jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Edirisuriya M. Dilanga Siriwardane | Yong Zhao | Indika Perera | Jianjun Hu
Source: ChemRxiv (preprint, 2022-01-03)
Field: Materials Science — Materials Chemistry

Generative Design of Stable Semiconductor Materials Using Deep Learning And DFT Semiconductor device technology has exceptionally developed in complexity since discovering the bipolar transistor. With the rapid advancement of various technologies, semiconductors with distinct properties are essential. Recently, deep-learning, data-mining, and density functional theory (DFT)- based high-throughput calculations were widely performed to discover potential semiconductors for diverse applications. CubicGAN is a generative adversarial network where high-throughput analyses were done to uncover mechanically and dynamically stable materials with the assistance of DFT. In our work, we screened the semiconductors using a binary classifier from materials found from the CubicGAN. Next, we performed DFT computations to study their thermodynamic stability based on energy-above-hull and formation energy. According to our studies, 12 stable semiconductors were found with a particular class of materials, which we label as AA′MH6. Those are BaNaRhH6, BaSrZnH6, BaCsAlH6, SrTlIrH6, KNaNiH6, NaYRuH6, CsKSiH6, CaScMnH6, YZnMnH6, NaZrMnH6, AgZrMnH6, AgZrMnH6, and ScZnMnH6. It could be shown that AA′MH6 with M=Mn and NaYRuH6 semiconductors have considerably different structural, mechanical, and thermodynamic properties compared to the rest of the AA′MH6 semiconductors. In this study, The maximum bandgap found was approximately 3.3 eV from KNaNiH6, while the minimum bandgap was about 1.3 eV from CaScMnH6. BaNaRhH6, BaCsAlH6, CsKSiH6, KNaNiH6, and NaYRuH6 were identified as wide-bandgap semiconductors, where bandgaps are greater than 2 eV. Furthermore, BaSrZnH6 and KNaNiH6 are a direct bandgap semiconductors, whereas other AA′MH6 semiconductors exhibit indirect bandgaps. Semiconductor ; Band gap ; Materials science ; Density functional theory ; Wide-bandgap semiconductor ; Optoelectronics ; Nanotechnology ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Yan Liu | Yiming Mo | Youwei Cheng
Source: ChemRxiv (preprint, 2022-12-08)
Field: Materials Science — Materials Chemistry

Uncertainty-calibrated deep learning for rapid identification of reaction mechanisms Analyzing the reaction mechanism is the cornerstone across all chemistry disciplines. However, the existing reaction mechanism data only have limited coverage of the enormous chemical space and are usually collected from various sources, thus, limiting advanced machine learning tools to facilitate classical computational chemistry approaches for obtaining reaction mechanisms. Herein we present an uncertainty-calibrated learning-to-ranking (UC-LTR) model for constructing mechanistic reaction networks. UC-LTR model can make efficient and accurate prediction with quantitative uncertainty learned from inconsistent and sparse data sources. This allows a coherent utilization of existing data, improving the confidence of using predicted results for the following experimental or quantum chemistry refinement. We demonstrated this approach can provide efficient and reliable identification of reaction networks for high-temperature gas-phase reactions. This work showcase a practical deep learning strategy in fields of chemistry where the availability of high-quality big data is limited Computer science ; Limiting ; Cornerstone ; Identification (biology) ; Ranking (information retrieval) ; Chemical space ; Machine learning ; Artificial intelligence ; Reaction mechanism ; Biochemical engineering ; Data science ; Chemistry ; Drug discovery ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Shehtab Zaman | Ethan Ferguson | Cécile Pereira | Denis Akhivarov | Mauricio Araya‐Polo | Kenneth Chiu
Source:  (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

ParticleGrid: Enabling Deep Learning using 3D Representation of Materials From AlexNet to Inception, autoencoders to diffusion models, the development of novel and powerful deep learning models and learning algorithms has proceeded at breakneck speeds. In part, we believe that rapid iteration of model architecture and learning techniques by a large community of researchers over a common representation of the underlying entities has resulted in transferable deep learning knowledge. As a result, model scale, accuracy, fidelity, and compute performance have dramatically increased in computer vision and natural language processing. On the other hand, the lack of a common representation for chemical structure has hampered similar progress. To enable transferable deep learning, we identify the need for a robust 3-dimensional representation of materials such as molecules and crystals. The goal is to enable both materials property prediction and materials generation with 3D structures. While computationally costly, such representations can model a large set of chemical structures. We propose ParticleGrid, a SIMD-optimized library for 3D structures, that is designed for deep learning applications and to seamlessly integrate with deep learning frameworks. Our highly optimized grid generation allows for generating grids on the fly on the CPU, reducing storage and GPU compute and memory requirements. We show the efficacy of 3D grids generated via ParticleGrid and accurately predict molecular energy properties using a 3D convolutional neural network. Our model is able to get 0.006 mean square error and nearly match the values calculated using computationally costly density functional theory at a fraction of the time. Computer science ; Deep learning ; Artificial intelligence ; Grid ; Convolutional neural network ; Representation (politics) ; Set (abstract data type) ; Machine learning Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Hydrocarbon exploration and reservoir analysis
Authors: Alan X. Wang
Source:  (article, 2022-03-04)
Field: Materials Science — Biomaterials

Diatom photonic crystal biosensors: materials, applications, and fusion with machine learning Photonic crystals provide unique mechanisms to enhance light-matter interactions and have been widely adopted in optical biosensing. However, traditional photonic crystal biosensors still face significant challenges in cost, repeatability, and accuracy in quantification. Diatoms are unicellular algae in nature which have photonic crystal nanostructures with unique optical properties. In addition, they provide ultra-hydrophilic surface, excellent adsorption capabilities, microfluidic effect, and thin layer chromatography. This talk will provide an overview of our recent research progress in materials synthesis, various applications, and fusion with machine learning for better performance. Biosensor ; Photonic crystal ; Materials science ; Nanotechnology ; Microfluidics ; Photonics ; Nanostructure ; Computer science ; Optoelectronics Diatoms and Algae Research
Authors: Mohammad Asad Tariq | Senthil Kumar Arumugasamy
Source: Process Integration and Optimization for Sustainability (article, 2022-03-22)
Field: Materials Science — Biomaterials

Prediction of Polycaprolactone Molecular Weight Synthesized via Enzymatic Polymerization Process: Comparing Training Algorithms of Artificial Neural Network Modeling Polycaprolactone ; Artificial neural network ; Polymerization ; Computer science ; Process (computing) ; Algorithm ; Artificial intelligence ; Materials science ; Polymer ; Composite material biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications
Authors: Dong Hyeon Mok | WooSeok Lee | JongSeung Kim | Hyun Dong Jung | Ho Yeon Jang | Sung-Jin Moon | ChaeHyeon Lee | Seoin Back
Source: Ceramist (article, 2022-06-30)
Field: Materials Science — Materials Chemistry

Computation and Machine Learning for Catalyst Discovery Towards a sustainable energy future, it is essential to develop new catalysts with improved properties for key catalytic systems such as Haber-Bosch process, water electrolysis and fuel cell. Unfortunately, the current state-of-the-art catalysts still suffer from high cost of noble metals, insufficient catalytic activity and long-term stability. Furthermore, the current strategy to develop new catalysts relies on “trial-and-error” method, which could be time-consuming and inefficient. To tackle this challenge, atomic-level simulations have demonstrated the potential to facilitate catalyst discovery. For the past decades, the simulations have become reasonably accurate so that they can provide useful insights toward the origin of experimentally observed improvements in catalytic properties. In addition, with the exponential increase in computing power, high-throughput catalyst screening has become feasible. More excitingly, recent advances in machine learning have opened the possibility to further accelerate catalyst discovery. Herein, we introduce recent applications and challenges of computation and machine learning for catalyst discovery. Catalysis ; Computer science ; Computation ; Biochemical engineering ; Nanotechnology ; Materials science ; Chemistry ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Iryna Didych | Oleh Yasniy | Iaroslav Pasternak | Lukash Sobashek
Source: Procedia Structural Integrity (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Modelling of AL-6061 aluminum alloy deformation diagrams by machine learning methods There was studied a deformation diagrams of AL-6061 aluminum alloy at various temperatures. Predicting the behavior of nonlinear systems is important. Experimental data have a certain variance that must be taken into account. This gives us a regression problem that can be solved by machine learning. Therefore, with sufficient experimental data, it is advisable to use machine learning methods, namely neural networks and boosted trees in science and technology, where stress-strain diagrams of structural materials is extremely important, in particular in metallurgy, aircraft, railways and more. The diagrams of AL-6061 aluminum alloy deformation was predicted by machine learning methods, in particular, neural networks and boosted trees at temperatures T = 343, 413°C. It is shown that the obtained results are in good agreement with the experimental ones. It was founded that the neural network method gives the least prediction error of 0.05% in the test sample. Artificial neural network ; Deformation (meteorology) ; Alloy ; Aluminium ; Diagram ; Materials science ; Experimental data ; Stress (linguistics) ; Artificial intelligence ; Machine learning ; Computer science ; Metallurgy ; Mathematics ; Composite material ; Statistics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Metallurgy and Material Forming
Authors: Suvo Banik | Debdas Dhabal | Henry Lik‐Yuen Chan | Sukriti Manna | Mathew J. Cherukara | Valeria Molinero | Subramanian KRS Sankaranarayanan
Source: arXiv (Cornell University) (preprint, 2022-07-20)
Field: Materials Science — Materials Chemistry

CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment Machine learning models and applications in materials design and discovery typically involve the use of feature representations or "descriptors" followed by a learning algorithm that maps them to a user-desired property of interest. Most popular mathematical formulation-based descriptors are not unique across atomic environments or suffer from transferability issues across different application domains and/or material classes. In this work, we introduce the Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) and diverse classes ranging from metals, oxides, non-metals and even hierarchical materials such as zeolites and semi ordered materials such as mesophases. We first demonstrate a case study where the classification is based on a global, structure-level representation such as space group and structural dimensionality (e.g., bulk, 2D, clusters etc.). Using representative materials such as polycrystals and zeolites, we next demonstrate the transferability of our network in successfully performing local atom-level classification tasks, such as grain boundary identification and other heterointerfaces. We also demonstrate classification in (thermal) noisy dynamical environments using a representative example of crystal nucleation and growth of a zeolite polymorph from an amorphous synthesis mixture. Finally, we characterize the formation of a binary mesophase and its phase transitions and the growth of ice, demonstrating the performance of CEGANN in systems with thermal noise and compositional diversity. Overall, our approach is agnostic to the material type and allows for multiscale classification of features ranging from atomic-scale crystal structures to heterointerfaces to microscale grain boundaries. Computer science ; Artificial intelligence ; Artificial neural network ; Ranging ; Curse of dimensionality ; Materials science ; Machine learning ; Pattern recognition (psychology) Machine Learning in Materials Science ; Neural Networks and Applications ; Advanced Memory and Neural Computing
Authors: Jiayang Li | Shuya Dong | Beibei An | Zhengkun Zhang | Yuanyuan Li | Li Wang | Jinglai Zhang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for the Yield Prediction of Co2 Cyclization Reaction Catalyzed by the Ionic Liquids Yield (engineering) ; Ionic liquid ; Catalysis ; Ionic bonding ; Chemistry ; Organic chemistry ; Materials science ; Ion ; Metallurgy Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: E. A. Kosenko | А. В. Остроух | N. I. Baurova
Source: Polymer Science Series D (article, 2022-07-25)
Field: Materials Science — General Materials Science

Prospects of Applying the Neural-Network Modeling for Estimating the Structure and Properties of Polymer-Composite Materials with Hybrid Matrices Artificial neural networks are used in various fields, ranging from economics and sociological research to medicine and robotics. Neural networks are used to solve a wide variety of problems, including event prediction, associative information search, product quality control, and many others. However, perhaps the most popular task solved using neural networks is visual-pattern recognition. Pattern and symbol recognition make it possible to considerably reduce a working time and enhance the accuracy of various operating procedures. In this work, we consider the problems of enhancing the efficiency and accuracy of the evaluation of the structures and properties of polymer-composite materials (PCMs) with different types of hybrid matrices by classifying the data using neural networks. The results of training a neural-network model for classifying the PCM structures with different hybrid matrices are presented. After additional training, the proposed neural-network model can be used to evaluate the mechanical properties of PCMs with different hybrid matrices and to predict them when designing products. Artificial neural network ; Artificial intelligence ; Computer science ; Machine learning ; Hybrid neural network ; Ranging ; Time delay neural network ; Quality (philosophy) ; Associative property ; Hybrid system ; Mathematics Material Properties and Applications ; Polymer Science and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Minjie Chen
Source:  (preprint, 2022-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Framework for Modeling Power Magnetic Material Characteristics &lt;p&gt;This paper applies machine learning to modeling power magnetics. We first introduce an open-source database - MagNet - which hosts a large amount of experimentally measured excitation data for many materials across a variety of operating conditions. The processes for data acquisition and data quality control are explained. We then demonstrate a few neural network-based power magnetics modeling tools for modeling core losses and B–H loops. Machine learning allows the many factors that may influence the magnetic characteristics being modeled in a unified framework, and provides insights to quantify the complexity of magnetic characteristics and reduce the size of the measurement data required to build a precise model. Neural network models are found effective in compressing the measurement data and predicting the behaviors of magnetic materials such as the core loss and the B–H loop. The behaviors of a typical power magnetic material (e.g., TDK N87) across a wide range of operating conditions (e.g., temperature, waveform, dc-bias) can be well described by a small-scale neural network (200 KB) which is 10,000 times smaller than the raw measured time-series data (2 GB), paving the way toward using neural networks as an interactive datasheet to assist magnetics design.&lt;/p&gt; Datasheet ; Artificial neural network ; Waveform ; Power (physics) ; Computer science ; Magnet ; Core (optical fiber) ; Electronic engineering ; Artificial intelligence ; Engineering ; Electrical engineering ; Voltage ; Physics Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Non-Destructive Testing Techniques
Authors: Sterling G. Baird | Marianne Liu | Hasan M. Sayeed | Taylor D. Sparks
Source: arXiv (Cornell University) (preprint, 2022-01-25)
Field: Materials Science — Materials Chemistry

Data-Driven Materials Discovery and Synthesis using Machine Learning Methods Experimentally [1-38] and computationally [39-50] validated machine learning (ML) articles are sorted based on the size of the training data: 1-100, 101-10000, and 10000+ in a comprehensive set summarizing legacy and recent advances in the field. The review emphasizes the interrelated fields of synthesis, characterization, and prediction. Size range 1-100 consists mostly of Bayesian optimization (BO) articles, whereas 101-10000 consists mostly of support vector machine (SVM) articles. The articles often use combinations of ML, feature selection (FS), adaptive design (AD), high-throughput (HiTp) techniques, and domain knowledge to enhance predictive performance and/or model interpretability. Grouping cross-validation (G-CV) techniques curb overly optimistic extrapolative predictive performance. Smaller datasets relying on AD are typically able to identify new materials with desired properties but do so in a constrained design space. In larger datasets, the low-hanging fruit of materials optimization is typically already discovered, and the models are generally less successful at extrapolating to new materials, especially when the model training data favors a particular type of material. The large increase of ML materials science articles that perform experimental or computational validation on the predicted results demonstrates the interpenetration of materials informatics with the materials science discipline and an accelerating materials discovery for real-world applications. Interpretability ; Machine learning ; Computer science ; Bayesian optimization ; Support vector machine ; Artificial intelligence ; Feature selection ; Field (mathematics) ; Set (abstract data type) ; Materials informatics ; Predictive modelling ; Feature engineering ; Characterization (materials science) ; Domain (mathematical analysis) ; Deep learning ; Health informatics ; Nanotechnology ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Silvan Käser | Luis Itza Vazquez-Salazar | Markus Meuwly | Kai Töpfer
Source: arXiv (Cornell University) (preprint, 2022-09-23)
Field: Materials Science — Materials Chemistry

Neural Network Potentials for Chemistry: Concepts, Applications and Prospects Artificial Neural Networks (ANN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on larger scale. Computer science ; Artificial neural network ; Representation (politics) ; Field (mathematics) ; Reliability (semiconductor) ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Various Chemistry Research Topics ; Advanced Chemical Physics Studies
Authors: A.J. Blanchard | Pei Zhang | Debsindhu Bhowmik | Kshitij Mehta | John Gounley | Samuel Temple Reeve | Stephan Irle | Massimiliano Lupo Pasini
Source: ChemRxiv (preprint, 2022-07-24)
Field: Materials Science — Materials Chemistry

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems. Chemical space ; Workflow ; Computer science ; Set (abstract data type) ; Quantum chemical ; Surrogate model ; Property (philosophy) ; Computation ; Graph ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Molecule ; Algorithm ; Drug discovery ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics