jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuxiao Gou | Yiping Zhang | Jian Zhu | Yidan Shu
Source: Scientific Data (article, 2024-04-11)
Field: Materials Science — Materials Chemistry

A document-level information extraction pipeline for layered cathode materials for sodium-ion batteries Natural language processing techniques enable extraction of valuable information from large amounts of published literature for the application of data science and technology, i.e. machine learning in the field of materials science. Nevertheless, the automated extraction of data from full-text documents remains a complex task. We propose a document-level natural language processing pipeline for literature extraction of comprehensive information on layered cathode materials for sodium-ion batteries. The pipeline enhances entity recognition with contextual supplementary information while capturing the article structure. Finally, a heuristic multi-level relationship extraction algorithm is employed in relation extraction to extract experimental parameters and complex performance relationships respectively. We successfully extracted a comprehensive dataset containing 5265 records from 1747 documents, encompassing essential information such as chemical composition, synthesis parameters, and electrochemical properties. By implementing our pipeline, we have made significant progress in overcoming the challenges associated with data scarcity in battery informatics. The extracted datasets provide a valuable resource for further research and development in the field of layered cathode materials. Pipeline (software) ; Information extraction ; Computer science ; Extraction (chemistry) ; Materials informatics ; Relationship extraction ; Field (mathematics) ; Information retrieval ; Resource (disambiguation) ; Heuristic ; Artificial intelligence ; Natural language processing ; Data science ; Health informatics ; Chemistry ; Programming language Machine Learning in Materials Science ; Topic Modeling
Authors: Yanjing He | Shitong Zhang | Rongmei Han | Kexin Peng | Min Wang | Zhengqing Zhang | Chongli Zhong
Source: Separation and Purification Technology (article, 2024-05-21)
Field: Chemistry — Inorganic Chemistry

Machine learning boosts molecular design of metal-organic framework for efficient CF4 capture Nanotechnology ; Process engineering ; Engineering ; Computer science ; Systems engineering ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Philipp Schienbein | Jochen Blumberger
Source: ChemPhysChem (article, 2024-10-04)
Field: Materials Science — Materials Chemistry

Data‐Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO<sub>4</sub> in Water The protonation state of molecules and surfaces is pivotal in various disciplines, including (electro-)catalysis, geochemistry, biochemistry, and pharmaceutics. Accurately and efficiently determining acidity constants is critical yet challenging, particularly when explicitly considering the electronic structure, thermal fluctuations, anharmonic vibrations, and solvation effects. In this research, we employ thermodynamic integration accelerated by committee Neural Network potentials, training a single machine learning model that accurately describes the relevant protonated, deprotonated, and intermediate states. We investigate two deprotonation reactions at the BiVO<sub>4</sub> (010)-water interface, a promising candidate for efficient photocatalytic water splitting. Our results illustrate the convergence of the required ensemble averages over simulation time and of the final acidity constant as a function of the Kirkwood coupling parameter. We demonstrate that simulation times on the order of nanoseconds are required for statistical convergence. This time scale is currently unachievable with explicit ab-initio molecular dynamics simulations at the hybrid DFT level of theory. In contrast, our machine learning workflow only requires a few hundred DFT single point calculations for training and testing. Exploiting the extended time scales accessible, we furthermore asses the effect of commonly applied bias potentials. Thus, our study significantly advances calculating free energy differences with ab-initio accuracy. Ab initio ; Thermodynamic integration ; Chemistry ; Density functional theory ; Deprotonation ; Protonation ; Computational chemistry ; Solvation ; Anharmonicity ; Ab initio quantum chemistry methods ; Artificial neural network ; Statistical physics ; Molecular dynamics ; Thermodynamics ; Molecule ; Physics ; Computer science ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Veronika Jurásková | Gers Tusha | Hanwen Zhang | Lars V. Schäfer | Fernanda Duarte
Source: Faraday Discussions (article, 2024-08-02)
Field: Materials Science — Materials Chemistry

