jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jianfeng Liao
Source: Journal of Experimental Nanoscience (article, 2023-10-17)
Field: Materials Science — Materials Chemistry

Machine learning aided evaluation and design based on polymer membrane materials With the acceleration of the global modern industrialisation process and the increasingly serious environmental problems, the development of low energy consumption, high efficiency electrochemical energy conversion equipment and separation system has become a research hotspot in the scientific and industrial circles. At present, machine learning has become an important research method to explore and expand two-dimensional material family. Traditional experimental and computational methods have low fault tolerance when studying two-dimensional materials, which requires a lot of time and research and development costs. Machine learning, due to its powerful data processing capability and flexible algorithm model, can help reduce the time and cost of discovering and understanding two-dimensional materials, and can effectively predict and expand two-dimensional material systems based on data and explore their potential for experimental synthesis and application. This paper will focus on the methods of machine learning, the exploration of machine learning in 2D material design and synthesis, and the exploration of machine learning in 2D material properties and applications. Finally, this paper uses ML algorithm to test the synthesised polymer. The experimental data points and prediction data points have relatively good consistency with each other, which indicates that ML model can be used as a prediction tool to identify the undeveloped polymer for gas separation. Computer science ; Machine learning ; Artificial intelligence ; Energy consumption ; Process (computing) ; Process engineering ; Materials science ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Covalent Organic Framework Applications
Authors: Bin Xing | Timothy J. Rupert | Xiaoqing Pan | Penghui Cao
Source: arXiv (Cornell University) (preprint, 2023-04-06)
Field: Materials Science — Materials Chemistry

Neural Network Kinetics for Exploring Diffusion Multiplicity and Chemical Ordering in Compositionally Complex Materials Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. Local chemical complexity in compositionally complex alloys poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. Using this method, we study the temperature-dependent local chemical ordering in a refractory Nb-Mo-Ta alloy and reveal a critical temperature at which the B2 order reaches a maximum. Our atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden. Nucleation ; Randomness ; Chemical physics ; Diffusion ; Statistical physics ; Multiplicity (mathematics) ; Materials science ; Thermodynamics ; Chemistry ; Physics ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nuclear Materials and Properties
Authors: Susumu Fujii | Yuta Shimizu | Junji Hyodo | Akihide Kuwabara | Yoshihiro Yamazaki
Source: ChemRxiv (preprint, 2023-06-18)
Field: Materials Science — Materials Chemistry

Discovery of unconventional proton-conducting inorganic solids via defect-chemistry-trained, interpretable machine learning High-throughput computational screening and machine learning holds significant potential for exploring diverse chemical compositions and discovering novel inorganic solids. However, the complexity of point defects, which occur in all inorganic solids and are often crucial to their functionality and synthesizability, presents significant challenges. Here, we present a defect-chemistry-trained, interpretable machine learning approach, designed to accelerate the exploration and discovery of unconventional proton-conducting inorganic solid electrolytes. By considering dopant dissolution and hydration reactions, our machine learning models provide quantitative predictions and physical interpretations for synthesizable host–dopant combinations with hydration capabilities across various structures. Utilizing these insights, two unconventional proton conductors, Pb-doped Bi12SiO20 sillenite and eulytite-type Sr-doped Bi4Ge3O12, are discovered in the first two synthesis trials. Notably, the Pb-doped Bi12SiO20 represents an unprecedented class of proton-conducting electrolyte composed solely of groups 14 and 15 cations and featuring a sillenite structure. It exhibits unique and fast three-dimensional proton conduction along a loosely bonded BiO5 network. This study demonstrates an efficient approach for exploring novel inorganic materials. Proton ; Dopant ; Electrolyte ; Doping ; Dissolution ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Physics ; Physical chemistry ; Optoelectronics ; Electrode Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Nithin Lalith | Aayush R. Singh | Joseph A. Gauthier
Source: ChemRxiv (preprint, 2023-12-05)
Field: Materials Science — Materials Chemistry

The importance of reaction energy in predicting chemical reaction barriers with machine learning models Improving our fundamental understanding of complex heterocatalytic processes increasingly relies on electronic structure simulations and microkinetic models based on calculated energy differences. In particular, calculation of activation barriers, usually achieved through compute-intensive saddle point search routines, remains a serious bottleneck in understanding trends in catalytic activity for highly branched reaction networks. Although the well-known Brønsted-Evans-Polyani (BEP) scaling – a one- dimensional linear regression model – has been widely applied in such microkinetic models, they still rely on calculated reaction energies and may not generalize beyond a single facet on a single class of materials, e.g., a terrace sites on transition metals. For highly branched and shallow reaction networks, such as electrochemical CO2 reduction or waste remediation, calculating even reaction energies on many surfaces can become computationally intractable due to the combinatorial explosion of states that must be considered. Here, we report a new database of over 500 dehydrogenation activation barriers calculated on a range of molecules and transition metal surfaces using a one- dimensional potential energy surface scan, benchmarked to a more rigorous transition state search routine. We investigate the importance of the reaction energy when predicting activation barriers using linear and nonlinear machine learning (ML) models and find that inclusion of the reaction energy significantly improves both classes of ML models. By excluding the reaction energy as a feature, even our most complex nonlinear model fails to significantly outperform the simplest BEP scaling when predicting activation barriers on new systems. Our results suggest that the reaction energy is a critical feature to consider when building models to predict activation barriers, indicating that efforts to reliably predict reaction energies reliably through, e.g., the Open Catalyst Project, will be an important route to effective model development for more complex systems. Saddle point ; Bottleneck ; Reaction rate ; Scaling ; Potential energy surface ; Dehydrogenation ; Transition state ; Computer science ; Chemistry ; Statistical physics ; Catalysis ; Molecule ; Physics ; Mathematics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Tejus Shastry | Yasemin Basdogan | Zhen‐Gang Wang | Sanat K. Kumar | Matthew R. Carbone
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Spectroscopy

Machine Learning-Based Discovery of Molecular Descriptors that Control Polymer Gas Permeation Permeation ; Polymer ; Control (management) ; Chromatography ; Computer science ; Chemistry ; Materials science ; Artificial intelligence ; Organic chemistry ; Membrane Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Machine Learning in Materials Science
Authors: Tejkiran Pindi Jayakumar | Sumanaspurthi Puvvadi Suresh Babu | Thanh Nhat Nguyen | Son Dinh Le | Toshiaki Taniike
Source: ChemRxiv (preprint, 2023-05-16)
Field: Materials Science — Materials Chemistry

Exploration of Ethanol-to-Butadiene Catalysts by High-Throughput Experimentation and Machine Learning Meeting the high demand for synthetic rubbers and elastomers requires a cost-effective source of 1,3-butadiene, making on-purpose synthesis from ethanol a crucial process. To tackle this challenge, a comprehensive multi-elemental approach was employed in conjunction with a genetic algorithm-driven high-throughput experimentation technique. The study explored a vast catalyst space, comprising up to 14 elements - including Mg, Al, Cr, Ni, Cu, Zn, Ga, Y, Zr, Nb, Mo, Ag, La, and Hf - co-supported on mesoporous silica, with the aims to discover effective combinations and understand the roles of each element in the overall reaction mechanism. The discovered efficient catalysts were composed of primarily Mg, Zn, Y, and Hf, and secondary Zr, Nb, and La. Such highly multi-elemental design was suggested to achieve a balance for the complex reactions of ETB, where efficient conversion of acetaldehyde to butadiene while minimizing the production of ethylene was critical. The highest yield obtained was 73% for butadiene. Through the application of machine learning techniques on the collected dataset, we successfully derived important insights related to catalyst design and catalysis. In particular, we proposed a visualization method to facilitate a deeper understanding of the role of each element in the overall catalysis. 1,3-Butadiene ; Catalysis ; Throughput ; Acetaldehyde ; Yield (engineering) ; Mesoporous material ; Materials science ; Computer science ; Ethanol ; Process engineering ; Chemical engineering ; Biochemical engineering ; Nanotechnology ; Chemistry ; Organic chemistry ; Engineering ; Metallurgy Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Hang Xiao | Rong Li | Xiaoyang Shi | Yan Chen | Liangliang Zhu | Xi Chen | Lei Wang
Source: Research Square (preprint, 2023-06-16)
Field: Materials Science — Materials Chemistry

An invertible, invariant crystallographic representation for inverse design of solid-state materials using generative deep learning Invertible matrix ; Inverse ; Generative grammar ; Invariant (physics) ; Representation (politics) ; Generative model ; Mathematics ; Pure mathematics ; Artificial intelligence ; Computer science ; Geometry ; Mathematical physics ; Political science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Titanium Alloys Microstructure and Properties
Authors: Jeļena Fiošina | Philipp Sievers | Marco Drache | Sabine Beuermann
Source: ChemRxiv (preprint, 2023-04-06)
Field: Materials Science — Materials Chemistry

Polymer Reaction Engineering meets Explainable Machine Learning Due to the complicated polymerization technique and statistical composition of the polymer, tailoring its characteristics is a challenging task. Modeling of the polymerizations can contribute to deeper insights into the process. This study applies state-of-the-art machine learning (ML) methods for modeling and reverse engineering of polymerization processes. ML methods (random forest, XGBoost and CatBoost) are trained on data sets generated by an in house developed kinetic Monte Carlo simulator. The applied ML models predict monomer concentration, average molar masses and full molar mass distributions with excellent accuracy (R2 > 0.96). Reverse engineering results delivering the polymerization recipe for a targeted molar mass distribution are less accurate, but still only minor deviations from the targeted molar mass distribution are seen. The influences of the input variables in ML models obtained by explainability methods correspond to the expert expectations. Molar mass ; Polymerization ; Polymer ; Monomer ; Computer science ; Process (computing) ; Monte Carlo method ; Molar mass distribution ; Random forest ; Molar concentration ; Biological system ; Materials science ; Algorithm ; Chemistry ; Machine learning ; Mathematics ; Statistics ; Organic chemistry Machine Learning in Materials Science
Authors: Anton Ashuiev | Anna Giorgia Nobile | D. Trummer | Daniel Klose | С. А. Гуда | Оlga V. Safonova | Christophe Copéret | Alexander A. Guda | Gunnar Jeschke
Source: ChemRxiv (preprint, 2023-09-07)
Field: Materials Science — Materials Chemistry

Active Sites in Cr(III)-based Ethylene Polymerization Catalysts from Machine Learning-Supported XAS and EPR Spectroscopy The ethylene polymerization Phillips catalyst has been employed for decades and is central to the polymer industry. While Cr(III) alkyl species are proposed to be the propagating sites, there is so far no direct experimental evidence for such proposal. In this work, by coupling Surface organometallic chemistry (SOMC), EPR spectroscopy, and machine learning-supported XAS studies, we have studied the electronic structure of well-defined silica-supported Cr(III) alkyls, and identified the presence of several surface species from high to low spin Cr(III), associated with different coordination environments. Notably, low-spin Cr(III) sites are shown to participate in ethylene polymerization, indicating that similar Cr(III) alkyl species could be involved in the related Phillips catalyst. X-ray absorption spectroscopy ; Polymerization ; Ethylene ; Electron paramagnetic resonance ; Alkyl ; Catalysis ; Organometallic chemistry ; Polymer ; Spectroscopy ; Polymer chemistry ; Materials science ; Absorption spectroscopy ; Physical chemistry ; Chemistry ; Photochemistry ; Organic chemistry ; Physics ; Nuclear magnetic resonance ; Optics Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis
Authors: Miguel Urbiztondo | Sergio G. Rodrigo | Said Hamad
Source: The Journal of Physical Chemistry C (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Energy Mapping of Chlorine Effects in an Epoxidation Reaction Catalyzed by a Silver–Copper Oxide Nanocatalyst Deep learning is poised to revolutionize the field of heterogeneous catalysis. In this study, we harness its potential to predict energy values across a catalyst surface, a task traditionally relegated to computationally intensive density functional theory (DFT). We propose a novel deep learning approach to construct an exhaustive energy map, pinpointing the optimal locations for adsorbed chlorine in the ethylene epoxidation reaction. Leveraging the power of trained neural networks, we achieved a staggering reduction in computational time, cutting down the duration of energy calculations by over 50 million times compared with traditional methods. This groundbreaking integration of artificial intelligence not only accelerates this process but also effectively surpasses the limitations of conventional methods. By highlighting the transformative potential of deep learning in catalysis, this research paves the way for future studies and stands to revolutionize efficiency in the chemical industry, fostering an urgent need to delve deeper into the implications and applications of this technology. Chlorine ; Copper ; Catalysis ; Copper oxide ; Oxide ; Energy (signal processing) ; Chemistry ; Silver oxide ; Inorganic chemistry ; Materials science ; Nanotechnology ; Chemical engineering ; Metallurgy ; Physics ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Cheng Zeng | Andrew Neils | Jack Lesko | Nathan Post
Source: arXiv (Cornell University) (preprint, 2023-07-12)
Field: Materials Science — Materials Chemistry

Machine learning accelerated discovery of corrosion-resistant high-entropy alloys Corrosion has a wide impact on society, causing catastrophic damage to structurally engineered components. An emerging class of corrosion-resistant materials are high-entropy alloys. However, high-entropy alloys live in high-dimensional composition and configuration space, making materials designs via experimental trial-and-error or brute-force ab initio calculations almost impossible. Here we develop a physics-informed machine-learning framework to identify corrosion-resistant high-entropy alloys. Three metrics are used to evaluate the corrosion resistance, including single-phase formability, surface energy and Pilling-Bedworth ratios. We used random forest models to predict the single-phase formability, trained on an experimental dataset. Machine learning inter-atomic potentials were employed to calculate surface energies and Pilling-Bedworth ratios, which are trained on first-principles data fast sampled using embedded atom models. A combination of random forest models and high-fidelity machine learning potentials represents the first of its kind to relate chemical compositions to corrosion resistance of high-entropy alloys, paving the way for automatic design of materials with superior corrosion protection. This framework was demonstrated on AlCrFeCoNi high-entropy alloys and we identified composition regions with high corrosion resistance. Machine learning predicted lattice constants and surface energies are consistent with values by first-principles calculations. The predicted single-phase formability and corrosion-resistant compositions of AlCrFeCoNi agree well with experiments. This framework is general in its application and applicable to other materials, enabling high-throughput screening of material candidates and potentially reducing the turnaround time for integrated computational materials engineering. Corrosion ; Formability ; High entropy alloys ; Materials science ; Entropy (arrow of time) ; Random forest ; Metallurgy ; Machine learning ; Computer science ; Microstructure ; Thermodynamics ; Physics Machine Learning in Materials Science ; High Entropy Alloys Studies ; Nuclear Materials and Properties
Authors: Xiangyu Kong | Bohao Xu
Source:  (article, 2023-10-25)
Field: Materials Science — Materials Chemistry

Prediction of photoelectric conversion efficiency of organic photovoltaic materials based on deep learning The energy crisis has gradually been reflected in all aspects. It is urgent to develop efficient and clean new energy to reduce the dependence on fossil energy. As a kind of material which can directly convert solar energy or other light energy into electric energy, organic photovoltaic material is becoming a kind of low-carbon energy material with great application prospect. While machine learning can improve the efficiency of material design in the search for new high performance organic photovoltaic materials, its predictive power is severely limited by developing and selecting descriptors. An end-to-end deep learning model has been constructed to predict the photoelectric conversion efficiency of organic photovoltaic materials using algorithms such as cyclic neural networks, convolutional neural networks, and graph neural networks. The constructed model can directly extract the structural information of compounds from Simplified Molecular Input Line Entry System (SMILES) symbols, molecular images and molecular graph networks, without the need to manually develop and select descriptors. Our model can not only accurately predict the photoelectric conversion efficiency of organic photovoltaic materials (the determination coefficients of the optimal model and the test set prediction are both > 0.73) and can identify key structural features that affect conversion efficiency. The research results can provide solutions for the design of new photovoltaic materials. Photovoltaic system ; Computer science ; Convolutional neural network ; Artificial neural network ; Photoelectric effect ; Deep learning ; Efficient energy use ; Artificial intelligence ; Energy conversion efficiency ; Solar energy ; Process engineering ; Materials science ; Electrical engineering ; Engineering ; Optoelectronics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Economic and Technological Innovation
Authors: Zhihao Dong | Jie Feng | Yujin Ji | Youyong Li
Source: ChemRxiv (preprint, 2023-03-17)
Field: Materials Science — Materials Chemistry

SLI-GNN: A Self-Learning-Input Graph Neural Network for Predicting Crystal and Molecular Properties Since the structures of crystals/molecules are often non-Euclidean data in real space, graph neural networks (GNNs) are regarded as the most prospective approach for their capacity of representing materials by graph-based inputs and emerged as an efficient and powerful tool in accelerating the discovery of new materials. Here, we proposed a self-learning-input GNN framework, named SLI-GNN, to uniformly predict the properties for both crystals and molecules, in which, for the first time, we design a dynamic embedding layer to self-update the input features along with the iteration of the neural network and introduce the Infomax mechanism to maximize the average mutual information between the local features and the global features. It is found that our SLI-GNN can reach ideal prediction accuracy with less inputs and more MPNN layers. The model evaluation on the Materials Project Dataset and QM9 dataset verify that the overall performance of our GNN is comparable to that of other previously reported GNNs. Thus, our GNN framework presents excellent performance in material property prediction, thereby being promising for accelerating the discovery of new materials. Computer science ; Embedding ; Graph ; Infomax ; Artificial neural network ; Euclidean space ; Artificial intelligence ; Data mining ; Theoretical computer science ; Algorithm ; Machine learning ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods