jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Henrik Schopmans | Patrick Reiser | Pascal Friederich
Source: arXiv (Cornell University) (preprint, 2023-03-21)
Field: Materials Science — Materials Chemistry

Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms Machine learning techniques have successfully been used to extract structural information such as the crystal space group from powder X-ray diffractograms. However, training directly on simulated diffractograms from databases such as the ICSD is challenging due to its limited size, class-inhomogeneity, and bias toward certain structure types. We propose an alternative approach of generating synthetic crystals with random coordinates by using the symmetry operations of each space group. Based on this approach, we demonstrate online training of deep ResNet-like models on up to a few million unique on-the-fly generated synthetic diffractograms per hour. For our chosen task of space group classification, we achieved a test accuracy of 79.9% on unseen ICSD structure types from most space groups. This surpasses the 56.1% accuracy of the current state-of-the-art approach of training on ICSD crystals directly. Our results demonstrate that synthetically generated crystals can be used to extract structural information from ICSD powder diffractograms, which makes it possible to apply very large state-of-the-art machine learning models in the area of powder X-ray diffraction. We further show first steps toward applying our methodology to experimental data, where automated XRD data analysis is crucial, especially in high-throughput settings. While we focused on the prediction of the space group, our approach has the potential to be extended to related tasks in the future. Artificial intelligence ; Computer science ; Materials science ; Artificial neural network ; Space (punctuation) ; Pattern recognition (psychology) ; Training set ; Crystal (programming language) ; Algorithm X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Jiajun Zhou | Austin M. Mroz | Kim E. Jelfs
Source: ChemRxiv (preprint, 2023-08-17)
Field: Materials Science — Materials Chemistry

Deep Generative Design of Porous Organic Cages via aVariational Autoencoder Porous organic cages (POCs) are a class of porous molecular materials characterised by their tunable, intrinsic porosity; this functional property makes them candidates for applications including guest storage and separation. Typically formed via dynamic covalent chemistry reactions from multifunctionalised molecular precursors, there is an enormous potential chemical space for POCs due to the fact they can be formed by combining two relatively small organic molecules, which themselves have an enormous chemical space. However, identifying suitable molecular precursors for POC formation is challenging, as POCs often lack shape persistence (the cage collapses upon solvent removal with loss of its cavity), thus losing a key functional property (porosity). Generative machine learning models have potential for targeted computational design of large functional molecular systems such as POCs. Here, we present a deep-learning-enabled generative model, Cage-VAE, for the targeted generation of shape-persistent POCs. We demonstrate the capacity of Cage-VAE to propose novel, shape-persistent POCs, via integration with multiple efficient sampling methods, including bayesian optimization and spherical linear interpolation. Chemical space ; Autoencoder ; Porosity ; Property (philosophy) ; Computer science ; Biological system ; Materials science ; Deep learning ; Nanotechnology ; Chemistry ; Artificial intelligence ; Organic chemistry ; Drug discovery ; Biology Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Qidong Lin | Bin Jiang
Source: arXiv (Cornell University) (preprint, 2023-04-21)
Field: Materials Science — Materials Chemistry

First-Principles Modeling of Equilibration Dynamics of Hyperthermal Products of Surface Reactions Using Scalable Neural Network Potential Equilibration dynamics of hot oxygen atoms following O2 dissociation on Pd(100) and Pd(111) surfaces are investigated by molecular dynamics simulations based on a scalable neural network potential enabling first-principles description of O2 and O interacting with variable Pd supercells. We find that to accurately describe the equilibration dynamics after dissociation, the simulation cell length necessarily exceeds twice the maximum distance of equilibrated oxygen adsorbates. By analyzing hundreds of trajectories with appropriate initial sampling, the measured distance distribution of equilibrated atom pairs on Pd(111) is well reproduced. However, our results on Pd(100) suggest that the ballistic motion of hot atoms predicted previously is a rare event under ideal conditions, while initial molecular orientation and surface thermal fluctuation could significantly affect the overall post-dissociation dynamics. On both surfaces, dissociated oxygen atoms remain primarily locate their nascent positions and then randomly cross bridge sites nearby. Dissociation (chemistry) ; Molecular dynamics ; Chemical physics ; Oxygen atom ; Oxygen ; Thermal ; Chemistry ; Molecular physics ; Atomic physics ; Physics ; Molecule ; Physical chemistry ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalytic Processes in Materials Science
Authors: Gamal M. A. Mahran | Omran M. Omran
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Using a Neural Network Model and Regression Analysis to Predict the Wear and Impact Energy of Al-Si Alloys Modified with Various Modifiers and Grain Refiners Using its Hardness Artificial neural network ; Regression analysis ; Materials science ; Energy (signal processing) ; Metallurgy ; Regression ; Linear regression ; Econometrics ; Statistics ; Computer science ; Artificial intelligence ; Machine learning ; Mathematics Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Powder Metallurgy Techniques and Materials
Authors: Ayana Ghosh | Sergei V. Kalinin | Maxim Ziatdinov
Source: arXiv (Cornell University) (preprint, 2023-01-06)
Field: Materials Science — Materials Chemistry

Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space Discovery of the molecular candidates for applications in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries, necessitates development of machine learning algorithms capable of rapid exploration of the chemical spaces targeting the desired functionalities. Here we introduce a novel approach for the active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data and introduce them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods such as SISSO and active learning into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning doesn't account for out-of-distribution effects and may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences. Chemical space ; Computer science ; Artificial intelligence ; Probabilistic logic ; Active learning (machine learning) ; Machine learning ; Property (philosophy) ; Construct (python library) ; Focus (optics) ; Space (punctuation) ; Process (computing) ; Gaussian process ; Drug discovery ; Gaussian ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Zhi Ning Chen
Source:  (article, 2023-05-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Transformation Optics and Prior-Knowledge-Guided Deep-Learning-Enabled Synthesis for Microwave Metalens Antennas (Keynote) This paper provides an overview of recent advances in metalens antenna modeling and synthesis. First, we discuss the application of true transformation optics (TTO) to Luneburg lenses, which enables compact antenna design without degrading radiation performance. The transformed lens antenna maintains the feed on/near the lens surface, a key feature of Luneburg lenses. We implement the lens antenna using anisotropic metamaterials. Next, we propose a deep learning-enabled synthesis method that incorporates prior knowledge to design pixelized metasurface unit cells and metasurface lens antennas. This method increases design freedom and expands the unit cell database, leading to enhancements in phase shift range and gain bandwidth. Our approach is demonstrated through the greatly improved gain bandwidth of a metalens antenna. Luneburg lens ; Metamaterial ; Transformation optics ; Lens (geology) ; Optics ; Computer science ; Bandwidth (computing) ; Wideband ; Antenna (radio) ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Naoki Noto | Akira Yada | Takeshi Yanai | Susumu Saito
Source: Angewandte Chemie (article, 2023-01-16)
Field: Chemistry — Organic Chemistry

Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols Abstract Catalytic systems using a small amount of organic photosensitizer for the activation of an inorganic (on‐demand ligand‐free) nickel(II) salt represent a cost‐effective method for cross‐coupling reactions, while C(sp 2 )−O bond formation remains less developed. Herein, we report a strategy for the synthesis of phenols with a nickel(II) salt and an organic photosensitizer, which was identified via an investigation into the catalytic activity of 60 organic photosensitizers consisting of various electron donor and acceptor moieties. To examine the effect of multiple intractable parameters on the catalytic activity of photosensitizers, machine‐learning (ML) models were developed, wherein we embedded descriptors representing their physical and structural properties, which were obtained from DFT calculations and RDKit, respectively. The study clarified that integrating both DFT‐ and RDKit‐derived descriptors in ML models balances higher “precision” and “recall” across a wide range of search space relative to using only one of the two descriptor sets. Photosensitizer ; Chemistry ; Catalysis ; Nickel ; Salt (chemistry) ; Ligand (biochemistry) ; Phenols ; Combinatorial chemistry ; Photochemistry ; Organic chemistry Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques ; Polyoxometalates: Synthesis and Applications
Authors: Haibo Niu | Tao Liu | Ji Fan | Hui Wang
Source:  (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

Image-Based Crystal Size Analysis for β- form L-Glutamic Acid Crystallization Via Deep Learning-Based Object Detection For evaluating the quality properties of p-form L-glutamic acid (LGA) crystals during a crystallization process, an image analysis method with deep learning-based object detection is proposed to acquire the crystal classification and size measurement, based on the optimization of in-situ image quality. Firstly, the homomorphic filtering is adopted to enhance the contrast of snapshotted images and highlight the image contours of crystals. Secondly, the crystal images are classified according to the degree of blur, and meanwhile, YOLOv6 is utilized for model training to obtain the position of individual crystals in a snapshotted image. Thirdly, the size of non-adhesive crystal image with clearness is calculated by a boundary fitting algorithm to improve the measurement accuracy. The effectiveness of the proposed image analysis method is verified by numerical simulation and experiments on the β-form LGA crystallization process. Crystallization ; Artificial intelligence ; Computer science ; Object (grammar) ; Object detection ; Crystal (programming language) ; Deep learning ; Pattern recognition (psychology) ; Chemistry ; Organic chemistry Crystallization and Solubility Studies ; Enzyme Structure and Function
Authors: Se Jun Yim | Ki-Won Gyak | Sanket A. Kawale | Amirreza Mottafegh | Chae‐Hyeon Park | Yoonseok Ko | In Kim | Sang Soo Jee | Dong‐Pyo Kim
Source: Journal of Industrial and Engineering Chemistry (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

One-flow multi-step synthesis of a monomer as a precursor of thermal-conductive semiconductor packaging polymer via multi-phasic separation Semiconductor ; Materials science ; Monomer ; Integrated circuit packaging ; Polymer ; Chemical engineering ; Nanotechnology ; Integrated circuit ; Optoelectronics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Epoxy Resin Curing Processes
Authors: Mayk Caldas Ramos | Andrew Dickson White
Source: arXiv (Cornell University) (preprint, 2023-07-11)
Field: Materials Science — Materials Chemistry

Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy and enthalpy, resulting in long computations for relatively poor accuracy. Data-driven approaches, such as deep learning, offer improved accuracy and computational efficiency but typically lack uncertainty quantification. Additionally, ease of use remains a concern for any computational technique, resulting in the sustained popularity of group-based contribution methods. In this work, we addressed these problems with a deep learning model with predictive uncertainty that runs on a static website (without a server). This approach moves computing needs onto the website visitor without requiring installation, removing the need to pay for and maintain servers. Our model achieves satisfactory results in solubility prediction. Furthermore, we demonstrate how to create molecular property prediction models that balance uncertainty and ease of use. The code is available at https://github.com/ur-whitelab/mol.dev, and the model is usable at https://mol.dev. USable ; Computer science ; Deep learning ; Computation ; Popularity ; Solubility ; Artificial intelligence ; Machine learning ; Artificial neural network ; Code (set theory) ; Entropy (arrow of time) ; Server ; Data mining ; Algorithm ; Chemistry ; Thermodynamics ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Zheyong Fan | Yang Xiao | Yanzhou Wang | Penghua Ying | Shunda Chen | Haikuan Dong
Source: arXiv (Cornell University) (preprint, 2023-10-23)
Field: Materials Science — Materials Chemistry

Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential is trained using reference data from quantum-mechanical density-functional theory calculations. This trained potential is then applied in large-scale molecular dynamics simulations, enabling the generation of realistic structures and accurate characterization of thermal transport properties. In addition, molecular dynamics simulations of atoms and linear-scaling quantum transport calculations of electrons are coupled to account for the electron-phonon scattering and other disorders that affect the charge carriers governing the electronic transport properties. We demonstrate the usefulness of this unified approach by studying thermoelectric transport properties of a graphene antidot lattice. Scaling ; Molecular dynamics ; Quantum ; Statistical physics ; Electron ; Phonon ; Scattering ; Thermal ; Transport phenomena ; Electronic structure ; Physics ; Materials science ; Computer science ; Condensed matter physics ; Quantum mechanics ; Thermodynamics ; Mathematics Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Chen Chen | Enze Xu | Defu Yang | Haibing Yin | Tao Wei | Hanning Chen | Yong Wei | Minghan Chen
Source: arXiv (Cornell University) (preprint, 2023-02-19)
Field: Materials Science — Materials Chemistry

Chemical Environment Adaptive Learning for Optical Band Gap Prediction of Doped Graphitic Carbon Nitride Nanosheets This study presents a novel Machine Learning Algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications, however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' local chemical environment underlying complex molecular structures. Our benchmark results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. Graphitic carbon nitride ; Computer science ; Benchmark (surveying) ; Graph ; Artificial neural network ; Carbon nitride ; Band gap ; Doping ; Deep learning ; Artificial intelligence ; Materials science ; Machine learning ; Nanotechnology ; Theoretical computer science ; Optoelectronics ; Photocatalysis ; Chemistry Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Limin Wang | M. Hanai | Toyotaro Suzumura | Shun Takashige | Kenjiro Taura
Source: arXiv (Cornell University) (preprint, 2023-08-17)
Field: Materials Science — Materials Chemistry

On Data Imbalance in Molecular Property Prediction with Pre-training Revealing and analyzing the various properties of materials is an essential and critical issue in the development of materials, including batteries, semiconductors, catalysts, and pharmaceuticals. Traditionally, these properties have been determined through theoretical calculations and simulations. However, it is not practical to perform such calculations on every single candidate material. Recently, a combination method of the theoretical calculation and machine learning has emerged, that involves training machine learning models on a subset of theoretical calculation results to construct a surrogate model that can be applied to the remaining materials. On the other hand, a technique called pre-training is used to improve the accuracy of machine learning models. Pre-training involves training the model on pretext task, which is different from the target task, before training the model on the target task. This process aims to extract the input data features, stabilizing the learning process and improving its accuracy. However, in the case of molecular property prediction, there is a strong imbalance in the distribution of input data and features, which may lead to biased learning towards frequently occurring data during pre-training. In this study, we propose an effective pre-training method that addresses the imbalance in input data. We aim to improve the final accuracy by modifying the loss function of the existing representative pre-training method, node masking, to compensate the imbalance. We have investigated and assessed the impact of our proposed imbalance compensation on pre-training and the final prediction accuracy through experiments and evaluations using benchmark of molecular property prediction models. Computer science ; Property (philosophy) ; Benchmark (surveying) ; Process (computing) ; Task (project management) ; Machine learning ; Artificial intelligence ; Training (meteorology) ; Stability (learning theory) ; Function (biology) ; Training set ; Construct (python library) ; Masking (illustration) ; Node (physics) ; Compensation (psychology) ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Text and Document Classification Technologies
Authors: Giovanni Trezza | Eliodoro Chiavazzo
Source: arXiv (Cornell University) (preprint, 2023-04-15)
Field: Materials Science — Materials Chemistry

Leveraging Composition-Based Material Descriptors for Machine Learning Optimization In this study, we evaluate several classifiers and focus on selecting a minimal set of appropriate material features. Our objective is to propose and discuss general strategies for reducing the number of descriptors required for material classification. The first strategy involves testing whether the critical temperature of the target material property is invariant with respect to binary groups of composition-based features. We also propose a multi-objective optimization procedure to reduce the set of composition-based material descriptors. The latter procedure is found to be particularly useful when applied to Bayesian classifiers. We test the proposed strategies focusing on low-temperature superconductors material data extracted from a public database. Computer science ; Invariant (physics) ; Artificial intelligence ; Bayesian optimization ; Binary number ; Bayesian probability ; Set (abstract data type) ; Machine learning ; Composition (language) ; Property (philosophy) ; Binary classification ; Focus (optics) ; Data mining ; Mathematics ; Support vector machine Machine Learning in Materials Science ; X-ray Diffraction in Crystallography