jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eslam G. Al-Sakkari | Ahmed Ragab | Mostafa Amer | Olumoye Ajao | Marzouk Benali | Daria C. Boffito | Hanane Dagdougui | Mouloud Amazouz
Source: Digital Chemical Engineering (article, 2024-12-13)
Field: Materials Science — Materials Chemistry

Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection Solubility ; Selection (genetic algorithm) ; Solvent ; Computer science ; Hildebrand solubility parameter ; Machine learning ; Process (computing) ; Chemistry ; Process engineering ; Biochemical engineering ; Artificial intelligence ; Thermodynamics ; Engineering ; Organic chemistry ; Physics Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Brook Wander | Muhammed Shuaibi | John R. Kitchin | Zachary W. Ulissi | C. Lawrence Zitnick
Source: arXiv (Cornell University) (preprint, 2024-05-03)
Field: Materials Science — Materials Chemistry

CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural network potential trained on the OC20 dataset, a related but different task, is able to find transition states energetically similar (within 0.1 eV) to density functional theory (DFT) 91% of the time with a 28x speedup. This speaks to the generalizability of the models, having never been explicitly trained on reactions, the machine learned potential approximates the potential energy surface well enough to be performant for this auxiliary task. We introduce the Open Catalyst 2020 Nudged Elastic Band (OC20NEB) dataset, which is made of 932 DFT nudged elastic band calculations, to benchmark machine learned model performance on transition state energies. To demonstrate the efficacy of this approach, we replicated a well-known, large reaction network with 61 intermediates and 174 dissociation reactions at DFT resolution (40 meV). In this case of dense NEB enumeration, we realize even more computational cost savings and used just 12 GPU days of compute, where DFT would have taken 52 GPU years, a 1500x speedup. Similar searches for complete reaction networks could become routine using the approach presented here. Finally, we replicated an ammonia synthesis activity volcano and systematically found lower energy configurations of the transition states and intermediates on six stepped unary surfaces. This scalable approach offers a more complete treatment of configurational space to improve and accelerate catalyst discovery. Artificial neural network ; Graph ; Transition (genetics) ; Computer science ; State (computer science) ; Power graph analysis ; Artificial intelligence ; Statistical physics ; Theoretical computer science ; Physics ; Algorithm ; Chemistry Machine Learning in Materials Science
Authors: Shannon R. Petersen | David Kohan Marzagão | Georgina L. Gregory | Yichen Huang | David A. Clifton | Charlotte K. Williams | Clive R. Siviour
Source: Angewandte Chemie International Edition (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

Property Prediction of Bio‐Derived Block Copolymer Thermoplastic Elastomers Using Graph Kernel Methods Increasing the diversity of bio-based polymers is needed to address the combined problems of plastic pollution and greenhouse gas emissions. The magnitude of the problems necessitates rapid discovery of new materials; however, identification of appropriate chemistries maybe slow using current iterative methods. Machine learning (ML) methods could significantly expedite new material discovery and property identification. Here, PolyAGM, a ML algorithm using graph kernel methods, is introduced and used to predict the properties of block copolymers and identify the responsible structural 'motifs'. It applies a "fingerprinting" method to convert Graph representations of polymers into numerical vectors. The Graphs explicitly encode the entire copolymer of atoms and bonds such that the sequencing of chemical features and polymer chain length are included, alongside relevant stereochemical information. PolyAGM gives predictions for both thermal and mechanical properties that are in good agreement with experimental measurements. This work focuses on predicting the properties of bio-derived ABA-block polymer thermoplastic elastomers, but the general fingerprinting technique of PolyAGM should be relevant to other application fields. Thermoplastic elastomer ; Copolymer ; Elastomer ; Polymer ; Materials science ; Block (permutation group theory) ; Thermoplastic ; Kernel (algebra) ; Computer science ; Algorithm ; Graph ; Polymer science ; Biological system ; Theoretical computer science ; Mathematics ; Composite material ; Combinatorics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Majid Rezaei | Sung Sakong | Axel Groß
Source: ACS Applied Materials & Interfaces (article, 2024-06-11)
Field: Materials Science — Materials Chemistry

Sodium Triflate Water-in-Salt Electrolytes in Advanced Battery Applications: A First-Principles-Based Molecular Dynamics Study Offering a compelling combination of safety and cost-effectiveness, water-in-salt (WiS) electrolytes have emerged as promising frontiers in energy storage technology. Still, there is a strong demand for research and development efforts to make these electrolytes ripe for commercialization. Here, we present a first-principles-based molecular dynamics (MD) study addressing in detail the properties of a sodium triflate WiS electrolyte for Na-ion batteries. We have developed a workflow based on a machine learning (ML) potential derived from ab initio MD simulations. As ML potentials are typically restricted to the interpolation of the data points of the training set and have hardly any predictive properties, we subsequently optimize a classical force field based on physics principles to ensure broad applicability and high performance. Performing and analyzing detailed MD simulations, we identify several very promising properties of the sodium triflate as a WiS electrolyte but also indicate some potential stability challenges associated with its use as a battery electrolyte. Trifluoromethanesulfonate ; Electrolyte ; Battery (electricity) ; Molecular dynamics ; Computer science ; Materials science ; Nanotechnology ; Computational chemistry ; Chemistry ; Thermodynamics ; Physics ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Anton Soria-López | María García-Martí | Enrique Barreiro | Juan C. Mejuto
Source: Tenside Surfactants Detergents (article, 2024-09-25)
Field: Chemistry — Analytical Chemistry

Ionic surfactants critical micelle concentration prediction in water/organic solvent mixtures by artificial neural network Abstract Critical micellar concentration (CMC) is a key physicochemical property of surfactants used to study their behaviour. This property is affected by factors such as temperature, pressure, pH, the type of organic solvent/water mixture, the chemical structure of the surfactants and the presence of electrolytes. Most of the existing studies in the literature have predicted the CMC under fixed conditions based on the chemical parameters of the surfactant. In this study, a machine learning approach using artificial neural network (ANN) models was used to estimate the CMC of some ionic surfactants. These models considered variables defining both the organic solvent-water mixture ( T , molecular weight, molar fraction and log P ) and the chemical structure of the surfactant (number of atoms of each element). A database consisting of a total of 258 CMC values for 10 ionic surfactants was collected from the literature. The ANN architecture consisting of an input layer with 12 neurons, an intermediate layer with 25 neurons and one neuron in the output layer is proposed. According to the results, the normalized ANN models provided the best statistical adjustments for the CMC prediction. These ANN models could be a promising method for CMC estimation. Chemistry ; Critical micelle concentration ; Organic solvent ; Micelle ; Solvent ; Artificial neural network ; Ionic liquid ; Thermodynamics of micellization ; Ionic bonding ; Organic chemistry ; Chemical engineering ; Chromatography ; Aqueous solution ; Artificial intelligence ; Ion Spectroscopy and Chemometric Analyses ; Surfactants and Colloidal Systems ; Analytical Chemistry and Chromatography
Authors: Yoonsu Shim | Incheol Jeong | Junpyo Hur | Hyoungjeen Jeen | Seung‐Taek Myung | Kang Taek Lee | Seungbum Hong | Jong Min Yuk | Chan‐Woo Lee
Source: Batteries & Supercaps (article, 2024-04-17)
Field: Materials Science — Polymers and Plastics

Data‐Driven Design of NASICON‐Type Electrodes Using Graph‐Based Neural Networks Abstract Sodium superionic conductor (NASICON)‐type cathode materials are considered promising candidates for high‐performance sodium‐ion batteries (SIBs) because of the abundance and low cost of raw materials. However, NASICON‐type cathodes suffer from low capacities. This limitation can be addressed through the activation of sodium‐excess phases, which can enhance capacities up to theoretical values. Thus, this paper proposes the use of transition metal (TM)‐substituted Na 3 V 2 (PO 4 ) 2 F 3 (NVPF) to induce sodium‐excess phases. To identify suitable doping elements, an inverse design approach is developed, combining machine learning prediction and density functional theory (DFT) calculations. Graph‐based neural networks are used to predict two crucial properties, i. e., the structural stability and voltage level. Results indicate that the use of TM‐substituted NVPF materials leads to about 150 % capacity enhancement with reduced time and resource requirements compared with the direct design approach. Furthermore, DFT calculations confirm improvements in cyclability, electronic conductivity, and chemical stability. The proposed approach is expected to accelerate the discovery of superior materials for battery electrodes. Fast ion conductor ; Artificial neural network ; Computer science ; Electrode ; Graph ; Materials science ; Artificial intelligence ; Chemistry ; Theoretical computer science ; Electrolyte Conducting polymers and applications
Authors: Kurudi V Vedavyasa | Ashok Kumar
Source: Advanced Quantum Technologies (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Classification Analysis of Transition Metal Compounds Using Quantum Machine Learning Abstract Quantum machine learning (QML) leverages the potential of machine learning (ML) to explore the subtle patterns in huge datasets of complex nature with quantum advantages. QML accelerates materials research with active screening of chemical space, identifying novel materials for practical applications, and classifying structurally diverse materials given their measured properties. This study analyzes the performance of three efficient quantum machine learning algorithms viz., variational quantum classifier (VQC), quantum support vector classifier (QSVC), and quantum neural networks (QNN) for distinguishing transition metal chalcogenides (TMCs) from transitional metal oxides (TMOs). By employing feature selection, classical machine learning achieves 100% accuracy whereas QML achieves the highest performance of 99% and 98% for test and train data respectively on QSVC. Further, to extend the QML models for structural and functional analysis of materials that cannot be inferred directly from the formula, stability analysis, and magnetic nature analysis on 1000 and 500 materials are performed, respectively. The stability analysis achieves 78% accuracy with QSVC and the magnetic nature analysis achieves 88% with QNN establishing the competence of QML models. This study proves that QML models are remarkable in materials classification and analysis which fuels the task of materials discovery in the future. Quantum ; Transition metal ; Transition (genetics) ; Materials science ; Chemistry ; Artificial intelligence ; Computer science ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum Computing Algorithms and Architecture
Authors: Vikram Sudarshan | Warren D. Seider
Source: Computers & Chemical Engineering (article, 2024-11-15)
Field: Materials Science — Materials Chemistry

Advancing machine learning in Industry 4.0: Benchmark framework for rare-event prediction in chemical processes Benchmark (surveying) ; Event (particle physics) ; Artificial intelligence ; Rare events ; Computer science ; Machine learning ; Industrial engineering ; Engineering ; Mathematics ; Physics ; Geology ; Statistics Machine Learning in Materials Science ; Fault Detection and Control Systems ; Data Quality and Management
Authors: Yuling Zhou | Qian Li | Wei Zhou | Huaijuan Zang | Liangfeng Xu | Yongsheng Ren | Jiajia Xu | Shu Zhan | Wenhui Ma
Source: Computational Materials Science (article, 2024-03-26)
Field: Materials Science — Materials Chemistry

Reinforce crystal material property prediction with comprehensive message passing via deep graph networks Property (philosophy) ; Crystal structure prediction ; Graph ; Computer science ; Crystal (programming language) ; Materials science ; Theoretical computer science ; Crystal structure ; Chemistry ; Crystallography ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Welding Techniques and Residual Stresses
Authors: Alauddin Ahmed | Karabi Nath | Adam J. Matzger | Donald J. Siegel
Source: ACS Applied Materials & Interfaces (article, 2024-10-02)
Field: Chemistry — Inorganic Chemistry

Machine Learning Predictions of Methane Storage in MOFs: Diverse Materials, Multiple Operating Conditions, and Reverse Models A machine learning (ML) model is developed for predicting useable methane (CH<sub>4</sub>) capacities in metal-organic frameworks (MOFs). The model applies to a wide variety of MOFs, including those with and without open metal sites, and predicts capacities for multiple pressure swing conditions. Despite its wider applicability, the model requires only 5 measurable structural features as input, yet achieves accuracies that surpass less-general models. Application of the model to a database of more than a million hypothetical MOFs identified several hundred whose capacities surpass that of the benchmark MOF, UMCM-152. Guided by the computational predictions, one of the promising candidates, UMCM-153, was synthesized and demonstrated to achieve superior volumetric capacity for CH<sub>4</sub>. Feature importance analyses reveal that pore volume and gravimetric surface area are the most important features for predicting CH<sub>4</sub> capacity in MOFs. Finally, a reverse ML model is demonstrated. This model predicts the set of elementary MOF structural properties needed to achieve a desired CH<sub>4</sub> capacity for a prescribed operating condition. Materials science ; Methane ; Process engineering ; Nanotechnology ; Biochemical engineering ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: R. W. Lee | Yong‐Hoon Kim
Source: npj Computational Materials (article, 2024-10-24)
Field: Materials Science — Materials Chemistry

Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution (ρ) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can shorten or bypass the SCF loop represents a critical question in electronic structure theory from both practical and fundamental standpoints. Herein, a machine learning strategy, DeepSCF, is presented in which the map between the SCF ρ and the initial guess density (ρ0) constructed by the summation of neutral atomic densities is learned using 3D convolutional neural networks (CNNs). High accuracy and transferability of DeepSCF are achieved by first encoding ρ0 on a 3D grid and then expanding the input features to include atomic fingerprints beyond ρ0. The prediction of the residual density (δρ) rather than ρ itself is targeted, and given that δρ is indicative of chemical bonding information, a dataset of small-sized organic molecules featuring diverse bonding characters is adopted. The fidelity of DeepSCF is finally enhanced by subjecting the atomic geometries of the dataset to random rotations and strains. The effectiveness of DeepSCF is demonstrated using a complex carbon nanotube-based DNA sequencer model. This work evidences that the nearsightedness in electronic structure can be optimally represented via the spatial locality in CNNs, offering insight into the success of various machine learning-based atomistic materials simulations. Computer science ; Convolutional neural network ; Grid ; Artificial intelligence ; Electron density ; Pattern recognition (psychology) ; Electron ; Physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Rodolfo Ledesma | Christopher J. Wohl | Brian W. Grimsley
Source: Polymer Engineering and Science (article, 2024-06-20)
Field: Materials Science — Materials Chemistry