Modelling ligand exchange in metal complexes with machine learning potentials Metal ions are irreplaceable in many areas of chemistry, including (bio)catalysis, self-assembly and charge transfer processes. Yet, modelling their structural and dynamic properties in diverse chemical environments remains challenging for both force fields and <i>ab initio</i> methods. Here, we introduce a strategy to train machine learning potentials (MLPs) using MACE, an equivariant message-passing neural network, for metal-ligand complexes in explicit solvents. We explore the structure and ligand exchange dynamics of Mg<sup>2+</sup> in water and Pd<sup>2+</sup> in acetonitrile as two illustrative model systems. The trained potentials accurately reproduce equilibrium structures of the complexes in solution, including different coordination numbers and geometries. Furthermore, the MLPs can model structural changes between metal ions and ligands in the first coordination shell, and reproduce the free energy barriers for the corresponding ligand exchange. The strategy presented here provides a computationally efficient approach to model metal ions in solution, paving the way for modelling larger and more diverse metal complexes relevant to biomolecules and supramolecular assemblies. Charge (physics) ; Metal ; Charge exchange ; Nanotechnology ; Metal ions in aqueous solution ; Chemistry ; Chemical physics ; Ion ; Biochemical engineering ; Computer science ; Materials science ; Engineering ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Piyush Sindhu Sharma | Sneha Thomas | M. K. C. Nair | Ananth Govind Rajan
Source: Journal of the American Chemical Society (article, 2024-10-25)
Field: Materials Science — Materials Chemistry

Machine Learnable Language for the Chemical Space of Nanopores Enables Structure–Property Relationships in Nanoporous 2D Materials The synthesis of nanoporous two-dimensional (2D) materials has revolutionized fields such as membrane separations, DNA sequencing, and osmotic power harvesting. Nanopores in 2D materials significantly modulate their optoelectronic, magnetic, and barrier properties. However, the large number of possible nanopore isomers makes their study onerous, while the lack of machine-learnable representations stymies progress toward structure-property relationships. Here, we develop a language for nanopores in 2D materials, called STring Representation Of Nanopore Geometry (STRONG), that opens the field of 2D nanopore informatics. We show that STRONGs are naturally suited for machine learning via recurrent neural networks, predicting formation energies/times of arbitrary nanopores and transport barriers for CO<sub>2</sub>, N<sub>2</sub>, and O<sub>2</sub> gas molecules, enabling structure-property relationships. The machine learning models enable the discovery of specific nanopore topologies to separate CO<sub>2</sub>/N<sub>2</sub>, O<sub>2</sub>/CO<sub>2</sub>, and O<sub>2</sub>/N<sub>2</sub> gas mixtures with high selectivity ratios. We also enable the rapid enumeration of unique configurations of stable, functionalized nanopores in 2D materials via STRONGs, allowing systematic searching of the vast chemical space of nanopores. Using the STRONGs approach, we find that a mix of hydrogen and quinone functionalization results in the most stable functionalized nanopore configuration in graphene, a discovery made feasible by expedited chemical space exploration. Additionally, we also unravel the STRONGs approach as ∼1000 times faster than graph theory algorithms to distinguish nanopore shapes. These advances in the language-based representation of 2D nanopores will accelerate the tailored design of nanoporous materials. Chemistry ; Nanoporous ; Nanopore ; Space (punctuation) ; Property (philosophy) ; Chemical space ; Nanotechnology ; Organic chemistry ; Computer science Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Salomé Trillot | Julien Lam | Simona Ispas | Akshay Krishna Ammothum Kandy | Mark E. Tuckerman | Nathalie Tarrat | Magali Benoit
Source: Computational Materials Science (article, 2024-02-08)
Field: Materials Science — Materials Chemistry

Elaboration of a neural-network interatomic potential for silica glass and melt Because of its importance in various aspects of everyday life, silica is a material that has been the subject of extensive research. Studies on its amorphous phase have particularly benefited from the contribution of atomistic simulations to understand the close relationships between its structure and properties. In this context, the main difficulty lies in the compromise that had to be made between the precision of the interactions that need to be computed at an ab initio level and the important statistics required to describe disorder. With the advent of machine learning approaches, it is now possible to couple accuracy and statistics by using interatomic potentials trained on ab initio databases. This opens up unprecedented prospects for studies where calculation accuracy, system size and trajectory length are critical. In this work, we propose a machine learning potential for silica obtained from a neural network trained on a database consisting of a few hundred configurations extracted from an ab initio molecular dynamics trajectory at the Density Functional Theory (DFT) level of a silica liquid at high temperature and under pressure. We show that this potential is sufficiently accurate to describe the liquid and amorphous phases of silica, and that it is also transferable to glasses under moderate pressure and, more surprisingly, to certain crystalline phases. Ab initio ; Context (archaeology) ; Amorphous solid ; Molecular dynamics ; Interatomic potential ; Amorphous silica ; Artificial neural network ; Ab initio quantum chemistry methods ; Materials science ; Statistical physics ; Computer science ; Chemical physics ; Artificial intelligence ; Chemistry ; Computational chemistry ; Physics ; Crystallography ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Glass properties and applications
Authors: Bo Yang | Anthony J. Schaefer | Brooke L. Small | Julie A. Hopkins Leseberg | Steven M. Bischof | Michael S. Webster‐Gardiner | Daniel H. Ess
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Experimentally-based Fe-catalyzed ethene oligomerization machine learning model provides highly accurate prediction of propagation/termination selectivity Linear α-olefins (1-alkenes) are critical comonomers for ethene copolymerization. A major impediment in the development of new homogeneous Fe catalysts for ethene oligomerization to produce comonomers and other important commercial products is the prediction of propagation <i>versus</i> termination rates that control the α-olefin distribution (<i>e.g.</i>, 1-butene through 1-decene), which is often referred to as a <i>K</i>-value. Because the transition states for propagation <i>versus</i> termination are generally separated by less than a one kcal mol<sup>-1</sup> difference in energy, this selectivity cannot be accurately predicted by either DFT or wavefunction methods (even DLPNO-CCSD(T)). Therefore, we developed a sub-kcal mol<sup>-1</sup> accuracy machine learning model based on several hundred experimental selectivity values and straightforward 2D chemical and physical features that enables the prediction of α-olefin distribution <i>K</i>-values. As part of our model, we developed a new <i>ad hoc</i> feature that boosted the model performance. This machine learning model captures the effects of a broad range of ligand architectures and chemically nonintuitive trends in oligomerization selectivity. Our machine learning model was experimentally validated by prediction of a <i>K</i>-value for a new Fe phosphaneyl-pyridinyl-quinoline catalyst followed by experimental measurement that showed precise agreement. In addition to quantitative predictions, we demonstrate how this machine learning model can provide qualitative catalyst design using proximity of pairs type analysis. Bespoke ; Selectivity ; Catalysis ; Work (physics) ; Biological system ; Computer science ; Chemistry ; Materials science ; Artificial intelligence ; Organic chemistry ; Thermodynamics ; Physics ; Biology Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; CO2 Reduction Techniques and Catalysts
Authors: Peng Zhang | Yinan Wang | Zixin Deng | Jiangtao Gao
Source: Organic & Biomolecular Chemistry (review, 2024-11-15)
Field: Chemistry — Inorganic Chemistry

Synthetic versatility: the C–P bond odyssey C-P bond formation reactions have garnered significant attention due to the widespread presence of organophosphorus compounds in pharmaceuticals, phosphine-containing ligands, pesticides, and materials science. Consequently, various efficient methodologies have been established in recent decades for constructing C-P bonds. This review article traces the historical evolution of C-P bond research and explores the prospects of C-P bond formation. It contrasts biotechnological approaches with chemical synthesis, emphasizing the critical importance of precision and innovation in developing novel C-P structures. A forward-looking perspective is provided on the role of computational tools and machine learning in optimizing C-P bond synthesis and discovering new compounds. The article explores prospective avenues for reactions that form C-P bonds and advocates for enhanced interdisciplinary collaboration to propel scientific and technological advancements. Bond ; Chemistry ; Business Asymmetric Hydrogenation and Catalysis ; Catalytic Cross-Coupling Reactions ; Asymmetric Synthesis and Catalysis
Authors: Yeongjun Yoon | Hyo Min You | Jinho Oh | Jung-Joon Lee | Jeong Woo Han | Kyeounghak Kim | Hyunguk Kwon
Source: Molecular Catalysis (article, 2024-01-20)
Field: Materials Science — Materials Chemistry

Recent advances in atomic-scale simulations for supported metal catalysts Density functional theory ; Kinetic Monte Carlo ; Molecular dynamics ; Monte Carlo method ; Computer science ; Statistical physics ; Scale (ratio) ; Catalysis ; Atomic units ; Multiscale modeling ; Ab initio ; Computational model ; Biochemical engineering ; Computational chemistry ; Nanotechnology ; Chemistry ; Materials science ; Physics ; Simulation ; Mathematics ; Engineering ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Jonathan Hollenbach | Cassandra Pate | Haili Jia | James L. Hart | Paulette Clancy | Mitra L. Taheri
Source: Scientific Reports (article, 2024-08-02)
Field: Materials Science — Materials Chemistry

Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale. MXenes ; Computer science ; Characterization (materials science) ; Artificial intelligence ; Machine learning ; Biological system ; Nanotechnology ; Pattern recognition (psychology) ; Materials science ; Biology MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Xiaolan Zhao | Xiaoqi Wang | Pei Gao | Peng Zhao | Jingjing Wang | Yingna Li | Zhibin Han | Boxiong Shen
Source: Green Energy & Environment (article, 2024-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Eco-friendly synthesis coupled with predictive analytics: Developing hierarchical lignin-derived ordered mesoporous carbon for advanced supercapacitors Hierarchical lignin-derived ordered mesoporous carbon (HOMC) was significant for advanced supercapacitors. However, achieving controllable fabrication and optimizing electrochemical behavior were challenging. In this work, an eco-friendly HOMC was synthesized using lignin as carbon precursors and Zn 2+ as cross-linking and pore-forming agents, followed by KHCO 3 activation, eliminating the need for toxic phenolic resins and acid treatments for metal removal. Machine learning technology, specifically an Artificial Neural Network (ANN) model, was utilized to assist the experimental design and prediction. The ANN model suggested an ideal hierarchical structure and optimized oxygen level, achieved through the adjustment of Zn 2+ additive concentration, carbonization temperature, and subsequent KHCO 3 activation to maximize capacitance. The HOMC electrode, with a micropore-to-mesopore ratio (S micro /S meso ) of 1.01 and an oxygen content of 8.81 at%, acquired a specific capacitance of 362 F·g −1 at 0.5 A·g −1 in 6 mol·L −1 KOH electrolyte. The assembled HOMC//HOMC supercapacitor could afford a high energy density of 33.38 Wh·kg −1 with a corresponding specific power density of 300 W·kg −1 in TEATFB/PC electrolyte. Meanwhile, the long-term cycle stability of 94.33% was achieved after 20,000 cycles. This work provides an ANN-assisted strategy for the synthesis of HOMC, highlighting its potential to valorize biomass and agricultural waste in sustainable energy storage solutions. Hierarchical ordered mesoporous carbon (HOMC) was fabricated and activated using an eco-friendly synthetic method with walnut shell derived lignin as carbon percussor, and Zn 2+ as crosslinking agent. The HOMC demonstrated high performance for supercapacitors with the assistance of machine learning. • Green synthesis of hierarchical ordered mesoporous carbon from lignin. • Machine learning assisted preparation and prediction. • Rational design of micro-mesoporous distribution and oxygen content. • HOMC exhibited high performance for supercapacitors. Supercapacitor ; Mesoporous material ; Lignin ; Materials science ; Environmentally friendly ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry ; Capacitance ; Catalysis ; Engineering ; Electrode Supercapacitor Materials and Fabrication ; Catalysis and Hydrodesulfurization Studies ; Catalysis for Biomass Conversion
Authors: S. S. Sree Sanker | Subin Thomas | Dhanya P. Jacob | V S Suniya | Savitha Nalini | K. N. Madhusoodanan
Source: Microchemical Journal (article, 2024-08-22)
Field: Chemistry — Analytical Chemistry

Highly sensitive colorimetric detection of ascorbic acid using molecularly imprinted photonic crystal hydrogel sensor Ascorbic acid ; Molecularly imprinted polymer ; Chemistry ; Photonic crystal ; Molecular imprinting ; Materials science ; Selectivity ; Optoelectronics ; Biochemistry ; Food science Analytical chemistry methods development ; Photonic Crystals and Applications ; Biosensors and Analytical Detection
Authors: Florian Bock | Ferenc Tasnádi | Igor A. Abrikosov
Source: Journal of Vacuum Science & Technology A Vacuum Surfaces and Films (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Active learning with moment tensor potentials to predict material properties: Ti0.5Al0.5N at elevated temperature Transition metal nitride alloys possess exceptional properties, making them suitable for cutting applications due to their inherent hardness or as protective coatings due to corrosion resistance. However, the computational demands associated with predicting these properties using ab initio methods can often be prohibitively high at the conditions of their operation at cutting tools, that is, at high temperatures and stresses. Machine learning approaches have been introduced into the field of materials modeling to address the challenge. In this paper, we present an active learning workflow to model the properties of our benchmark alloy system cubic B1 Ti0.5Al0.5N at temperatures up to 1500 K. With a minimal requirement of prior knowledge about the alloy system for our workflow, we train a moment tensor potential (MTP) to accurately model the material’s behavior over the entire temperature range and extract elastic and vibrational properties. The outstanding accuracy of MTPs with relatively little training data demonstrates that the presented approach is highly efficient and requires about two orders of magnitude less computational resources than state-of-the-art ab initio molecular dynamics. Workflow ; Ab initio ; Materials science ; Tensor (intrinsic definition) ; Benchmark (surveying) ; Moment (physics) ; Moment tensor ; Computer science ; Alloy ; Cauchy stress tensor ; Computational science ; Chemistry ; Physics ; Metallurgy ; Mathematics ; Composite material ; Classical mechanics ; Geometry ; Deformation (meteorology) Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Advanced Chemical Physics Studies
Authors: Yen-Hsiang Lin | Hsin-Hao Liang | Shiang‐Tai Lin | Yi‐Pei Li
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Advancing vapor pressure prediction: A machine learning approach with directed message passing neural networks Artificial neural network ; Computer science ; Message passing ; Artificial intelligence ; Machine learning ; Distributed computing Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Fault Detection and Control Systems