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine learning of solvent effects on molecular spectra and reactions Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning has extended the boundaries of quantum chemistry by providing highly accurate and efficient surrogate models of electronic structure theory, which previously have been out of reach for conventional approaches. Those models have long been restricted to closed molecular systems without accounting for environmental influences, such as external electric and magnetic fields or solvent effects. Here, we introduce the deep neural network FieldSchNet for modeling the interaction of molecules with arbitrary external fields. FieldSchNet offers access to a wealth of molecular response properties, enabling it to simulate a wide range of molecular spectra, such as infrared, Raman and nuclear magnetic resonance. Beyond that, it is able to describe implicit and explicit molecular environments, operating as a polarizable continuum model for solvation or in a quantum mechanics/molecular mechanics setup. We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction. Based on these results, we use FieldSchNet to design an external environment capable of lowering the activation barrier of the rearrangement reaction significantly, demonstrating promising venues for inverse chemical design. Solvation ; Polarizable continuum model ; Molecular dynamics ; Polarizability ; Solvent effects ; Chemical physics ; Chemistry ; Quantum ; Molecule ; Computer science ; Computational chemistry ; Statistical physics ; Nanotechnology ; Physics ; Materials science ; Solvent ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Jianjun Hu | Yong Zhao | Li, Qin | Yuqi Song | Rongzhi Dong | Wenhui Yang | Edirisuriya M. Dilanga Siriwardane
Source: arXiv (Cornell University) (preprint, 2021-02-02)
Field: Materials Science — Materials Chemistry

AlphaCrystal: Contact map based crystal structure prediction using deep learning Crystal structure prediction is one of the major unsolved problems in materials science. Traditionally, this problem is formulated as a global optimization problem for which global search algorithms are combined with first principle free energy calculations to predict the ground-state crystal structure given only a material composition or a chemical system. These ab initio algorithms usually cannot exploit a large amount of implicit physicochemical rules or geometric constraints (deep knowledge) of atom configurations embodied in a large number of known crystal structures. Inspired by the deep learning enabled breakthrough in protein structure prediction, herein we propose AlphaCrystal, a crystal structure prediction algorithm that combines a deep residual neural network model that learns deep knowledge to guide predicting the atomic contact map of a target crystal material followed by reconstructing its 3D crystal structure using genetic algorithms. Based on the experiments of a selected set of benchmark crystal materials, we show that our AlphaCrystal algorithm can predict structures close to the ground truth structures. It can also speed up the crystal structure prediction process by predicting and exploiting the predicted contact map so that it has the potential to handle relatively large systems. We believe that our deep learning based ab initio crystal structure prediction method that learns from existing material structures can be used to scale up current crystal structure prediction practice. To our knowledge, AlphaCrystal is the first neural network based algorithm for crystal structure contact map prediction and the first method for directly reconstructing crystal structures from materials composition, which can be further optimized by DFT calculations. Crystal structure prediction ; Artificial intelligence ; Algorithm ; Computer science ; Crystal (programming language) ; Artificial neural network ; Benchmark (surveying) ; Ab initio ; Crystal structure ; Deep learning ; Machine learning ; Physics ; Chemistry ; Crystallography ; Geology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Esben Jannik Bjerrum | Tobias Rastemo | Ross Irwin | Christos Kannas | Samuel Genheden
Source: ChemRxiv (preprint, 2021-06-29)
Field: Materials Science — Materials Chemistry

PySMILESUtils – Enabling deep learning with theSMILES chemical language Recent years have seen a large interest in using the Simplified Molecular Input Line Entry System (SMILES) chemical language as input for deep learning architectures solving chemical tasks. Many successful applications have been demonstrated within de novo molecular design, quantitative structure-activity relationship modelling, forward reaction prediction and single-step retrosynthetic planning as examples. PySMILESUtils aims to enable these tasks by providing readyto- use and adaptable Python classes for tokenization, augmentation, dataset, and dataloader creation. Classes for handling datasets larger than memory and speeding up training by minimizing padding are also provided. The framework subclasses PyTorch dataset and dataloaders but should be adaptable for other deep learning frameworks. The project is open-sourced with a permissive license and made available at GitHub: https://github.com/MolecularAI/pysmilesutils Computer science ; Python (programming language) ; Artificial intelligence ; Deep learning ; MIT License ; Machine learning ; Programming language ; Natural language processing ; Software Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: K. Wijesinghe | J. Wanni | Natasha Kholgade Banerjee | Sean Banerjee | Ajit Achuthan
Source: Materials & Design (article, 2021-06-27)
Field: Materials Science — Materials Chemistry

