jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Oliver Mey | Arash Rahimi‐Iman
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2021-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning‐Based Optimization of Chiral Photonic Nanostructures: Evolution‐ and Neural Network‐Based Designs Chiral photonics opens new pathways to manipulate light–matter interactions and tailor the optical response of metasurfaces and ‐materials by nanostructuring nontrivial patterns. Chirality of matter, such as that of molecules, and light, which in the simplest case is given by the handedness of circular polarization, have attracted much attention for applications in chemistry, nanophotonics and optical information processing. The design of chiral photonic structures using two machine learning methods, the evolutionary algorithm, and neural network approach, for rapid and efficient optimization of optical properties for dielectric metasurfaces, is reported. The design recipes obtained for visible light in the range of transition‐metal dichalcogenide exciton resonances show a frequency‐dependent modification in the reflected light's degree of circular polarization, that is represented by the difference between left‐ and right‐circularly polarized intensity. Our results suggest the facile fabrication and characterization of optical nanopatterned reflectors for chirality‐sensitive light–matter coupling scenarios employing tungsten disulfide as possible active material with features such as valley Hall effect and optical valley coherence. Nanophotonics ; Photonics ; Circular polarization ; Chirality (physics) ; Nanostructure ; Optical phenomena ; Dielectric ; Materials science ; Optoelectronics ; Polarization (electrochemistry) ; Nanotechnology ; Optics ; Physics ; Chemistry Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Plasmonic and Surface Plasmon Research
Authors: Bei Wu | Zhanlei Hao | Jinhui Chen | Qiaoliang Bao | Yineng Liu | Huanyang Chen
Source: Journal of Electronic Science and Technology (article, 2021-11-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Total transmission from deep learning designs Total transmission plays an important role in efficiency improvement and wavefront control, and has made great progress in many applications, such as the optical film and signal transmission. Therefore, many traditional physical methods represented by transformation optics have been studied to achieve total transmission. However, these methods have strict limitations on the size of the photonic structure, and the calculation is complex. Here, we exploit deep learning to achieve this goal. In deep learning, the data-driven prediction and design are carried out by artificial neural networks (ANNs), which provide a convenient architecture for large dataset problems. By taking the transmission characteristic of the multi-layer stacks as an example, we demonstrate how optical materials can be designed by using ANNs. The trained network directly establishes the mapping from optical materials to transmission spectra, and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space. Our work paves the way for the optical material design with special properties based on deep learning. Transmission (telecommunications) ; Wavefront ; Computer science ; Deep learning ; Artificial neural network ; Photonics ; NASA Deep Space Network ; Data transmission ; Transformation (genetics) ; Artificial intelligence ; Electronic engineering ; Telecommunications ; Optics ; Engineering ; Physics ; Computer hardware Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Photonic and Optical Devices
Authors: Vipul Mann | Venkat Venkatasubramanian
Source: ChemRxiv (preprint, 2021-04-15)
Field: Materials Science — Materials Chemistry

Retrosynthesis Prediction using Grammar-based Neural Machine Translation: An Information-Theoretic Approach Retrosynthetic prediction is one of the main challenges in chemical synthesis that requires identifying reaction pathways and precursor molecules for synthesizing a target molecule. This requires a search over the space of plausible chemical reactions that often results in complex, multi-step, branched synthesis trees for even moderately complex organic reactions. Here, we propose an approach that performs single-step retrosynthesis prediction using SMILES grammar-based representations in a neural machine translation framework. Information-theoretic analyses of such grammar-representations reveal that they are both superior and well-suited for machine learning tasks due to their underlying redundancy and high information capacity compared to purely character-based representations. We report the top-1 prediction accuracy of 43.8% (top-5 measure of 61.4%) and syntactic validity of 95.6% (top-5 measure of 91.6%) on a standard reaction dataset. Comparing our model's performance with previous work that used purely character-based SMILES representations demonstrate improved accuracy and reduced grammatically invalid predictions. Retrosynthetic analysis ; Computer science ; Artificial intelligence ; Machine translation ; Grammar ; Redundancy (engineering) ; Character (mathematics) ; Rule-based machine translation ; Natural language processing ; Measure (data warehouse) ; Machine learning ; Translation (biology) ; Data mining ; Mathematics ; Chemistry ; Linguistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Rishikesh Magar | Yuyang Wang | Cooper Lorsung | Liang Chen | Hariharan Ramasubramanian | Peiyuan Li | Amir Barati Farimani
Source: arXiv (Cornell University) (preprint, 2021-11-29)
Field: Materials Science — Materials Chemistry

AugLiChem: Data Augmentation Library of Chemical Structures for Machine\n Learning Machine learning (ML) has demonstrated the promise for accurate and efficient\nproperty prediction of molecules and crystalline materials. To develop highly\naccurate ML models for chemical structure property prediction, datasets with\nsufficient samples are required. However, obtaining clean and sufficient data\nof chemical properties can be expensive and time-consuming, which greatly\nlimits the performance of ML models. Inspired by the success of data\naugmentations in computer vision and natural language processing, we developed\nAugLiChem: the data augmentation library for chemical structures. Augmentation\nmethods for both crystalline systems and molecules are introduced, which can be\nutilized for fingerprint-based ML models and Graph Neural Networks(GNNs). We\nshow that using our augmentation strategies significantly improves the\nperformance of ML models, especially when using GNNs. In addition, the\naugmentations that we developed can be used as a direct plug-in module during\ntraining and have demonstrated the effectiveness when implemented with\ndifferent GNN models through the AugliChem library. The Python-based package\nfor our implementation of Auglichem: Data augmentation library for chemical\nstructures, is publicly available at: https://github.com/BaratiLab/AugLiChem.\n Python (programming language) ; Computer science ; Artificial neural network ; Training set ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Graph ; Cheminformatics ; Chemical database ; Fingerprint (computing) ; Data mining ; Programming language ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Vu Ngoc Tuoc | Sanjeev K. Nayak | Nga T. T. Nguyen | S. P. Alpay | Tran Doan Huan
Source: AIP Advances (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

Atomic configurations for materials research: A case study of some simple binary compounds Data obtained from computational studies are crucial in building the necessary infrastructure for materials informatics. This computational foundation supplemented with experimental observations can then be employed in the extraction of possible hidden structure–property relationships through machine learning. There are limited attempts to sample the materials configuration space, even for the simplest chemical formulas. Advances in computational methods have now made it possible to accomplish this task. In this study, we analyze four chemical formulas, i.e., BSb, AlSb, MgSi2, and Sn3S, using first-principles computations. We show that numerous thermodynamically more stable crystal structures can be predicted computationally for these relatively simple chemical formulas, while the configuration space can be significantly and effectively mapped out. This approach allows for the prediction of new ground state structures, thereby expanding the available data on these materials. It also provides an understanding of the underlying potential energy topography and adds quality data for materials informatics. Computer science ; Simple (philosophy) ; Materials informatics ; Binary number ; Computation ; Space (punctuation) ; Chemical space ; Computational science ; Property (philosophy) ; Task (project management) ; Theoretical computer science ; Algorithm ; Chemistry ; Health informatics ; Mathematics ; Systems engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yanan Zhao | Xiaochen Liu | Lu Han | Xuefeng Zhu | Tianhang Wang | Gen Luo | Rencheng Zheng | Yi Luo
Source: Chemical Physics (article, 2021-07-07)
Field: Materials Science — Materials Chemistry

An optimized deep convolutional neural network for yield prediction of Buchwald-Hartwig amination Amination ; Yield (engineering) ; Pooling ; Convolutional neural network ; Principal component analysis ; Chemistry ; Artificial intelligence ; Deep learning ; Artificial neural network ; Machine learning ; Biological system ; Pattern recognition (psychology) ; Computer science ; Biochemical engineering ; Organic chemistry ; Catalysis ; Engineering ; Thermodynamics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Pedro S.F. Mendes | Sébastien Siradze | Laura Pirro | Joris Thybaut
Source: Reaction Chemistry & Engineering (article, 2021-10-06)
Field: Materials Science — Materials Chemistry

Extracting kinetic information in catalysis: an automated tool for the exploration of small data Kinetically relevant information for heterogeneously catalysed reactions is automatically extracted from small datasets by means of a newly-developed machine learning chemically-enriched tool. Computer science ; Catalysis ; Kinetic energy ; Data mining ; Chemistry ; Information retrieval ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Yingheng Wang | Yaosen Min | Erzhuo Shao | Ji Wu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-12-06)
Field: Materials Science — Materials Chemistry

