jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qi Zhang | Chang Liu | Stephen Wu | Yoshihiro Hayashi | Ryo Yoshida
Source: Science and Technology of Advanced Materials Methods (article, 2023-04-19)
Field: Materials Science — Materials Chemistry

A Bayesian method for concurrently designing molecules and synthetic reaction networks In the last few years, de novo molecular design using machine learning has made great technical progress but there are still challenges to be overcome in their practical applications. This is mostly owing to the cost and technical difficulty of synthesizing such computationally designed molecules. To overcome such barriers, various methods for synthetic route design using deep neural networks have been studied intensively in recent years. However, little progress has been made in designing molecules and their synthetic routes simultaneously. Here, we formulate the problem of simultaneously designing molecules with the desired set of properties and their synthetic routes within the framework of Bayesian inference. The design variables consist of a set of reactants in a reaction network and its network topology. The design space is extremely large because it consists of all combinations of purchasable reactants, often in the order of millions or more. In addition, the designed reaction networks can adopt any topology beyond simple multistep linear reaction routes. To solve this hard combinatorial problem, we present a powerful sequential Monte Carlo algorithm that recursively designs a synthetic reaction network by sequentially building up single-step reactions. In a case study of designing drug-like molecules based on commercially available compounds, compared with heuristic combinatorial search methods, the proposed method showed overwhelming performance in terms of computational efficiency, coverage, and novelty with respect to existing compounds. We also provide the Python library “Seq-Stack-Reaction” with its illustrative example of designing highly viscous lubricant molecules. Bayesian network ; Bayesian probability ; Molecule ; Computer science ; Artificial intelligence ; Algorithm ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Shuqin Wang | Qiongxiong Ma | Zhongchao Wei | Wanrong Liu | Ruihuan Wu | Wen Feng Ding | Jianping Guo
Source: Plasmonics (article, 2023-12-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Realizing Multi-Absorption Properties Metamaterial Absorbers by a Dual-Channel Tandem Neural Network Metamaterial ; Tandem ; Materials science ; Wideband ; Artificial neural network ; Absorption (acoustics) ; Computer science ; Nanotechnology ; Optoelectronics ; Optics ; Artificial intelligence ; Composite material ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Hao Xu | Jiwei Xu | Long-Xiang Yi | Yu-Ting Yuan | Zheng-Qun Cai
Source: Frontiers in Computational Neuroscience (article, 2023-02-03)
Field: Materials Science — Polymers and Plastics

Performance control study of interleaved meltblown non-woven materials based on statistical analysis and predictive modeling Meltblown nonwoven materials have gained attention due to their excellent filtration performance. The research on the performance of the intercalation meltblown preparation process is complex and a current research focus in the field of chemical production. Based on data related to intercalated and unintercalated meltblown materials under given process conditions, a product performance prediction model of intercalated meltblown materials was established under different process parameters (receiving distance, hot air velocity). The structural variables (thickness, porosity, and compressive resilience), the change in product performance, and the relationship between structural variables and product performance (filtration resistance, efficiency, air permeability) after intercalation were studied. Multiple regression analysis was used to analyze the structural variables, and evaluation of the regression results were made using R2, MSE, SSR, and SST. A BP neural network prediction model for product performance was established. The BP neural network model was used to find the maximum filtration efficiency. The study provides theoretical support for regulating product performance by solving the maximum filtration efficiency using BP neural network model. Filtration (mathematics) ; Materials science ; Porosity ; Artificial neural network ; Composite material ; Regression analysis ; Biological system ; Computer science ; Mathematics ; Statistics ; Artificial intelligence ; Machine learning Textile materials and evaluations
Authors: Shimpei Takemoto | Yoshishige Okuno
Source: MRS Advances (article, 2023-02-06)
Field: Materials Science — Materials Chemistry

How to lead R&amp;D digital transformation in a chemical corporation Interface (matter) ; Computer science ; Relational database ; Transformation (genetics) ; Data science ; Artificial intelligence ; Machine learning ; Data mining ; Database ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Materials Characterization Techniques
Authors: Hyunjoo Lee | Dongchoul Kim
Source: Multiscale Science and Engineering (review, 2023-12-01)
Field: Materials Science — Materials Chemistry

Recent Computational Approaches for Accelerating Dendrite Growth Prediction: A Short Review Computer science ; Field (mathematics) ; Graphics processing unit ; Discretization ; Throughput ; Computational science ; Machine learning ; Artificial intelligence ; Parallel computing Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Magnetic Properties and Applications
Authors: Hart Plommer | Isaiah O. Betinol | Tom Dupree | Markus Roggen | Jolene P. Reid
Source: Digital Discovery (article, 2023-11-28)
Field: Chemistry — Spectroscopy

Extraction yield prediction for the large-scale recovery of cannabinoids Machine learning techniques typically applied in organic synthesis also extend to the optimization of extractions where the process conditions are even more expensive to evaluate. Yield (engineering) ; Extraction (chemistry) ; Scale (ratio) ; Chromatography ; Environmental science ; Chemistry ; Materials science ; Geography ; Metallurgy ; Cartography Analytical Chemistry and Chromatography ; Innovative Microfluidic and Catalytic Techniques Innovation ; Metabolomics and Mass Spectrometry Studies
Authors: Immanuel Sulzer | Tobias Buck
Source: arXiv (Cornell University) (preprint, 2023-12-10)
Field: Materials Science — Materials Chemistry

Speeding up astrochemical reaction networks with autoencoders and neural ODEs In astrophysics, solving complex chemical reaction networks is essential but computationally demanding due to the high dimensionality and stiffness of the ODE systems. Traditional approaches for reducing computational load are often specialized to specific chemical networks and require expert knowledge. This paper introduces a machine learning-based solution employing autoencoders for dimensionality reduction and a latent space neural ODE solver to accelerate astrochemical reaction network computations. Additionally, we propose a cost-effective latent space linear function solver as an alternative to neural ODEs. These methods are assessed on a dataset comprising 29 chemical species and 224 reactions. Our findings demonstrate that the neural ODE achieves a 55x speedup over the baseline model while maintaining significantly higher accuracy by up to two orders of magnitude reduction in relative error. Furthermore, the linear latent model enhances accuracy and achieves a speedup of up to 4000x compared to standard methods. Ode ; Speedup ; Computer science ; Artificial neural network ; Solver ; Dimensionality reduction ; Curse of dimensionality ; Reduction (mathematics) ; Computation ; Chemical space ; Artificial intelligence ; Algorithm ; Parallel computing ; Applied mathematics ; Mathematics ; Chemistry Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Spectroscopy and Chemometric Analyses
Authors: Guillermo Bárcena‐González | Andrei Hernández‐Robles | Álvaro Mayoral | L. Martı́nez | Yves Huttel | Pedro L. Galindo | Arturo Ponce
Source: Crystal Research and Technology (article, 2023-01-03)
Field: Materials Science — Surfaces, Coatings and Films

Unsupervised Learning for the Segmentation of Small Crystalline Particles at the Atomic Level Abstract Electron backscattering diffraction provides the analysis of crystalline phases at large scales (microns) while precession electron diffraction may be used to get 4D‐STEM data to elucidate structure at nanometric resolution. Both are limited by the probe size and also exhibit some difficulties for the generation of large datasets, given the inherent complexity of image acquisition. The latter appoints the application of advanced machine learning techniques, such as deep learning adapted for several tasks, including pattern matching, image segmentation, etc. This research aims to show how Gabor filters provide an appropriate feature extraction technique for electron microscopy images that could prevent the need of large volumes of data to train deep learning models. The work presented herein combines an algorithm based on Gabor filters for feature extraction and an unsupervised learning method to perform particle segmentation of polyhedral metallic nanoparticles and crystal orientation mapping at atomic scale. Experimental results have shown that Gabor filters are convenient for electron microscopy images analysis, that even a nonsupervised learning algorithm can provide remarkable results in crystal segmentation of individual nanoparticles. This approach enables its application to dynamic analysis of particle transformation recorded with aberration‐corrected microscopy, offering new possibilities of analysis at nanometric scale. Artificial intelligence ; Segmentation ; Gabor filter ; Computer science ; Pattern recognition (psychology) ; Unsupervised learning ; Diffraction ; Materials science ; Orientation (vector space) ; Image segmentation ; Feature extraction ; Transformation (genetics) ; Computer vision ; Optics ; Physics ; Chemistry ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Andy S. Anker | Emil T. S. Kjær | Mikkel Juelsholt | Kirsten M. Ø. Jensen
Source: ChemRxiv (preprint, 2023-08-23)
Field: Materials Science — Materials Chemistry

POMFinder: Identifying polyoxometalate cluster structures from pair distribution function data using explainable machine learning Characterisation of material structure with Pair Distribution Function (PDF) analysis typically involves refining a structure model against an experimental dataset. However, finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. We present POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometalate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is demonstrated to identify suitable POMs on experimental data, including in situ data collected with fast acquisition time. This automated approach shows significant potential for identifying suitable structure models for structure refinements to extract quantitative, structural parameters in materials chemistry research. The code is open source and user-friendly, making it accessible to those without prior ML knowledge. We also demonstrate that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques compared to conventional refinement methods. Computer science ; Polyoxometalate ; Cluster (spacecraft) ; Classifier (UML) ; Experimental data ; Artificial intelligence ; Pair distribution function ; Function (biology) ; Machine learning ; Data structure ; Data mining ; Chemistry ; Physics Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Radioactive element chemistry and processing
Authors: Sai Mahit Vadaddi | Qiyuan Zhao | Brett M. Savoie
Source: ChemRxiv (preprint, 2023-11-01)
Field: Materials Science — Materials Chemistry

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability Activation energy characterization of competing reactions is a costly, but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However recently, several groups have popularized the idea of predicting activation energies directly, based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough dataset. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state dataset and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ~4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case-studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction datasets but that out-of-distribution performance remains poor. Transferability ; Computer science ; Graph ; Activation energy ; Theoretical computer science ; Chemistry ; Machine learning ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Benjamin B. Hoar | Weitong Zhang | Yuanzhou Chen | Jingwen Sun | Hongyuan Sheng | Yucheng Zhang | Jenny Y. Yang | Cyrille Costentin | Quanquan Gu | Chong Liu
Source: ChemRxiv (preprint, 2023-12-06)
Field: Chemistry — Electrochemistry

Object-detecting deep learning for mechanism discernment in multi-redox cyclic voltammograms In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification of electrochemical mechanisms in cyclic voltammetry set the foundation for subsequent quantitative evaluation and practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists in mechanism assignment. Herein, we present a custom architecture based on Faster R-CNN (Regional Convolutional Neural Network), dubbed as EchemNet, capable of assigning both voltage windows and mechanism classes to electrochemical events within multi-redox cyclic voltammograms. The developed technique detects over 96% of all electrochemical events in simulated testing data and shows a classification accuracy of up to 97.2% on redox events with 8 known mechanisms. Further, the overall inference F1 score, a combined measure of accuracy and sensitivity in statistical analysis, achieves 0.937, relaying high reliability for detecting and classifying all electrochemical events within complicated voltammograms. This newly developed DL model, the first of its kind, proves the feasibility of redox-event detection and electrochemical mechanism classification with minimal a priori knowledge. The DL model will augment human researchers’ productivity and constitute a critical component in a general-purpose autonomous electrochemistry laboratory Computer science ; Convolutional neural network ; Artificial intelligence ; Cyclic voltammetry ; Redox ; Pattern recognition (psychology) ; Mechanism (biology) ; A priori and a posteriori ; Deep learning ; Electrochemistry ; Chemistry ; Electrode Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Dmitrij Rappoport
Source: The Journal of Physical Chemistry A (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

Statistics and Bias-Free Sampling of Reaction Mechanisms from Reaction Network Models Selection bias is inevitable in manually curated computational reaction databases but can have a significant impact on generalizability of quantum chemical methods and machine learning models derived from these data sets. Here, we propose quasireaction subgraphs as a discrete, graph-based representation of reaction mechanisms that has a well-defined associated probability space and admits a similarity function using graph kernels. Quasireaction subgraphs are thus well suited for constructing representative or diverse data sets of reactions. Quasireaction subgraphs are defined as subgraphs of a network of formal bond breaks and bond formations (transition network) composed of all shortest paths between reactant and product nodes. However, due to their purely geometric construction, they do not guarantee that the corresponding reaction mechanisms are thermodynamically and kinetically feasible. As a result, a binary classification of feasible (reaction subgraphs) and infeasible (nonreactive subgraphs) must be applied after sampling. In this paper, we describe the construction and properties of quasireaction subgraphs and characterize the statistics of quasireaction subgraphs from CHO transition networks with up to six non-hydrogen atoms. We explore their clustering using Weisfeiler-Lehman graph kernels. Computer science ; Cluster analysis ; Graph ; Theoretical computer science ; Binary number ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Miruna T. Cretu | Alessandra Toniato | Amol Thakkar | Amin Debabeche | Teodoro Laino | Alain C. Vaucher
Source: Machine Learning Science and Technology (article, 2023-07-18)
Field: Materials Science — Materials Chemistry

Standardizing chemical compounds with language models Abstract With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds accurately and consistently. Harmonized representations facilitate the extraction of insightful information from datasets, and are advantageous for machine learning applications. To achieve consistent representations throughout datasets, one relies on molecule standardization, which is typically accomplished using rule-based algorithms that modify descriptions of functional groups. Here, we present the first deep-learning model for molecular standardization. We enable custom standardization schemes based solely on data, which, as additional benefit, support standardization options that are difficult to encode into rules. Our model achieves over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>98</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> accuracy in learning two popular rule-based standardization protocols. We then follow a transfer learning approach to standardize metal-organic compounds (for which there is currently no automated standardization practice), based on a human-curated dataset of 1512 compounds. This model predicts the expected standardized molecular format with a test accuracy of 80.7%. As standardization can be considered, more broadly, a transformation from undesired to desired representations of compounds, the same data-driven architecture can be applied to other tasks. For instance, we demonstrate the application to compound canonicalization and to the determination of major tautomers in solution, based on computed and experimental data. Standardization ; Computer science ; Artificial intelligence ; Machine learning ; Data mining ; Natural language processing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Jitendra Pramanik | Somalika Pradhan | Abhaya Kumar Samal
Source: Materials Today Proceedings (article, 2023-07-28)
Field: Materials Science — Polymers and Plastics

Artificial neural network-based mechanical properties prediction of cellulose polyvinyl alcohol bio composite Polyvinyl alcohol ; Materials science ; Mass fraction ; Cellulose ; Composite material ; Shear modulus ; Composite number ; Modulus ; Flexural modulus ; Artificial neural network ; Flexural strength ; Chemical engineering ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications
Authors: Mahdi Rashvand | Giuseppe Altieri | Attilio Matera | Francesco Genovese | Giovanni Carlo Di Renzo
Source: Journal of Food Measurement & Characterization (article, 2023-02-07)
Field: Chemistry — Analytical Chemistry

Potential of low frequency dielectric spectroscopy and machine learning methods for extra virgin olive oils discrimination based on the olive cultivar and ripening stage Ripening ; Cultivar ; Peroxide value ; Olive oil ; Chemistry ; Mathematics ; Biological system ; Olea ; Correlation coefficient ; Horticulture ; Food science ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Zechen Tang | He Li | Peize Lin | Xiaoxun Gong | Gan Jin | Lixin He | Hong Jiang | Xinguo Ren | Wenhui Duan | Yong Xu
Source: arXiv (Cornell University) (preprint, 2023-02-16)
Field: Materials Science — Materials Chemistry

Efficient hybrid density functional calculation by deep learning Hybrid density functional calculation is indispensable to accurate description of electronic structure, whereas the formidable computational cost restricts its broad application. Here we develop a deep equivariant neural network method (named DeepH-hybrid) to learn the hybrid-functional Hamiltonian from self-consistent field calculations of small structures, and apply the trained neural networks for efficient electronic-structure calculation by passing the self-consistent iterations. The method is systematically checked to show high efficiency and accuracy, making the study of large-scale materials with hybrid-functional accuracy feasible. As an important application, the DeepH-hybrid method is applied to study large-supercell Moiré twisted materials, offering the first case study on how the inclusion of exact exchange affects flat bands in the magic-angle twisted bilayer graphene. Hybrid functional ; Computer science ; Artificial neural network ; Density functional theory ; Hamiltonian (control theory) ; Electronic structure ; Algorithm ; Topology (electrical circuits) ; Artificial intelligence ; Mathematics ; Physics ; Mathematical optimization ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques