Machine Learning-Assisted Discovery of Propane-Selective Metal–Organic Frameworks

Journal of the American Chemical Society, Год журнала: 2024, Номер 146(10), С. 6955 - 6961

Опубликована: Фев. 29, 2024

Machine learning is gaining momentum in the prediction and discovery of materials for specific applications. Given abundance metal–organic frameworks (MOFs), computational screening existing MOFs propane/propylene (C3H8/C3H6) separation could be equally important developing new MOFs. Herein, we report a machine learning-assisted strategy C3H8-selective from CoRE MOF database. Among four algorithms applied learning, random forest (RF) algorithm displays highest degree accuracy. We experimentally verified identified top-performing (JNU-90) with its benchmark selectivity performance directly producing C3H6. Considering excellent hydrolytic stability, JNU-90 shows great promise energy-efficient C3H8/C3H6. This work may accelerate development challenging separations.

Язык: Английский

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Interpretable Machine‐Learning and Big Data Mining to Predict Gas Diffusivity in Metal‐Organic Frameworks

Advanced Science, Год журнала: 2023, Номер 10(21)

Опубликована: Май 11, 2023

Abstract For gas separation and catalysis by metal‐organic frameworks (MOFs), diffusion has a substantial impact on the process' overall rate, so it is necessary to determine molecular behavior within MOFs. In this study, an interpretable machine learing (ML) model, light gradient boosting (LGBM), trained predict diffusivity selectivity of 9 gases (Kr, Xe, CH 4 , N 2 H S, O CO He). these gases, LGBM displays high accuracy (average R = 0.962) superior extrapolation for C 6 . And model calculation five orders magnitude faster than dynamics (MD) simulations. Subsequently, using interactive desktop application developed that can help researchers quickly accurately calculate molecules in porous crystal materials. Finally, authors find difference polarizability ( ΔPol ) key factor governing combining with Shapley additive explanation (SHAP). By ML, optimal MOFs are selected separating binary mixtures methanation. This work provides new direction exploring structure‐property relationships realizing rapid diffusivity.

Язык: Английский

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Machine learning strategies for small sample size in materials science

Science China Materials, Год журнала: 2025, Номер unknown

Опубликована: Янв. 2, 2025

Язык: Английский

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A portable europium complex-loaded fluorescent test paper combined with smartphone analysis for the on-site and visual detection of mancozeb in food samples

Food Chemistry, Год журнала: 2024, Номер 458, С. 140311 - 140311

Опубликована: Июль 2, 2024

Язык: Английский

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Leveraging Artificial Intelligence to Enhance Port Operation Efficiency

Polish Maritime Research, Год журнала: 2024, Номер 31(2), С. 140 - 155

Опубликована: Июнь 1, 2024

Abstract Maritime transport forms the backbone of international logistics, as it allows for transfer bulk and long-haul products. The sophisticated planning required this form transportation frequently involves challenges such unpredictable weather, diverse types cargo kinds, changes in port conditions, all which can raise operational expenses. As a result, accurate projection ship’s total time spent port, anticipation potential delays, have become critical effective activity management. In work, we aim to develop management system based on enhanced prediction classification algorithms that are capable precisely forecasting lengths ship stays delays. On both training testing datasets, XGBoost model was found consistently outperform alternative approaches terms RMSE, MAE, R2 values turnaround waiting period models. When used model, had lowest RMSE 1.29 during 0.5019 testing, also achieved MAE 0.802 0.391 testing. It highest 0.9788 0.9933 Similarly, outperformed random forest decision tree models, with greatest phases.

Язык: Английский

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Accelerated Discovery of Metal–Organic Frameworks for CO₂ Capture by Artificial Intelligence

Industrial & Engineering Chemistry Research, Год журнала: 2023, Номер 63(1), С. 37 - 48

Опубликована: Дек. 25, 2023

The existence of a very large number porous materials is great opportunity to develop innovative technologies for carbon dioxide (CO2) capture address the climate change problem. On other hand, identifying most promising adsorbent and membrane candidates using iterative experimental testing brute-force computer simulations challenging due enormous variety materials. Artificial intelligence (AI) has recently been integrated into molecular modeling materials, specifically metal–organic frameworks (MOFs), accelerate design discovery high-performing adsorbents membranes CO2 adsorption separation. In this perspective, we highlight pioneering works in which AI, simulations, experiments have combined produce exceptional MOFs MOF-based composites that outperform traditional capture. We outline future directions by discussing current opportunities challenges field harnessing experiments, theory, AI accelerated

Язык: Английский

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Olfactory Diagnosis Model for Lung Health Evaluation Based on Pyramid Pooling and SHAP-Based Dual Encoders

ACS Sensors, Год журнала: 2024, Номер 9(9), С. 4934 - 4946

Опубликована: Сен. 9, 2024

This study introduces a novel deep learning framework for lung health evaluation using exhaled gas. The synergistically integrates pyramid pooling and dual-encoder network, leveraging SHapley Additive exPlanations (SHAP) derived feature importance to enhance its predictive capability. is specifically designed effectively distinguish between smokers, individuals with chronic obstructive pulmonary disease (COPD), control subjects. structure aggregates multilevel global information by features at four scales. SHAP assesses from the eight sensors. Two encoder architectures handle different sets based on their importance, optimizing performance. Besides, model's robustness enhanced sliding window technique white noise augmentation original data. In 5-fold cross-validation, model achieved an average accuracy of 96.40%, surpassing that single 10.77%. Further optimization filters in transformer convolutional layer size module increased 98.46%. offers efficient tool identifying effects smoking COPD, as well approach utilizing technology address complex biomedical issues.

Язык: Английский

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Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Applied Physics Letters, Год журнала: 2024, Номер 124(20)

Опубликована: Май 13, 2024

Metal-organic frameworks (MOFs) and covalent organic (COFs) have great potential to be used as porous adsorbents membranes achieve high-performance methane purification. Although the continuous increase in number diversity of MOFs COFs is a opportunity for discovery novel with superior performances, evaluating such vast materials quickest most effective manner requires development computational approaches. High-throughput screening based on molecular simulations has been extensively identify promising However, enormous ever-growing material space necessitates more efficient approaches terms time effort. Combining data science recently accelerated optimal MOF COF purification revealed hidden structure–performance relationships. In this perspective, we highlighted recent developments combining high-throughput machine learning accurately among thousands candidates separating from other gases including acetylene, carbon dioxide, helium, hydrogen, nitrogen. After providing brief overview topic, reviewed pioneering contributions field discussed current opportunities challenges that need direct our efforts design adsorbent membrane materials.

Язык: Английский

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Metal–Organic Frameworks through the Lens of Artificial Intelligence: A Comprehensive Review

Langmuir, Год журнала: 2024, Номер 40(42), С. 21957 - 21975

Опубликована: Окт. 9, 2024

Metal–organic frameworks (MOFs) are a class of hybrid porous materials that have gained prominence as noteworthy material with varied applications. Currently, MOFs in extensive use, particularly the realms energy and catalysis. The synthesis these poses considerable challenges, their computational analysis is notably intricate due to complex structure versatile applications field science. Density functional theory (DFT) has helped researchers understanding reactions mechanisms, but it costly time-consuming requires bigger systems perform calculations. Machine learning (ML) techniques were adopted order overcome problems by implementing ML data sets for synthesis, structure, property predictions MOFs. These fast, efficient, accurate do not require heavy computing. In this review, we discuss models used MOF incorporation artificial intelligence (AI) predictions. advantage AI would accelerate research, synthesizing novel multiple properties oriented minimum information.

Язык: Английский

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Understanding CO adsorption in MOFs combining atomic simulations and machine learning

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 22, 2024

Abstract This study introduces a computational method integrating molecular simulations and machine learning (ML) to assess the CO adsorption capacities of synthesized hypothetical metal–organic frameworks (MOFs) at various pressures. After extracting structural, chemical, energy-based features MOFs (hMOFs), we conducted compute in used these simulation results train ML models for predicting hMOFs. Results showed that uptakes hMOFs are between 0.02–2.28 mol/kg 0.45–3.06 mol/kg, respectively, 1 bar, 298 K. At low pressures (0.1 bar), Henry’s constant is most dominant feature, whereas structural properties such as surface area porosity more influential determining high pressure (10 bar). Structural chemical analyses revealed with narrow pores (4.4–7.3 Å), aromatic ring-containing linkers carboxylic acid groups, along metal nodes Co, Zn, Ni achieve bar. Our approach evaluated ~ 100,000 MOFs, extensive diverse set studied capture thus far, robust alternative computationally demanding iterative experiments.

Язык: Английский

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