Molecular Graph Contrastive Learning with Parameterized Explainable Augmentations ABSTRACT Learning generalizable, transferable, and robust representations for molecule data has always been a challenge. The recent success of contrastive learning (CL) for self-supervised graph representation learning provides a novel perspective to learn molecule representations. The most prevailing graph CL framework is to maximize the agreement of representations in different augmented graph views. However, existing graph CL frameworks usually adopt stochastic augmentations or schemes according to pre-defined rules on the input graph to obtain different graph views in various scales (e.g. node, edge, and subgraph), which may destroy topological semantemes and domain prior in molecule data, leading to suboptimal performance. Therefore, designing parameterized, learnable, and explainable augmentation is quite necessary for molecular graph contrastive learning. A well-designed parameterized augmentation scheme can preserve chemically meaningful structural information and intrinsically essential attributes for molecule graphs, which helps to learn representations that are insensitive to perturbation on unimportant atoms and bonds. In this paper, we propose a novel Molecular Graph Contrastive Learning with Parameterized Explainable Augmentations, MolCLE for brevity, that self-adaptively incorporates chemically significative information from both topological and semantic aspects of molecular graphs. Specifically, we apply deep neural networks to parameterize the augmentation process for both the molecular graph topology and atom attributes, to highlight contributive molecular substructures and recognize underlying chemical semantemes. Comprehensive experiments on a variety of real-world datasets demonstrate that our proposed method consistently outperforms compared baselines, which verifies the effectiveness of the proposed framework. Detailedly, our self-supervised MolCLE model surpasses many supervised counterparts, and meanwhile only uses hundreds of thousands of parameters to achieve comparative results against the state-of-the-art baseline, which has tens of millions of parameters. We also provide detailed case studies to validate the explainability of augmented graph views. CCS CONCEPTS • Mathematics of computing → Graph algorithms; • Applied computing → Bioinformatics; • Computing methodologies → Neural networks; Unsupervised learning. Parameterized complexity ; Computer science ; Molecular graph ; Graph ; Theoretical computer science ; Artificial intelligence ; Feature learning ; Machine learning ; Topology (electrical circuits) ; Algorithm ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yuyuan Lu | Geng Deng | Zhigang Shuai
Source: Pure and Applied Chemistry (article, 2021-06-30)
Field: Materials Science — Materials Chemistry

Future directions of chemical theory and computation Abstract Theoretical and computational chemistry aims to develop chemical theory and to apply numerical computation and simulation to reveal the mechanism behind complex chemical phenomena via quantum theory and statistical mechanics. Computation is the third pillar of scientific research together with theory and experiment. Computation enables scientists to test, discover, and build models/theories of the corresponding chemical phenomena. Theoretical and computational chemistry has been advanced to a new era due to the development of high-performance computational facilities and artificial intelligence approaches. The tendency to merge electronic structural theory with quantum chemical dynamics and statistical mechanics is of increasing interest because of the rapid development of on-the-fly dynamic simulations for complex systems plus low-scaling electronic structural theory. Another challenging issue lies in the transition from order to disorder, from thermodynamics to dynamics, and from equilibrium to non-equilibrium. Despite an increasingly rapid emergence of advances in computational power, detailed criteria for databases, effective data sharing strategies, and deep learning workflows have yet to be developed. Here, we outline some challenges and limitations of the current artificial intelligence approaches with an outlook on the potential future directions for chemistry in the big data era. Computation ; Computer science ; Workflow ; Statistical mechanics ; Chemistry ; Merge (version control) ; Statistical physics ; Data science ; Management science ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yunsie Chung | Florence H. Vermeire | Haoyang Wu | Pierre J. Walker | Michael H. Abraham | William H. Green
Source: ChemRxiv (preprint, 2021-09-13)
Field: Materials Science — Materials Chemistry

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain whilst the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the 3 models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software package, and source code. Solvation ; Enthalpy ; Implicit solvation ; Chemistry ; Thermodynamics ; Computer science ; Machine learning ; Statistical physics ; Physics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Shuyan Zhang | Gao Yang-yang | Zhibin Zhang | Xiubing Liang | Lizhong Wang
Source: SHILAP Revista de lepidopterología (article, 2021-01-01)
Field: Materials Science — Ceramics and Composites