Crystallization kinetics analysis and modeling of aerospace <scp>PAEK</scp> materials Abstract Polyaryletherketone (PAEK) thermoplastic composite materials have received increasing attention for the manufacturing of composite structures. One manufacturing process of great interest for PAEK composite structures is automated fiber placement, which can increase the throughput of complex part fabrication and reduce the use of autoclaves for assembly of composite parts. Understanding the thermal effects of the material allows the control and adjustment of manufacturing variables to assure the integrity of the composite structure. PAEK resins and carbon fiber reinforced composites have been analyzed using differential scanning calorimetry to study crystallization kinetics. Isothermal and non‐isothermal tests were conducted, and the time‐dependent crystallization kinetics were modeled using the Velisaris and Seferis model for isothermal processes and the Gaussian process regression method for non‐isothermal processes. The results show the influence of the thermal conditions and the chemical structures considering the ketone content, and the effects of the presence of the carbon fibers on crystallization kinetics. The models utilized describe with high accuracy the time‐dependent crystallization kinetics of PAEK composite materials. An artificial neural network application was also implemented using the non‐isothermal PAEK composite models for further prediction of relative volume crystallinity and crystallization rate. Highlights Polyaryletherketone (PAEK) materials crystallize differently depending on the crystallization conditions. Carbon fibers influence the crystallization response in PAEK polymers. Machine learning was used to characterize crystallization kinetics. Activation energies were obtained for different thermal treatments. Artificial neural network tool was developed for PAEK composites in non‐isothermal regimes. Kinetics ; Aerospace ; Crystallization ; Materials science ; Chemical engineering ; Engineering ; Aerospace engineering ; Physics Thermal and Kinetic Analysis ; Polymer crystallization and properties ; Energetic Materials and Combustion
Authors: Christopher Blais | Chao Xu | Richard H. West
Source: The Journal of Physical Chemistry C (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification of Linear Scaling, Machine Learning, and Density Functional Theory Derived Thermodynamics for the Catalytic Partial Oxidation of Methane on Rhodium Accurate and complete microkinetic models (MKMs) are powerful for anticipating the behavior of complex chemical systems at different operating conditions. In heterogeneous catalysis, they can be further used for the rapid development and screening of new catalysts. Density functional theory (DFT) is often used to calculate the parameters used in MKMs with relatively high fidelity. However, given the high cost of DFT calculations for adsorbates in heterogeneous catalysis, linear scaling relations (LSRs) and machine learning (ML) models were developed to give rapid estimates of the parameters in MKM. Regardless of the method, few studies have attempted to quantify the uncertainty in catalytic MKMs, as the uncertainties are often orders of magnitude larger than those for gas phase models. This study explores uncertainty quantification and Bayesian Parameter Estimation for thermodynamic parameters calculated by DFT, LSRs, and GemNet-OC, a ML model developed under the Open Catalyst Project. A model for catalytic partial oxidation of methane (CPOX) on Rhodium was chosen as a case study, in which the model's thermodynamic parameters and their associated uncertainties were determined using DFT, LSR, and GemNet-OC. Markov Chain Monte Carlo coupled with Ensemble Slice Sampling was used to sample the highest probability density (HPD) region of the posterior and determine the maximum of the a posteriori (MAP) for each thermodynamic parameter included. The optimized microkinetic models for each of the three estimation methods had quite similar mechanisms and agreed well with the experimental data for gas phase mole fractions. Exploration of the HPD region of the posterior further revealed that adsorbed hydroxide and oxygen likely bind on facets other than Rhodium 111. The demonstrated workflow addresses the issue of inaccuracies arising from the integration of data from multiple sources by considering both experimental and computational uncertainties, and further reveals information about the active site that would not have been discovered without considering the posterior. Chemistry ; Monte Carlo method ; Density functional theory ; Scaling ; Thermodynamics ; Uncertainty quantification ; Statistical physics ; Methane ; Applied mathematics ; Physics ; Mathematics ; Statistics ; Computational chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science