Characterization of microscopic deformation of materials using deep learning algorithms Microstructure-informed design approach is set to revolutionize the design of metals and alloy components for aerospace applications. In this approach, a designer utilizes the influence of individual microstructural features on microscopic deformation to yield desirable macroscopic properties. Therefore, the development of advanced experimental capabilities that enable detailed characterization of microscopic deformation of material test specimens is critical to realize this paradigm shift in practice. However, extracting the complex characteristics of microscopic deformation hidden in raw image data is quite challenging. In this article, we propose an automated data extraction and analysis method based on instance segmentation and tracking of microstructural features using deep learning (DL) and image processing algorithms. The method consists of a trained mask Region-based Convolutional Neural Network (mask R-CNN) DL model combined with a regional instance segmentation approach for the instance segmentation of features, an intersection over union based multi-object tracking method to track segmented instances as they deform, and kinematics models to extract the material characteristics from the geometrical data of the deforming instances. The method is then validated by characterizing the microscopic deformation of an additively manufactured 316L stainless steel coupon specimen under quasi-static tensile loading. Our study presents a general framework for advancing deep learning algorithms to solve complex problems in the field of experimental mechanics. Characterization (materials science) ; Deformation (meteorology) ; Materials science ; Segmentation ; Artificial intelligence ; Intersection (aeronautics) ; Deep learning ; Convolutional neural network ; Tracking (education) ; Test data ; Kinematics ; Algorithm ; Computer science ; Mechanical engineering ; Composite material ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Image Processing Techniques and Applications ; Industrial Vision Systems and Defect Detection
Authors: Brian DeCost | Kamal Choudhary
Source:  (preprint, 2021-06-03)
Field: Materials Science — Materials Chemistry

Atomistic Line Graph Neural Network for Improved Materials Property Predictions Graph neural networks (GNN) have been shown to provide much improved performance for representing and modeling atomistic materials compared with descriptor-based machine-learning models. While most existing GNN models for atomistic predictions are based on atomic distance information, they do not explicitly incorporate bond angles, which are critical for distinguishing many atomic structures. Furthermore, many material properties are known to be sensitive to slight changes in bond angles. We develop Atomistic Line Graph Neural Network (ALIGNN) using a GNN architecture that performs message passing on both the interatomic bond graph and its line graph corresponding to bond angles. We demonstrate that angle information can be explicitly and efficiently included for materials to provide much improved performance. We train 44 models for predicting several solid-state material properties available in the JARVIS-DFT and materials-project databases. ALIGNN can outperform some of the previously known GNN models by up to 43.8 %. Computer science ; Artificial neural network ; Graph ; Bond graph ; Line (geometry) ; Property (philosophy) ; Algorithm ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Geometry ; Combinatorics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Logan Williams | Arpan Mukherjee | Aparajita Dasgupta | Krishna Rajan
Source: Journal of Materials Chemistry C (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Monitoring the role of site chemistry on the formation energy of perovskites <i>via</i> deep learning analysis of Hirshfeld surfaces Machine learning based on atomic Hirshfeld surfaces shows promise for material property prediction of diverse crystal systems. Materials science ; Property (philosophy) ; Crystal structure ; Crystal (programming language) ; Nanotechnology ; Crystallography ; Chemical physics ; Atomic force microscopy ; Engineering physics ; Chemistry ; Computer science ; Physics ; Epistemology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Rohit Modee | Siddhartha Laghuvarapu | U. Deva Priyakumar
Source: Journal of Computational Chemistry (article, 2021-12-06)
Field: Materials Science — Materials Chemistry

Benchmark study on deep neural network potentials for small organic molecules There has been tremendous advancement in machine learning (ML) applications in computational chemistry, particularly in neural network potentials (NNP). NNPs can approximate potential energy surface (PES) as a high dimensional function by learning from existing reference data, thereby circumventing the need to solve the electronic Schrödinger equation explicitly. As a result, ML accelerates chemical space exploration and property prediction compared to quantum mechanical methods. Novel ML methods have the potential to provide efficient means for predicting the properties of molecules. However, this potential has been limited by the lack of standard comparative evaluations. In this work, we compare four selected models, that is, ANI, PhysNet, SchNet, and BAND-NN, developed to represent the PES of small organic molecules. We evaluate these models for their accuracy and transferability on two different test sets (i) Small organic molecules of up to eight-heavy atoms on which ANI and SchNet achieve root mean square error (RMSE) of 0.55 and 0.60 kcal/mol, respectively. (ii) On random selection of molecules from the GDB-11 database with 10-heavy atoms, ANI achieves RMSE of 1.17 kcal/mol and SchNet achieves RMSE of 1.89 kcal/mol. We examine their ability to produce smooth meaningful surface by performing PES scans for bond stretch, angle bend, and dihedral rotations on relatively large molecules to assess their possible application in molecular dynamics simulations. We also evaluate their performance for yielding minimum energy structures via geometry optimization using various minimization algorithms. All these models were also able to accurately differentiate different isomers of the same empirical formula <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>C</mml:mi><mml:mn>10</mml:mn></mml:msub><mml:msub><mml:mi>H</mml:mi><mml:mn>20</mml:mn></mml:msub></mml:math> . ANI and PhysNet achieve an RMSE of 0.29 and 0.52 kcal/mol, respectively, on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>C</mml:mi><mml:mn>10</mml:mn></mml:msub><mml:msub><mml:mi>H</mml:mi><mml:mn>20</mml:mn></mml:msub></mml:math> isomers. Mean squared error ; Dihedral angle ; Benchmark (surveying) ; Artificial neural network ; Potential energy surface ; Energy minimization ; Computer science ; Molecule ; Quantum ; Minification ; Surface (topology) ; Algorithm ; Biological system ; Artificial intelligence ; Chemistry ; Computational chemistry ; Mathematics ; Physics ; Mathematical optimization ; Geometry ; Statistics ; Quantum mechanics ; Hydrogen bond Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Evan Walter Clark Spotte‐Smith | Samuel M. Blau | Xiaowei Xie | Hetal Patel | Mingjian Wen | Brandon C. Wood | Shyam Dwaraknath | Kristin A. Persson
Source: ChemRxiv (preprint, 2021-03-17)
Field: Materials Science — Materials Chemistry