Research progress in corrosion resistance of high-entropy metallic glasses The high-entropy metallic glasses, as a new kind of alloyed materials, have attracted considerable attention due to its excellent mechanical, anti-corrosion and magnetic properties and so forth, which combines the advantages of high-entropy alloys and metallic glasses. Herein, a comprehensive understanding of the concepts and features of high-entropy metallic glasses was provided as well as its preparation methods, distinguished structure and performance characteristics. The recent developments in the corrosion resistance mechanism and anti-corrosion properties were summarized. A new paradigm of using machine learning to design high-entropy metallic glasses was prospected. It was also pointed out that the precondition to the extensive use of this sort of materials is to explore the corrosion failure mechanism under the working conditions, and to achieve the micro-corrosion resistance mechanism perfection and its preparation process optimization. At present, the applied basic research on the development of high-entropy metallic glasses and its corrosion resistance will provide advanced technical support and material guarantee for the "Marching into the deep ocean" of China’s marine industry. Materials science ; Corrosion ; Amorphous metal ; High entropy alloys ; Metallurgy ; High resistance ; Metal ; Microstructure ; Alloy Glass properties and applications ; Material Science and Thermodynamics ; Metallic Glasses and Amorphous Alloys
Authors: Arailym Serikbay | Mehdi Bagheri | Amin Zollanvari
Source:  (article, 2021-09-07)
Field: Materials Science — Materials Chemistry

High Voltage Insulators Condition Analysis using Convolutional Neural Network High voltage insulators are nonconductive materials that are in use to isolate the conductor from the earthed transmission tower. However, they are regularly subjected to operational environmental contamination and detrimental electrical or mechanical stress. Undesirable operational conditions can cause insulations’ failure and ultimately the power transmission line outage. Therefore, monitoring and condition analysis of the insulators are essential tasks for the utility operators as well as transmission companies. With the widespread applications of deep learning techniques in engineering, in this study, we propose Convolutional Neural Network (CNN) as the backbone data analysis method and aerial images as the primary dataset. We conducted a model selection through an exhaustive search within a limited hyperparameter space based on our computational resources. We show that the constructed CNN classifier achieved a remarkable accuracy of 83.3% in classifying a clean insulator surface from the one covered with water droplets. Convolutional neural network ; Computer science ; Voltage ; Artificial neural network ; Artificial intelligence ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Elevator Systems and Control
Authors: Susan R. Atlas
Source: The Journal of Physical Chemistry A (article, 2021-04-23)
Field: Materials Science — Materials Chemistry

Embedding Quantum Statistical Excitations in a Classical Force Field Quantum-mechanically driven charge polarization and charge transfer are ubiquitous in biomolecular systems, controlling reaction rates, allosteric interactions, ligand-protein binding, membrane transport, and dynamically driven structural transformations. Molecular dynamics (MD) simulations of these processes require quantum mechanical (QM) information in order to accurately describe their reactive dynamics. However, current techniques-empirical force fields, subsystem approaches, <i>ab initio</i> MD, and machine learning-vary in their ability to achieve a consistent chemical description across multiple atom types, and at scale. Here we present a physics-based, atomistic force field, the <i>ensemble DFT charge-transfer embedded-atom method</i>, in which QM forces are described at a uniform level of theory across all atoms, avoiding the need for explicit solution of the Schrödinger equation or large, precomputed training data sets. Coupling between the electronic and atomistic length scales is effected through an ensemble density functional theory formulation of the embedded-atom method originally developed for elemental materials. Charge transfer is expressed in terms of ensembles of <i>ionic</i> state basis densities of individual atoms, and charge polarization, in terms of atomic <i>excited</i>-state basis densities. This provides a highly compact yet general representation of the force field, encompassing both local and system-wide effects. Charge rearrangement is realized through the evolution of ensemble weights, adjusted at each dynamical time step via chemical potential equalization. Quantum ; Statistical physics ; Charge (physics) ; Force field (fiction) ; Ab initio ; Molecular dynamics ; Physics ; Density functional theory ; Atom (system on chip) ; Chemical physics ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics