jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Francesco Guidarelli Mattioli | Francesco Sciortino | John Russo
Source: The Journal of Physical Chemistry B (article, 2023-04-19)
Field: Materials Science — Materials Chemistry

Are Neural Network Potentials Trained on Liquid States Transferable to Crystal Nucleation? A Test on Ice Nucleation in the mW Water Model Neural network potentials (NNPs) are increasingly being used to study processes that happen on long time scales. A typical example is crystal nucleation, which rate is controlled by the occurrence of a rare fluctuation, i.e., the appearance of the critical nucleus. Because the properties of this nucleus are far from those of the bulk crystal, it is yet unclear whether NN potentials trained on equilibrium liquid states can accurately describe nucleation processes. So far, nucleation studies on NNPs have been limited to ab initio models whose nucleation properties are unknown, preventing an accurate comparison. Here we train a NN potential on the mW model of water─a classical three-body potential whose nucleation time scale is accessible in standard simulations. We show that a NNP trained only on a small number of liquid state points can reproduce with great accuracy the nucleation rates and free energy barriers of the original model, computed from both spontaneous and biased trajectories, strongly supporting the use of NNPs to study nucleation events. Nucleation ; Water model ; Ice nucleus ; Classical nucleation theory ; Crystal (programming language) ; Statistical physics ; Chemical physics ; Materials science ; Chemistry ; Physics ; Thermodynamics ; Molecular dynamics ; Computer science ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Akshansh Mishra | Vijaykumar S. Jatti | Eyob Messele Sefene
Source: Materials Today Communications (article, 2023-11-04)
Field: Materials Science — Biomaterials

Exploratory analysis and evolutionary computing coupled machine learning algorithms for modelling the wear characteristics of AZ31 alloy Particle swarm optimization ; Materials science ; Magnesium alloy ; Algorithm ; Alloy ; Gradient boosting ; Boosting (machine learning) ; Mean squared error ; Computer science ; Biological system ; Machine learning ; Metallurgy ; Mathematics ; Statistics ; Random forest Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Lubricants and Their Additives
Authors: Hiroki Chikuma | Gentoku Takasao | Toru Wada | Patchanee Chammingkwan | Jörg Behler | Toshiaki Taniike
Source: The Journal of Physical Chemistry C (article, 2023-06-09)
Field: Materials Science — Materials Chemistry

Accelerating Non-Empirical Structure Determination of Ziegler–Natta Catalysts with a High-Dimensional Neural Network Potential The determination of catalyst nanostructures with first-principles accuracy using genetic algorithms (GA) is very demanding due to the cubic scaling of the computational cost of density functional theory (DFT) calculations. Here, we demonstrate, for the case of Ziegler–Natta MgCl2/TiCl4 nanoplates, how this structure determination can be accelerated by employing a high-dimensional neural network potential (HDNNP) of essentially DFT accuracy. First, when building HDNNPs for MgCl2/TiCl4 clusters with computationally tractable sizes, we found that the structural diversity in the training set is crucial for obtaining HDNNPs reliably describing the large variety of structures generated by GA. The resulting HDNNPs dramatically accelerated the structure determination while yielding results consistent with DFT. Subsequently, we developed a multistep adaptive procedure to construct a HDNNP for MgCl2/TiCl4 clusters consistent in size and TiCl4 coverage with experiments where prior DFT results were scarcely collected. The structure determination and analyses underline the importance of system size and composition in order to predict some experimentally known facts such as the surface morphology and population of isospecific sites. Natta ; Density functional theory ; Artificial neural network ; Nanostructure ; Scaling ; Materials science ; Construct (python library) ; Biological system ; Statistical physics ; Algorithm ; Computer science ; Computational chemistry ; Nanotechnology ; Chemistry ; Mathematics ; Physics ; Artificial intelligence ; Geometry ; Biology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Xiao Jin | Yixiao Chen | LinFeng Zhang | Handong Wang | Tong Zhu
Source: Artificial Intelligence Chemistry (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

A machine learning-based high-precision density functional method for drug-like molecules In computer-aided drug discovery, accurately determining the structure and properties of drug-like molecules is of utmost importance. This necessitates the use of precise and efficient electronic structure methods. Here, we developed two deep learning-based density functional methods, namely DeePHF and DeePKS, specifically tailored for drug-like molecules. Notably, DeePKS incorporates self-consistency into its framework. With a limited dataset labelled at the CCSD(T)/def2-TZVP level, both models have been able to achieve chemical accuracy in calculating molecular energies and have demonstrated excellent transferability. We anticipate that further advancements in this field will lead to the development of high-quality density functional methods designed specifically for drug discovery purposes. This research showcases the capabilities of deep learning approaches in simplifying the construction complexity associated with traditional DFT methods. Computer science ; Drug discovery ; Consistency (knowledge bases) ; Transferability ; Field (mathematics) ; Machine learning ; Density functional theory ; Artificial intelligence ; Quality (philosophy) ; Data mining ; Data science ; Chemistry ; Computational chemistry ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Yongtao Liu | Kyle P. Kelley | Rama K. Vasudevan | Maxim Ziatdinov | Sergei V. Kalinin
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Materials Chemistry

Machine Learning-driven Autonomous Microscopy for Materials and Physics Discovery Microscopy ; Nanotechnology ; Physics ; Materials science ; Optics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Masanori Kaneko | Ayane Suzaki | Azusa Muraoka | Kazuma Gotoh | Koichi Yamashita
Source: Journal of Materials Informatics (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Neural network to predict 23Na NMR spectra of Nan clusters In order to understand the charging and discharging processes of sodium-ion batteries, we are interested in the relationship between the size of sodium clusters inserted into the hard carbon anode and the solid-state 23Na NMR chemical shifts. In this study, we investigated the predictability of the size dependence of 23Na NMR shielding constants by SchNet, a deep learning model that uses the distance between Na atoms without graph connection information. The data set required for training the neural network was constructed by density functional theory (DFT) calculations. This study shows that the neural network model, which only used structural data, achieved comparable accuracy in predicting the shielding constant to the Lasso model, which utilized gross orbital population predicted from DFT calculations. Moreover, by introducing a penalty term to the neural network's loss function, the neural network was able to reproduce the skewed distribution of the shielding constant without modifying its architecture. Artificial neural network ; Density functional theory ; Electromagnetic shielding ; Population ; Chemical shift ; Statistical physics ; Computer science ; Chemistry ; Molecular physics ; Materials science ; Physics ; Nuclear magnetic resonance ; Computational chemistry ; Artificial intelligence ; Quantum mechanics Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Jun-Zhe Hao | Shiai Xu | Jiajun Xu | Hai-Lian Cao | Han Miao
Source: Materials Today Communications (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Modeling and optimization of the corrosion resistance of Cr-free and Cr-based chemical conversion coatings on nickel foil by artificial neural network and response surface method Materials science ; Response surface methodology ; Corrosion ; FOIL method ; X-ray photoelectron spectroscopy ; Artificial neural network ; Nickel ; Metallurgy ; Scanning electron microscope ; Composite material ; Chemical engineering ; Machine learning ; Computer science Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Metal and Thin Film Mechanics
Authors: Rafał Stottko | Elżbieta Dziadyk-Stopyra | Bartłomiej M. Szyja
Source: Catalysts (article, 2023-11-26)
Field: Materials Science — Materials Chemistry

Can Machine Learning Predict the Reaction Paths in Catalytic CO2 Reduction on Small Cu/Ni Clusters? In this paper, we explore the catalytic CO2 reduction process on 13-atom bimetallic nanoclusters with icosahedron geometry. As copper and nickel atoms may be positioned in different locations and either separated into groups or uniformly distributed, the possible permutations lead to many unnecessary simulations. Thus, we have developed a machine learning model aimed at predicting the energy of a specific group of bimetallic (CuNi) clusters and their interactions with CO2 reduction intermediates. The training data for the algorithm have been provided from DFT simulations and consist only of the coordinates and types of atoms, together with the related potential energy of the system. While the algorithm is not able to predict the exact energy of the given complex, it is able to select the candidates for further optimization with reasonably good certainty. We have also found that the stability of the complex depends on the type of central atom in the nanoparticle, despite it not directly interacting with the intermediates. Bimetallic strip ; Nanoclusters ; Atom (system on chip) ; Reduction (mathematics) ; Catalysis ; Stability (learning theory) ; Nickel ; Materials science ; Chemical physics ; Computer science ; Chemistry ; Nanotechnology ; Mathematics ; Machine learning ; Geometry ; Metallurgy Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Brenden W. Hamilton | Timothy C. Germann
Source: Physical Review Materials (article, 2023-08-14)
Field: Materials Science — Materials Chemistry

Using limited neural networks to assess relative mechanistic influence on shock heating in granular solids The rapid compaction of granular media results in localized heating that can induce chemical reactions, phase transformations, and melting. However, there are numerous mechanisms in play that can be dependent on a variety of microstructural features. Machine learning techniques such as neural networks offer a ubiquitous method to develop models for physical processes. Limiting what kind of microstructural information is used as an input and assessing normalized changes in network error, the relative importance of different mechanisms can be inferred. Here we utilize binned, initial density information as network inputs to predict local shock heating in a granular high explosive trained from large-scale molecular dynamics simulations. The spatial extent of the density field used in the network is altered to assess the importance and relevant length scales of the physical mechanisms in play, where different microstructural features result in different predictive capabilities. Explosive material ; Materials science ; Limiting ; Artificial neural network ; Shock (circulatory) ; Biological system ; Field (mathematics) ; Compaction ; Statistical physics ; Computer science ; Chemical physics ; Artificial intelligence ; Mechanical engineering ; Composite material ; Physics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Energetic Materials and Combustion
Authors: Da‐Jiang Liu | James W. Evans
Source: The Journal of Chemical Physics (article, 2023-07-10)
Field: Materials Science — Materials Chemistry

Fluorine spillover for ceria- vs silica-supported palladium nanoparticles: A MD study using machine learning potentials Supported metallic nanoparticles play a central role in catalysis. However, predictive modeling is particularly challenging due to the structural and dynamic complexity of the nanoparticle and its interface with the support, given that the sizes of interest are often well beyond those accessible via traditional ab initio methods. With recent advances in machine learning, it is now feasible to perform MD simulations with potentials retaining near-density-functional theory (DFT) accuracy, which can elucidate the growth and relaxation of supported metal nanoparticles, as well as reactions on those catalysts, at temperatures and time scales approaching those relevant to experiments. Furthermore, the surfaces of the support materials can also be modeled realistically through simulated annealing to include effects such as defects and amorphous structures. We study the adsorption of fluorine atoms on ceria and silica supported palladium nanoparticles using machine learning potential trained by DFT data using the DeePMD framework. We show defects on ceria and Pd/ceria interfaces are crucial for the initial adsorption of fluorine, while the interplay between Pd and ceria and the reverse oxygen migration from ceria to Pd control spillover of fluorine from Pd to ceria at later stages. In contrast, silica supports do not induce fluorine spillover from Pd particles. Nanoparticle ; Materials science ; Palladium ; Fluorine ; Catalysis ; Amorphous solid ; Density functional theory ; Amorphous silica ; Ab initio ; Adsorption ; Annealing (glass) ; Nanotechnology ; Chemical engineering ; Chemical physics ; Computational chemistry ; Physical chemistry ; Chemistry ; Metallurgy ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; X-ray Diffraction in Crystallography
Authors: Himanshu | Kaushik Chakraborty | Tarak K. Patra
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Developing efficient deep learning model for predicting copolymer properties Deep learning models are gaining popularity and potency in predicting polymer properties. These models can be built using pre-existing data and are useful for the rapid prediction of polymer properties. However, the performance of a deep learning model is intricately connected to its topology and the volume of training data. There is no facile protocol available to select a deep learning architecture, and there is a lack of a large volume of homogeneous sequence-property data of polymers. These two factors are the primary bottleneck for the efficient development of deep learning models for polymers. Here we assess the severity of these factors and propose strategies to address them. We show that a linear layer-by-layer expansion of a neural network can help in identifying the best neural network topology for a given problem. Moreover, we map the discrete sequence space of a polymer to a continuous one-dimensional latent space using a feature extraction technique to identify minimal data points for training a deep learning model. We implement these approaches for two representative cases of building sequence-property surrogate models, <i>viz.</i>, the single-molecule radius of gyration of a copolymer and copolymer compatibilizer. This work demonstrates efficient methods for building deep learning models with minimal data and hyperparameters for predicting sequence-defined properties of polymers. Deep learning ; Artificial neural network ; Artificial intelligence ; Selection (genetic algorithm) ; Copolymer ; Computer science ; Deep neural networks ; Model selection ; Machine learning ; Topology (electrical circuits) ; Polymer ; Biological system ; Materials science ; Mathematics ; Biology Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Computational Drug Discovery Methods
Authors: Yanqiao Zhu | JeeHyun Hwang | Keir Adams | Zhen Liu | Bozhao Nan | Brock A. Stenfors | Yuanqi Du | Jatin Chauhan | Olaf Wiest | Olexandr Isayev | Connor W. Coley | Yizhou Sun | Wei Wang
Source: arXiv (Cornell University) (preprint, 2023-09-29)
Field: Materials Science — Materials Chemistry

Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design. While Graph Neural Networks (GNNs) are effective at learning molecular representations from a 2D molecular graph or a single 3D structure, existing works often overlook the flexible nature of molecules, which continuously interconvert across conformations via chemical bond rotations and minor vibrational perturbations. To better account for molecular flexibility, some recent works formulate MRL as an ensemble learning problem, focusing on explicitly learning from a set of conformer structures. However, most of these studies have limited datasets, tasks, and models. In this work, we introduce the first MoleculAR Conformer Ensemble Learning (MARCEL) benchmark to thoroughly evaluate the potential of learning on conformer ensembles and suggest promising research directions. MARCEL includes four datasets covering diverse molecule- and reaction-level properties of chemically diverse molecules including organocatalysts and transition-metal catalysts, extending beyond the scope of common GNN benchmarks that are confined to drug-like molecules. In addition, we conduct a comprehensive empirical study, which benchmarks representative 1D, 2D, and 3D molecular representation learning models, along with two strategies that explicitly incorporate conformer ensembles into 3D MRL models. Our findings reveal that direct learning from an accessible conformer space can improve performance on a variety of tasks and models. Conformational isomerism ; Computer science ; Molecular graph ; Benchmark (surveying) ; Artificial intelligence ; Machine learning ; Representation (politics) ; Drug discovery ; Ensemble learning ; Graph ; Molecule ; Chemistry ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Bamidele Aroboto | Shaohua Chen | Tim Hsu | Brandon C. Wood | Yang Jiao | James Chapman
Source: Applied Physics Letters (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning Materials processing often occurs under extreme dynamic conditions leading to a multitude of unique structural environments. These structural environments generally occur at high temperatures and/or high pressures, often under non-equilibrium conditions, which results in drastic changes in the material's structure over time. Computational techniques, such as molecular dynamics simulations, can probe the atomic regime under these extreme conditions. However, characterizing the resulting diverse atomistic structures as a material undergoes extreme changes in its structure has proved challenging due to the inherently non-linear relationship between structures as large-scale changes occur. Here, we introduce SODAS++, a universal graph neural network framework, that can accurately and intuitively quantify the atomistic structural evolution corresponding to the transition between any two arbitrary phases. We showcase SODAS++ for both solid–solid and solid–liquid transitions for systems of increasing geometric and chemical complexity, such as colloidal systems, elemental Al, rutile and amorphous TiO2, and the non-stoichiometric ternary alloy Ag26Au5Cu19. We show that SODAS++ can accurately quantify all transitions in a physically interpretable manner, showcasing the power of unsupervised graph neural network encodings for capturing the complex and non-linear pathway, a material's structure takes as it evolves. Amorphous solid ; Molecular dynamics ; Computer science ; Ternary operation ; Graph ; Statistical physics ; Complex system ; Materials science ; Biological system ; Artificial intelligence ; Theoretical computer science ; Physics ; Chemistry ; Computational chemistry ; Crystallography Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Chun-I Wang | J. Charlie Maier | Nicholas E. Jackson
Source: Journal of Chemical Theory and Computation (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Identifying Coarse-Grained Representations for Electronic Predictions Coarse-grained (CG) simulations are an important computational tool in chemistry and materials science. Recently, systematic "bottom-up" CG models have been introduced to capture electronic structure variations of molecules and polymers at the CG resolution. However, the performance of these models is limited by the ability to select reduced representations that preserve electronic structure information, which remains a challenge. We propose two methods for (i) identifying important electronically coupled atomic degrees of freedom and (ii) scoring the efficacy of CG representations used in conjunction with CG electronic predictions. The first method is a physically motivated approach that incorporates nuclear vibrations and electronic structure derived from simple quantum chemical calculations. We complement this physically motivated approach with a machine learning technique based on the marginal contribution of nuclear degrees of freedom to electronic prediction accuracy using an equivariant graph neural network. By integrating these two approaches, we can both identify critical electronically coupled atomic coordinates and score the efficacy of arbitrary CG representations for making electronic predictions. We leverage this capability to make a connection between optimized CG representations and the future potential for "bottom-up" development of simplified model Hamiltonians incorporating nonlinear vibrational modes. Leverage (statistics) ; Computer science ; Electronic structure ; Degrees of freedom (physics and chemistry) ; Nonlinear system ; Theoretical computer science ; Artificial intelligence ; Statistical physics ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Surface Chemistry and Catalysis