Quantum Chemical Calculations of Lithium-Ion Battery Electrolyte and Interphase Species Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte(LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural,thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties. Electrolyte ; Lithium (medication) ; Battery (electricity) ; Interphase ; Ion ; Molecule ; Chemistry ; Reactivity (psychology) ; Chemical physics ; Density functional theory ; Lithium-ion battery ; Computational chemistry ; Physics ; Thermodynamics ; Physical chemistry ; Electrode ; Organic chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Charles D. Griego | Alex M. Maldonado | Lingyan Zhao | Barbaro Zulueta | Brian M. Gentry | Eli Lipsman | Tae Hoon Choi | John A. Keith
Source: The Journal of Physical Chemistry C (article, 2021-02-19)
Field: Materials Science — Materials Chemistry

Computationally Guided Searches for Efficient Catalysts through Chemical/Materials Space: Progress and Outlook Computational quantum chemistry promises to help guide the design of catalysts that are more sustainable and economical. This Feature Article gives a tutorial overview of how our group accounts for the thermodynamics and kinetics of chemical reactions in complex environments. We start with explanations of how to include environmental contributions when modeling homogeneous and heterogeneous catalytic processes. We also provide examples of schemes that use machine learning and alchemical perturbation density functional theory that eschew high computational costs while providing useful insights into chemical reaction mechanisms. With this toolbox of computational methods, we highlight progress in understanding how to reliably model renewable energy catalysis reaction mechanisms that occur in complex environments. Chemical space ; Toolbox ; Biochemical engineering ; Computer science ; Homogeneous ; Catalysis ; Chemistry ; Statistical physics ; Physics ; Drug discovery ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Liyuan Huang | Chen Ling
Source: Journal of Chemical Information and Modeling (article, 2021-08-26)
Field: Materials Science — Materials Chemistry

Leveraging Transfer Learning and Chemical Principles toward Interpretable Materials Properties Machine learning is emerging as a new paradigm to rationalize chemical properties for deepening our understanding of chemistry and providing instructive clues on better materials performance. While the complex architecture of machine learning contributes to unprecedented capability in this task, it prevents easy interpretation, leading to extensive criticisms on the lack of physical foundations for the black-box like models. Here, we demonstrate a transfer learning strategy that leverages fundamental principles of chemistry to offer adequate physical insights for the interpretation. Through interpreting the models for the formation energies of inorganic compounds, the proposed strategy revealed the deficiency of deep neural network in handling interelemental patterns and proved the more proper abstraction of recurrent neural network with attention mechanism, which led to predicting the elegant form of periodic table with high precision. The success demonstrates a new solution toward models with full transparency in materials informatics. Abstraction ; Computer science ; Artificial intelligence ; Interpretation (philosophy) ; Deep learning ; Transparency (behavior) ; Artificial neural network ; Machine learning ; Transfer of learning ; Task (project management) ; Black box ; Cognitive science ; Data science ; Epistemology ; Engineering ; Systems engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Kasra Asnaashari | Roman V. Krems
Source: Machine Learning Science and Technology (article, 2021-11-10)
Field: Materials Science — Materials Chemistry

Gradient domain machine learning with composite kernels: improving the accuracy of PES and force fields for large molecules Abstract The generalization accuracy of machine learning models of potential energy surfaces (PES) and force fields (FF) for large polyatomic molecules can be improved either by increasing the number of training points or by improving the models. In order to build accurate models based on expensive ab initio calculations, much of recent work has focused on the latter. In particular, it has been shown that gradient domain machine learning (GDML) models produce accurate results for high-dimensional molecular systems with a small number of ab initio calculations. The present work extends GDML to models with composite kernels built to maximize inference from a small number of molecular geometries. We illustrate that GDML models can be improved by increasing the complexity of underlying kernels through a greedy search algorithm using Bayesian information criterion as the model selection metric. We show that this requires including anisotropy into kernel functions and produces models with significantly smaller generalization errors. The results are presented for ethanol, uracil, malonaldehyde and aspirin. For aspirin, the model with composite kernels trained by forces at 1000 randomly sampled molecular geometries produces a global 57-dimensional PES with the mean absolute accuracy 0.177 kcal mol −1 (61.9 cm −1 ) and FFs with the mean absolute error 0.457 kcal mol −1 Å −1 . Ab initio ; Generalization ; Computer science ; Metric (unit) ; Kernel (algebra) ; Energy minimization ; Force field (fiction) ; Algorithm ; Artificial intelligence ; Machine learning ; Mathematics ; Computational chemistry ; Physics ; Chemistry ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics