jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Brandon K. Phan | Kuan-Hsuan Shen | Rishi Gurnani | Tran Doan Huan | Ryan P. Lively | Rampi Ramprasad
Source: arXiv (Cornell University) (preprint, 2024-06-21)
Field: Materials Science — Materials Chemistry

Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning Machine learning (ML) models for predicting gas permeability through polymers have traditionally relied on experimental data. While these models exhibit robustness within familiar chemical domains, reliability wanes when applied to new spaces. To address this challenge, we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques. This framework combines scarce "high-fidelity" experimental data with abundant diverse "low-fidelity" simulation or synthetic data, resulting in predictive models that display a high level of generalizability across novel chemical spaces. Additionally, this multi-task scheme capitalizes on known physics and interrelated properties, such as gas diffusivity and solubility, both of which are closely tied to permeability. By amalgamating high-throughput generated simulation data with available experimental data for gas permeability, diffusivity, and solubility for various gases, we construct multi-task deep learning models. These models can simultaneously predict all three properties for all gases under consideration. With markedly enhanced predictive accuracy, particularly compared to traditional models reliant solely on experimental data for a singular property. This strategy underscores the potential of coupling high-throughput classical simulations with data fusion methodologies to yield state-of-the-art property predictors, especially when experimental data for targeted properties is scarce. Thermal diffusivity ; Solubility ; Permeability (electromagnetism) ; Fusion ; Task (project management) ; Petroleum engineering ; Computer science ; Materials science ; Artificial intelligence ; Thermodynamics ; Chemistry ; Geology ; Engineering ; Philosophy ; Physics ; Physical chemistry ; Systems engineering Machine Learning in Materials Science
Authors: Yue Ma | Yongzhen Wang | Shaoxiong Han | Yan Sun | Zhenming Cui | Pengyu Liu | Xiaomin Wang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Improving Ionic Conductivity of Garnet Solid-State Electrolytes Using Gradient Boosting Regression Optimized Machine Learning Gradient boosting ; Boosting (machine learning) ; Conductivity ; Regression ; Ionic conductivity ; Electrolyte ; Regression analysis ; Materials science ; Artificial intelligence ; Machine learning ; Computer science ; Statistics ; Mathematics ; Chemistry ; Random forest ; Electrode ; Physical chemistry Solid-state spectroscopy and crystallography ; Machine Learning and ELM ; Perovskite Materials and Applications
Authors: Tomomi Shimazaki | Masanori Tachikawa
Source: Chemical Physics Letters (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Density functional theory predictions of properties of bioactive molecules based on guided neural network approach using semi-empirical quantum chemistry Density functional theory ; Chemistry ; Quantum chemistry ; Computational chemistry ; Molecule ; Artificial neural network ; Quantum ; Quantum chemical ; Organic chemistry ; Computer science ; Machine learning ; Quantum mechanics ; Physics ; Supramolecular chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Guoqing Wang | Ben Gao | Gai Zhao | Haoyu Shi | Shuntao Fang | Yuzhen Liu
Source: Physica Scripta (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

Machine learning accelerated the prediction of mechanical properties of copper modified by TMDs based on molecular dynamics simulation Abstract In this study, we constructed a dataset of elastic modulus and ultimate stress for copper material enhanced by Transition Metal Dichalcogenides (TMDs) through Molecular Dynamics (MD) simulations. Subsequently, leveraging chemical insights, we selected appropriate descriptors and established machine learning prediction models for elastic modulus and ultimate stress, respectively. Finally, the performance of the machine learning models was evaluated using a test set. The results demonstrate excellent performance of the machine learning models in predicting material properties. This work presents a novel approach for efficient material screening, demonstrating the synergy between MD simulations and machine learning in advancing materials research and intelligent material selection platforms. Molecular dynamics ; Computer science ; Machine learning ; Elastic modulus ; Materials science ; Stress (linguistics) ; Set (abstract data type) ; Copper ; Artificial intelligence ; Composite material ; Physics ; Metallurgy Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Metal and Thin Film Mechanics
Authors: Jiaxin Tian | Haoyuan Li
Source: ChemRxiv (preprint, 2024-04-12)
Field: Materials Science — Materials Chemistry

Machine-Learning Discovered Crystallization Model for TwoDimensional Covalent Organic Frameworks: Towards Precise Control of the Crystal Quality The rational molecular design and experimental condition optimizations for two-dimensional co-valent organic frameworks (2D COFs) call for a crystallization model capable of capturing exper-imental time and size scales. However, accurately describing their crystallization process remains a significant challenge due to the presence of non-classical pathways. Here, we demonstrate the implementation of a machine-learning approach, overcoming the difficulties associated with bot-tom-up model derivation. The resulting model, referred to as NEgen1, establishes correlations among the induction time, nucleation rate, growth rate, material parameters, and common solu-tion synthesis conditions for 2D COFs that belong to the nucleation-elongation category. NEg-en1 represents the emergence of practical crystallization models for 2D COFs, enabling the direct calculation of their crystallization processes in both experimental times and sizes. The results elu-cidate the detailed competition between the nucleation and growth dynamics in solution, which has been inappropriately apprehended via classical, empirical models with assumptions invalid for 2D COFs. Importantly, we demonstrate the potential application of the NEgen1 model in opti-mizing the synthesis conditions, which has predominantly relied on empirical knowledge to date. The identification of conditions superior to those routinely used experimentally reveals a promis-ing strategy of gradually increasing monomer addition speed for growing large 2D COF crystals while maintaining a reasonable synthesis time. These results highlight the potential for systemati-cally improving the crystal quality of 2D COFs for wider applications. Crystallization ; Nucleation ; Computer science ; Crystal (programming language) ; Process (computing) ; Quality (philosophy) ; Crystal growth ; Artificial intelligence ; Biological system ; Materials science ; Chemistry ; Physics ; Crystallography ; Thermodynamics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Lijie Ding | Chi-Huan Tung | Zhiqiang Cao | Zekun Ye | Xiaodan Gu | Yan Xia | Wei‐Ren Chen | Changwoo Do
Source: arXiv (Cornell University) (preprint, 2024-10-31)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Profiling of Ladder Polymer Structure using Scattering Ladder polymers, known for their rigid, ladder-like structures, exhibit exceptional thermal stability and mechanical strength, positioning them as candidates for advanced applications. However, accurately determining their structure from solution scattering remains a challenge. Their chain conformation is largely governed by the intrinsic orientational properties of the monomers and their relative orientations, leading to a bimodal distribution of bending angles, unlike conventional polymer chains whose bending angles follow a unimodal Gaussian distribution. Meanwhile, traditional scattering models for polymer chains do not account for these unique structural features. This work introduces a novel approach that integrates machine learning with Monte Carlo simulations to address this challenge. We first develop a Monte Carlo simulation for sampling the configuration space of ladder polymers, where each monomer is modeled as a biaxial segment. Then, we establish a machine learning-assisted scattering analysis framework based on Gaussian Process Regression. Finally, we conduct small-angle neutron scattering experiments on a ladder polymer solution to apply our approach. Our method uncovers structural details of ladder polymers that conventional methods fail to capture. Profiling (computer programming) ; Materials science ; Polymer ; Scattering ; Computer science ; Physics ; Optics ; Composite material Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Handwritten Text Recognition Techniques
Authors: Jason Allen | Stephen A. Andrews | Kyle S. Hickmann
Source: Journal of Dynamic Behavior of Materials (article, 2024-09-24)
Field: Materials Science — Materials Chemistry

Predicting Material Strength Model Parameters Using a Machine Learning Based Approach Solid mechanics ; Materials science ; Composite material ; Machine learning ; Artificial intelligence ; Computer science High-Velocity Impact and Material Behavior ; Non-Destructive Testing Techniques ; Material Properties and Failure Mechanisms
Authors: Yuexuan Shu | Jiwei Chen | Beibei Xu | Zhengchang Liu | Hao Zheng | Fan Zhang | Weiqi Fu
Source: Advanced Intelligent Systems (article, 2024-08-01)
Field: Materials Science — Biomaterials

