jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martin Nováček | Jan Řezáč
Source: ChemRxiv (preprint, 2024-08-09)
Field: Chemistry — Physical and Theoretical Chemistry

PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physical principles into these models, or utilizing them in a ∆-ML scheme, significantly enhances their robustness and transferability. This paper introduces PM6-ML, a ∆-ML method that synergizes the semiempir- ical quantum-mechanical (SQM) method PM6 with a state-of-the-art ML poten- tial applied as a universal correction. The method demonstrates superior perfor- mance over standalone SQM and ML approaches and covers a broader chemical space than its predecessors. It is scalable to systems with thousands of atoms, which makes it applicable to large biomolecular systems. Extensive benchmarking confirms PM6-ML’s accuracy and robustness. Its practical application is facili- tated by a direct interface to MOPAC. The code and parameters are available at https://github.com/Honza-R/mopac-ml. Quantum chemistry ; Quantum chemical ; Computer science ; Quantum ; Computational chemistry ; Chemistry ; Artificial intelligence ; Statistical physics ; Theoretical physics ; Physical chemistry ; Quantum mechanics ; Physics ; Organic chemistry ; Molecule History and advancements in chemistry ; Various Chemistry Research Topics ; Machine Learning in Materials Science
Authors: Alexey Klopov | A. V. Yanilkin
Source: Materials Today Communications (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Approach to finding material with increased long-term strength using mechanical creep model, machine learning and optimization algorithm Materials science ; Creep ; Term (time) ; Composite material ; Algorithm ; Computer science Material Properties and Failure Mechanisms ; Advanced machining processes and optimization ; Metallurgy and Material Forming
Authors: Kirtibir Rajguru | Sujan Bhandari | Ganesh Kumar Shrestha | Chhabi Lal Gnawali | Bhadra P. Pokharel
Source: Bibechana (article, 2024-06-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Comparative study of machine learning based prediction of supercapacitance performance of activated carbon prepared from Bio-based Materials The performance of electrochemical double-layer capacitors (EDLCs) is evaluated by the capacitance of activated carbon (AC) electrodes. The capacitance of AC electrodes is influenced by many factors such as precursor type, activation method, pore structure, surface chemistry and electrolytic properties. In this paper, we present a comparative study of machine learning based prediction of surface area, mesopore volume and total pore volume of activated carbon for energy storage applications. The ML models were trained on a dataset of synthetic data that were generated from the limited number of experimental data and which included the activation temperature, methylene blue number and iodine number of the activated carbon (AC). The best performing ML model was random forest model and XG boost model. The results of this study can be used to optimize the production of activated carbon and improve its performance in energy storage applications. Activated carbon ; Computer science ; Materials science ; Chemistry ; Organic chemistry ; Adsorption Supercapacitor Materials and Fabrication
Authors: Shidi Tang | X. R. Zhou | Ming Ling
Source:  (article, 2024-08-21)
Field: Materials Science — Materials Chemistry

Modeling Equivariant Neural Networks for Hardware Acceleration, a Case Study on the Molecular Docking Tool DiffDock The equivariant neural network (ENN) exploits the symmetry of problems by enforcing equivariance with the neural network. ENN has a significant impact on various real-world scenarios, including molecular modeling, protein biology, and 3D point cloud. However, the intricate computational flow of ENN hampers its further advancement for applications that require high runtime speed. For instance, virtual screening, which is an early stage of computer-aided drug discovery, demands efficient screening (i.e., molecular docking) of large molecular databases to identify potential drug candidates. Among the available molecular docking tools, an SE(3) ENN named Diffdock stands out for its exceptional docking accuracy and serves as a prominent tool in the drug discovery research. Although customized hardware such as FPGA could accelerate such applications, the huge design effort is unaffordable. SystemC is an efficient solution to model the hardware designs with higher level abstraction. In this study, we propose the first SystemC model for equivariant neural networks, specifically targeting Diffdock, with the aim of accelerating its performance using customized hardware such as the FPGA. Experiments demonstrate that our model on DiffDock can achieve speedups of up to 3.20× and an average of 1.84× when compared to the GPU implementation. Computer science ; Equivariant map ; Artificial neural network ; Acceleration ; Hardware acceleration ; Docking (animal) ; Computer hardware ; Embedded system ; Computer architecture ; Artificial intelligence ; Mathematics ; Physics ; Field-programmable gate array Machine Learning in Materials Science
Authors: Sung Woo Lee | Marcel Jonathan Hidajat | Seung Hyeok | Gwang‐Nam Yun | Dong Won Hwang
Source: Fuel Processing Technology (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study In this study, the oligomerization of 1-octene was investigated using various zeolites through both experimental and machine learning (ML) approaches. The structural characteristics of the zeolites and experimental conditions were used as input parameters to analyze the feature importance of each key factor on the oligomerization of 1-octene. By quantifying these influences, the reaction mechanism was elucidated, and a methodology for maximizing the oligomerization reaction was developed. The ML methods employed in this study were SHapley Additive exPlanations (SHAP) and Sure Independence Screening and Sparsifying Operator (SISSO). While the SHAP method is a well-validated conventional approach, it has limitations due to its high data requirements. Therefore, the SISSO method was applied, as it requires fewer data points and offers a transparent computational process. SISSO provided results in the form of human-interpretable equations, allowing for an analysis of these equations to deepen insights into the reaction mechanism. • Linear alpha olefin oligomerization over heterogeneous zeolite catalysts. • Uncover the relationship between physicochemical properties of zeolites and oligomerization. • Development of methodologies combining experimental and machine learning outputs. • Quantitative elucidation of parameter effectiveness in 1-octene oligomerization on zeolites. Olefin fiber ; Zeolite ; Catalysis ; Alpha (finance) ; Chemistry ; Chain (unit) ; Organic chemistry ; Physics ; Mathematics ; Statistics Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Yuan Liu | Yao Jiang | Liuxuan Luo | Jiewei Yin | Xiaohui Yan | Xiaojing Cheng | Shuiyun Shen | Junliang Zhang
Source:  (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Local Pore Modeling and Gas Mass Transfer Simulation in Electrochemical CO2 Reduction Catalyst Layers The electrochemical <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CO}_{2}$</tex> reduction reaction (CO2RR) is a promising approach to combat climate change, with the catalyst layer (CL) of membrane electrode assemblies playing a crucial role in both the reaction and mass transfer. Traditional methods have limitations, as experimental measurements of the internal environment are difficult, and most simulation models treat the CL as a whole, ignoring its local pore structure. This work thus develops a local pore model of the CL using deep learning techniques to examine the effects of different ionomer contents on gas mass transfer. Results align well with existing studies: the CL with an I/C ratio of 0.2 has the highest gas diffusion distance (679.53 nm), indicating optimal mass transfer; the CL with an I/C ratio of 0.6 has the largest silver catalyst surface area, reflecting the highest number of active sites; and the CL with an I/C ratio of 0.4 shows the highest CO concentration in the high-concentration region, suggesting a balance of mass transfer and reaction efficiency. This model could serve as a useful tool and pathway to offer valuable insights for optimizing CL design and then improving the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CO}_{2}$</tex> RR performance. Mass transfer ; Reduction (mathematics) ; Electrochemistry ; Materials science ; Chemical engineering ; Catalysis ; Computer science ; Process engineering ; Electrode ; Chemistry ; Chromatography ; Engineering ; Physical chemistry Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts ; Machine Learning in Materials Science
Authors: Samira Siahrostami | Nicholas Murray
Source: Canadian Journal of Chemistry (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

The success of computational material design for sustainable energy catalysis Computational material design (CMD) employs quantum mechanical simulations, density functional theory, and machine learning techniques to correlate electronic structural attributes with physical and chemical properties of materials. Over the last decade, CMD has proven to be critical to the advancement of materials science and a variety of engineering fields. This contribution provides an overview of CMD’s success in driving materials discovery for catalysis in the context of sustainable energy applications. Specifically, we discuss how CMD has enabled the development of catalysts for three electrochemical processes that are critical to sustainable energy applications, oxygen reduction reactions, water oxidation reactions, and CO 2 reduction reactions. We illustrate how CMD provides a powerful and efficient method for understanding underlying reaction mechanisms as well as predicting and optimizing catalyst properties. Furthermore, we demonstrate how this strategic approach has enabled researchers to effectively navigate the vast chemical space of potential catalysts and rapidly identify novel materials possessing desirable electronic structures and catalytic activity. Catalysis ; Context (archaeology) ; Chemistry ; Chemical space ; Biochemical engineering ; Nanotechnology ; Variety (cybernetics) ; Density functional theory ; Chemical reaction engineering ; Process engineering ; Computer science ; Computational chemistry ; Organic chemistry ; Materials science ; Artificial intelligence ; Engineering ; Drug discovery Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Xincheng Zhou | Hikaru Matsumoto | Masanori Nagao | Shuji Hironaka | Yoshiko Miura
Source: Polymer Journal (article, 2024-06-06)
Field: Materials Science — Materials Chemistry

Development of Pd-immobilized porous polymer catalysts via Bayesian optimization Bayesian optimization ; Catalysis ; Polymer ; Workflow ; Porosity ; Polymerization ; Materials science ; Chemical engineering ; Computer science ; Chemistry ; Organic chemistry ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Bin Cao | Daniel R. Anderson | Luke K. Davis
Source: Journal of Applied Crystallography (article, 2024-12-24)
Field: Materials Science — Materials Chemistry

ASUGNN: an asymmetric-unit-based graph neural network for crystal property prediction Material properties can often be derived directly from fundamental equations governing electron behavior. In this study, we present an open-source asymmetric-unit-based graph neural network designed to capture atomic patterns and their corresponding electron distributions. By coarse-graining sites belonging to conjugate subgroups and analyzing reciprocal space through powder X-ray diffraction patterns, our model predicts key physical properties, including formation energy, band gap, bulk modulus and metal/non-metal classification. Our method demonstrates exceptional predictive accuracy for properties calculated using density functional theory across the Materials Project dataset. Our approach is compared with state-of-the-art models and exhibits impressively low error rates in zero-shot predictions. Granularity ; Reciprocal lattice ; Bulk modulus ; Reciprocal ; Artificial neural network ; Graph ; Computer science ; Materials science ; Statistical physics ; Diffraction ; Physics ; Theoretical computer science ; Condensed matter physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Chen Chen | Xin Jing | Peng Zhou | ChenXi Wang | Hongxu Lan | Cuiping Yao | Jing Wang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-10-28)
Field: Chemistry — Analytical Chemistry

Stand-off hazardous materials identification based on near-infrared hyperspectral imaging combined with convolutional neural network Chemistry ; Hyperspectral imaging ; Convolutional neural network ; Identification (biology) ; Hazardous waste ; Chemical imaging ; Infrared ; Pattern recognition (psychology) ; Artificial intelligence ; Remote sensing ; Nanotechnology ; Environmental chemistry ; Optics ; Waste management ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Measurement and Detection Methods ; Water Quality Monitoring and Analysis
Authors: Chengqiao Yang | Minhua Sun
Source: Journal of Molecular Modeling (article, 2024-11-12)
Field: Materials Science — Materials Chemistry

Exploring structural variances in monatomic metallic glasses using machine learning and molecular dynamics simulation Monatomic ion ; Molecular dynamics ; Statistical physics ; Amorphous metal ; Materials science ; Chemical physics ; Artificial intelligence ; Computer science ; Computational chemistry ; Physics ; Chemistry ; Metallurgy ; Quantum mechanics Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Theoretical and Computational Physics
Authors: Ning Xu | Zheng Li | Xiaolan Fu | Xiaojuan Hu | Wenwu Xu | Zhongkang Han
Source: Chemical Communications (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Unveiling the physical mechanisms driving delafossite crystal (ABX<sub>2</sub>) formation through interpretable machine learning A method integrating machine learning with first-principles calculations is employed to forecast the formation energy of delafossite crystals, facilitating the rapid identification of stable crystals. This approach identifies several stable candidates and highlights the importance of atomic ionization energy and electron affinity in the formation of delafossite crystals. Delafossite ; Ionization ; Energy (signal processing) ; Chemical physics ; Identification (biology) ; Materials science ; Chemistry ; Oxide ; Physics ; Ion ; Metallurgy Copper-based nanomaterials and applications ; Quantum Dots Synthesis And Properties ; Calcium Carbonate Crystallization and Inhibition
Authors: Shuichiro Hashimoto | Takanobu Watanabe
Source: Japanese Journal of Applied Physics (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Training dependency of neural network interatomic potential for molecular dynamics simulation of Ru-Si-O mixed system Abstract We investigated the training dependency of neural network interatomic potentials for molecular dynamics simulation of a Ru–Si–O mixed system. Our neural network interatomic potential was improved using a data augmentation technique for the training dataset, including data points of reference energies and forces related to reference structures. We demonstrated that the data augmentation technique, focusing on the lattice expansion coefficient of bulk structures in the training dataset, requires moderation to ensure optimal training of the neural network interatomic potential. We found that Ru/SiO 2 interfaces were accurately represented using the neural network interatomic potential trained with Ru and SiO 2 surfaces in addition to Ru/SiO 2 interfaces. In the case of modeling Ru/SiO 2 interfaces including unbonded atoms, training the surfaces with unbonded atoms is effective in generalizing the neural network interatomic potential. Our demonstration and finding shed light on the pivotal role of the training dataset on the development of the neural network interatomic potential for the Ru–Si–O mixed system. Molecular dynamics ; Interatomic potential ; Dependency (UML) ; Artificial neural network ; Chemical physics ; Computer science ; Materials science ; Statistical physics ; Physics ; Chemistry ; Artificial intelligence ; Computational chemistry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Force Microscopy Techniques and Applications
Authors: Xinyu Chen | Guoliang Ru | Weihong Qi
Source: The Journal of Physical Chemistry C (article, 2024-09-04)
Field: Materials Science — Materials Chemistry

Structural Embedding Methods for Machine Learning Models Accelerate Research on Stacked 2D Materials The properties of two-dimensional materials can be controlled with a high degree of freedom through stacking, so it is crucial to explore the relationship between the structures and properties of stacked two-dimensional (2D) materials. However, the large material space caused by the high degree of freedom and the large system caused by the moiré effect make density functional theory (DFT) calculations very expensive. Here, to accelerate research on stacked 2D materials, we develop a structural embedding method for machine learning models that treats intralayer bonding interactions and interlayer nonbonding interactions equally. This method performs significantly better than other traditional edge embedding methods. Permutation importance analysis revealed that electrons significantly influence the properties of stacked 2D materials. Owing to the improved accuracy of the structural embedding method, we used the structure-embedded PAINN (SE-PAINN) to predict the binding energy and band gap of twisted stacked 2D materials, which basically reproduced the DFT calculation results, while the computational cost was reduced by several orders of magnitude. The structural embedding method makes it possible to study the torsional properties of stacked 2D materials. SE-PAINN also shows promising application prospects in potential energy surface prediction. This work proposes a structural embedding method to reliably predict the properties of layered stacked 2D materials, which will open an important avenue for studying twisted stacked 2D materials. Embedding ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Stochastic Gradient Optimization Techniques
Authors: Takahiro Tsukahara | Takahiro Ishigami | M. Irikura
Source: Journal of Fluid Science and Technology (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Deep learning estimation of scalar source distance for different turbulent and molecular diffusion environments In order to adopt convolutional neural networks (CNN) for practical use in estimating the source of scalar dispersion in turbulent flows, such as gas leaks in industrial plants, the inference accuracy was verified using scalar concentration distributions in various turbulent environmental conditions. Training and test data were obtained through quasi direct numerical simulations on a flow system with a scalar-source-attached cylinder downstream of a turbulent grid, at two Schmidt numbers. An inference accuracy of at least 90% was confirmed under trained flow conditions, provided that the observation window was large enough to capture the integral scale in scalar fluctuations in turbulence. When the flow conditions (in terms of the turbulence intensity and the Schmidt number) differed between the training and testing phases, the accuracy was significantly reduced. However, the learner supervised with images under all different conditions showed high generalization performance. Singular value decomposition was used to discuss the features essential for training and prediction. In our CNN inference, we concluded that the source estimation was achieved by extracting from the integral-scale scalar patch distribution the features related to each of the turbulent and molecular diffusion progressions and their correlations. The results provide insights into the potential solutions for accurately predicting the sources of material dispersion in turbulent environments. Scalar (mathematics) ; Computer science ; Diffusion ; Turbulence ; Turbulent diffusion ; Statistical physics ; Mechanics ; Physics ; Mathematics ; Geometry ; Thermodynamics Spectroscopy and Laser Applications
Authors: Alexander Wang | Samuel Chen | Matthew Chang | Evan Xie | Anthony Zhu | Adam Hansen | John Jerome | Miriam Rafailovich
Source: ChemRxiv (preprint, 2024-01-02)
Field: Materials Science — Materials Chemistry

Utilizing Machine Learning to Model Interdependency of Bulk Molecular Weight, Solution Concentration, and Thickness of Spin Coated Polystyrene Thin Films Spin coating is a wide-spread, quick, and inexpensive method to create nanometer-thick thin films of various polymers, such as polystyrene, on top of solid substrates. Since the film thickness determines the mechanical, optical, and degradation properties of the coated film, it is essential to develop a simple method to predict thickness based on other manipulatable factors. In this study, a three-dimensional manifold relating initial solution concentration, thin film coverage thickness, and monodisperse bulk molecular weight is developed utilizing curve-fit machine learning. The model is able to receive polystyrene bulk molecular weight and desired thin film thickness as input and output an accurate prediction of initial solution concentration required to generate a coating of a desired thickness. Polystyrene ; Dispersity ; Materials science ; Spin coating ; Thin film ; Nanometre ; Polymer ; Coating ; Composite material ; Nanotechnology ; Polymer chemistry High voltage insulation and dielectric phenomena