Biomimetic Synthesis of Nanosilica by Deep Learning‐Designed Peptides and Its Anti‐UV Application Biomimetic Synthesis In article 2300467, Weiqi Fu and co-workers use machine learning techniques to design peptides with silicifying functionality. Inspired by the exquisite nanosilica structures from nature, a deep learning model, based on antimicrobial peptide migration learning, is developed with the inputs of a comprehensive collection of silicifying peptides from diatoms to achieve the biomimetic synthesis of nanosilica. The newly designed silicified peptides could facilitate the development of new biosensors and drug delivery systems. [Image by Jiwei Chen and Mengsheng Xia.] Nanotechnology ; Chemistry ; Materials science Diatoms and Algae Research
Authors: Xiaoyu Zhang | Kai Gu | Wenchao Zhang | Jiyu He | Rongjie Yang
Source: ACS Applied Materials & Interfaces (article, 2024-07-07)
Field: Materials Science — Materials Chemistry

Synthesis of Sulfonated Phenylsilsesquioxanes Guided by Machine Learning Sulfonated octaphenylsilsesquioxane (SPOSS) has garnered significant interest due to its unique structural properties of containing the -SO<sub>3</sub>H group and its wide range of applications. This study introduces a novel approach to the synthesis of SPOSS, leveraging machine learning algorithms to explore new recipes and achieve higher -SO<sub>3</sub>H functionality. The focus was on synthesizing SPOSS with 2, 4, 6, and 8-SO<sub>3</sub>H functional groups on the phenyl group, marked as SPOSS-2, SPOSS-4, SPOSS-6, and SPOSS-8, respectively. The successful synthesis of SPOSS-8 was achieved by 5 training outputs based on the recipes of 21 sets of low-functionality (<4) SPOSS. The structure of SPOSS was confirmed using Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and time-of-flight mass spectrometry (MALDI-TOF MS). Machine learning analysis revealed that K<sub>2</sub>SO<sub>4</sub> is an important additive to improve the functionality of SPOSS. A synthetic mechanism was proposed and validated that K<sub>2</sub>SO<sub>4</sub> participated in the reaction to generate sulfur trioxide (SO<sub>3</sub>), a sulfonating agent with high reactivity. SPOSS shows thermal stability superior to octaphenylsilsesquioxane (OPS) according to thermogravimetric analysis (TGA) and TG-FTIR. Fourier transform infrared spectroscopy ; Thermogravimetric analysis ; Thermal stability ; Materials science ; Reactivity (psychology) ; Mass spectrometry ; Sulfur trioxide ; Chemical engineering ; Organic chemistry ; Chemistry ; Chromatography ; Engineering Silicone and Siloxane Chemistry ; Organometallic Compounds Synthesis and Characterization ; Synthesis and characterization of novel inorganic/organometallic compounds
Authors: Alex K. Chew | Mohammad Atif Faiz Afzal | Zach Kaplan | Eric M. Collins | Suraj Gattani | Mayank Misra | Anand Chandrasekaran | Karl Leswing | Mathew D. Halls
Source: ChemRxiv (preprint, 2024-11-10)
Field: Materials Science — Materials Chemistry

Leveraging High-throughput Molecular Simulations and Machine Learning for Formulation Design Formulations, or mixtures of chemical ingredients, are ubiquitous in materials science, but optimizing their properties remains challenging due to the vast design space. Computational approaches offer a promising solution to traverse this space while minimizing trial-and-error experimentation. Using high-throughput classical molecular dynamics simulations, we generated a comprehensive dataset of over 30,000 solvent mixtures to evaluate three machine learning approaches that connect molecular structure and composition to property: formulation descriptor aggregation (FDA), formulation graph (FG), and Set2Set-based method (FDS2S). Our results demonstrate that our new FDS2S approach outperforms other approaches in predicting simulation-derived properties. Formulation-property relationships can reveal important substructures and identify promising formulations at least two to three times faster than random guessing. The models show robust transferability to experimental datasets, accurately predicting properties across energy, pharmaceutical, and petroleum applications. Our research demonstrates the utility of high-throughput simulations and machine learning tools to design formulations with promising properties. Throughput ; Computer science ; Artificial intelligence ; Machine learning ; Computer architecture ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Zhengda Li | Xuyang Chen | Yang Wang
Source: ChemRxiv (preprint, 2024-06-07)
Field: Chemistry — Organic Chemistry

Deep Learning-Driven Prediction of Chemical Addition Patterns for Carboncones and Fullerenes Carboncones and fullerenes are exemplary π-conjugated carbon nanomaterials with unsaturated, positively curved surfaces, enabling the attachment of atoms or functional groups to enhance their physicochemical properties. However, predicting and understanding the addition patterns in functionalized carboncones and fullerenes are extremely challenging due to the formidable complexity of the regioselectivity exhibited in the adducts. Existing predictive models fall short in systems where the carbon molecular framework undergoes severe distortion upon high degrees of addition. Here, we propose an incremental deep learning approach to predict regioselectivity in the hydrogenation of carboncones and chlorination of fullerenes. Utilizing exclusively graph-based features, our deep neural network (DNN) models rely solely on atomic connectivity, without requiring 3D molecular coordinates as input or iterative optimization of them. This advantage inherently avoids the risk of obtaining chemically unreasonable optimized structures, enabling the handling of highly distorted adducts. The DNN models allow us to study regioselectivity in hydrogenated carboncones of C70H20 and C62H16, accommodating up to at least, 40 and 30 additional H atoms, respectively. Our approach also correctly predicts experimental addition patterns in C50Cl10 and C76Cln (n = 18, 24, and 28), whereas in the latter cases all other known methods have proven unsuccessful. Compared to our previously developed topology-based models, the DNN’s superior predictive power and generalization ability make it a promising tool for investigating complex addition patterns in similar chemical systems. Regioselectivity ; Fullerene ; Generalization ; Computer science ; Deep learning ; Graph ; Deep neural networks ; Artificial neural network ; Materials science ; Biological system ; Artificial intelligence ; Chemistry ; Mathematics ; Theoretical computer science ; Organic chemistry Fullerene Chemistry and Applications ; Machine Learning in Materials Science ; Synthesis and Properties of Aromatic Compounds
Authors: Muyang Li | Tuo Yao | Jian Liu | Ziyi Liu | Zhenguo Gao | Junbo Gong
Source: Mathematics (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis Deep learning-based in situ imaging and analysis for crystallization process are essential for optimizing product qualities, reducing experimental costs through real-time monitoring, and controlling the process. However, large and high-quality annotated datasets are required to train accurate models, which are time consuming. Therefore, we proposed a novel methodology that applied image synthesis neural networks to generate virtual information-rich images, enabling efficient and rapid dataset expansion while simultaneously reducing annotation costs. Experiments were conducted on the L-alanine crystallization process to obtain process images and to validate the proposed workflow. The proposed method, aided by interpolation augmentation and data warping augmentation to enhance data richness, utilized only 25% of the training annotations, consistently segmenting crystallization process images comparable to those models utilizing 100% of the training data annotations, achieving an average precision of nearly 98%. Additionally, based on the analysis of Kullback–Leibler divergence, the proposed method demonstrated excellent performance in extracting in situ information regarding aspect ratios and crystal size distributions during the crystallization process. Moreover, its ability to leverage expert labels with a four-fold enhanced efficiency holds great potential for advancing various applications in crystallization processes. Computer science ; Process (computing) ; Image warping ; Leverage (statistics) ; Workflow ; Artificial intelligence ; Crystallization ; Artificial neural network ; Data mining ; Pattern recognition (psychology) ; Engineering ; Database Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Image Retrieval and Classification Techniques
Authors: Muhammad Nouman | Richard B. Canty | Brent A. Koscher | Matthew A. McDonald | Klavs F. Jensen
Source: ChemRxiv (preprint, 2024-12-01)
Field: Chemistry — Physical and Theoretical Chemistry

General Chemically Intuitive Atom- and Bond-level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction We demonstrate the usefulness of general atom- and bond-level DFT descriptors to enhance the performance of neural networks for general reaction condition prediction. We treat reaction condition prediction as a multi-class classification task and report the performance of neural networks trained on 59,512 reactions with 283 distinct reaction condition classes and varying input embedding compositions. We show that by combining structural and general DFT descriptors in optimized ratios, models with input size up to 15% smaller than their purely structural counterparts can provide comparable recall, top-1 and top-3 accuracies. Moreover, we report improvements of up to 6%, 7% and 9% in weighted F1 score, top-1 accuracy and weighted recall, respectively, for neural networks trained on combined general DFT and structural descriptors when compared to purely structural models with equivalent architectures and input sizes. Remarkably, these results were achieved using a training set containing 267 times fewer data points than the one used for generating and embedding structural descriptors, despite both embedding strategies being similar unsupervised learning algorithms. Atom (system on chip) ; Computer science ; Artificial intelligence ; Machine learning ; Bond ; Chemistry ; Computational chemistry ; Parallel computing ; Economics Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Jie Peng | Liping Wang
Source: Journal of Physics Conference Series (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

The study of the convergence conditions of the deep-learning method in the Li<sub>10</sub>GeP<sub>2</sub>S<sub>12</sub> solid state electrolyte system Abstract The ionic conductivity of solid-state electrolytes at room temperature is crucial for commercializing lithium-ion batteries with solid-state electrolytes. Ab initio methods encounter a challenge due to their substantial computational resource demands. Classical molecular dynamics methods, on the other hand, are suitable for large-scale systems with simulation times reaching the nanosecond scale. However, they rely on empirical parameters in force fields, limiting their use to systems with well-established and extensively validated parameters, which is a constraint in studying new materials. A simulation approach combines ab initio simulations and classical molecular dynamics. Deepmd-kit, a deep learning tool, trains a tailored force field model using ab initio simulation data for the target system. However, as an approximate method, its reliability must be compared with ab initio results. As a data-sensitive method, the amount of data required to achieve the desired accuracy varies for different systems and must be tested accordingly. This paper performs convergence training for system size and simulation time concerning Li 10 GeP 2 S 12 solid-state electrolytes, establishing the convergence criteria for deep learning data in this system. Convergence (economics) ; Electrolyte ; Solid-state ; State (computer science) ; Computer science ; Materials science ; Chemistry ; Algorithm ; Physical chemistry ; Electrode Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Sadhana Barman | Harkishan Dua | Utpal Sarkar
Source: Journal of Physics Condensed Matter (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

Bandgap prediction of non-metallic crystals through machine learning approach The determination of bandgap is the heart of electronic structure of any material and is a crucial factor for thermoelectric performance of it. Due to large amount to data (features) that are related to bandgap are now a days available, it is possible to make use of machine learning (ML) approach to predict the bandgap of the material. The study commences by selecting the feature through Pearson correlation study between bandgap and various thermoelectric parameters in non-metallic crystals. Among the 42 parameters available in the dataset, the Seebeck coefficient and its corresponding temperatures show high correlation with the bandgap. With these three selected features we have used different ML models like multilinear regression, polynomial regression, random forest regression and support vector regression to predict the bandgap. Amongst the different ML models considered, random forest regression outperforms the other models to predict the bandgap with<i>R</i><sup>2</sup>value of 97.55% between actual bandgap and predicted bandgap. Materials science ; Metal ; Artificial intelligence ; Machine learning ; Computer science ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Semiconductor materials and interfaces