jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vaibhav Bihani | Utkarsh Pratiush | Sajid Mannan | Tao Du | Zhimin Chen | Santiago Miret | M. Micoulaut | Morten M. Smedskjær | Sayan Ranu | N. M. Anoop Krishnan
Source: arXiv (Cornell University) (preprint, 2023-10-03)
Field: Materials Science — Materials Chemistry

EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations Equivariant graph neural networks force fields (EGraFFs) have shown great promise in modelling complex interactions in atomic systems by exploiting the graphs' inherent symmetries. Recent works have led to a surge in the development of novel architectures that incorporate equivariance-based inductive biases alongside architectural innovations like graph transformers and message passing to model atomic interactions. However, thorough evaluations of these deploying EGraFFs for the downstream task of real-world atomistic simulations, is lacking. To this end, here we perform a systematic benchmarking of 6 EGraFF algorithms (NequIP, Allegro, BOTNet, MACE, Equiformer, TorchMDNet), with the aim of understanding their capabilities and limitations for realistic atomistic simulations. In addition to our thorough evaluation and analysis on eight existing datasets based on the benchmarking literature, we release two new benchmark datasets, propose four new metrics, and three challenging tasks. The new datasets and tasks evaluate the performance of EGraFF to out-of-distribution data, in terms of different crystal structures, temperatures, and new molecules. Interestingly, evaluation of the EGraFF models based on dynamic simulations reveals that having a lower error on energy or force does not guarantee stable or reliable simulation or faithful replication of the atomic structures. Moreover, we find that no model clearly outperforms other models on all datasets and tasks. Importantly, we show that the performance of all the models on out-of-distribution datasets is unreliable, pointing to the need for the development of a foundation model for force fields that can be used in real-world simulations. In summary, this work establishes a rigorous framework for evaluating machine learning force fields in the context of atomic simulations and points to open research challenges within this domain. Benchmarking ; Computer science ; Graph ; Theoretical computer science ; Scalability ; Distributed computing ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Taoyong Cui | Chenyu Tang | Mao Su | Shufei Zhang | Yuqiang Li | Lei Bai | Yuhan Dong | Xin-Gao Gong | Wanli Ouyang
Source: arXiv (Cornell University) (preprint, 2023-09-27)
Field: Materials Science — Materials Chemistry

Geometry-enhanced Pre-training on Interatomic Potentials Machine learning interatomic potentials (MLIPs) enables molecular dynamics (MD) simulations with ab initio accuracy and has been applied to various fields of physical science. However, the performance and transferability of MLIPs are limited by insufficient labeled training data, which require expensive ab initio calculations to obtain the labels, especially for complex molecular systems. To address this challenge, we design a novel geometric structure learning paradigm that consists of two stages. We first generate a large quantity of 3D configurations of target molecular system with classical molecular dynamics simulations. Then, we propose geometry-enhanced self-supervised learning consisting of masking, denoising, and contrastive learning to better capture the topology and 3D geometric information from the unlabeled 3D configurations. We evaluate our method on various benchmarks ranging from small molecule datasets to complex periodic molecular systems with more types of elements. The experimental results show that the proposed pre-training method can greatly enhance the accuracy of MLIPs with few extra computational costs and works well with different invariant or equivariant graph neural network architectures. Our method improves the generalization capability of MLIPs and helps to realize accurate MD simulations for complex molecular systems. Computer science ; Molecular dynamics ; Invariant (physics) ; Transferability ; Ab initio ; Generalization ; Artificial neural network ; Artificial intelligence ; Ranging ; Topology (electrical circuits) ; Algorithm ; Machine learning ; Physics ; Computational chemistry ; Mathematics ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Jinde Liu | Chenglong Qin | Xi He | Gang Jiang
Source: arXiv (Cornell University) (preprint, 2023-12-23)
Field: Materials Science — Materials Chemistry

Calculate electronic excited states using neural networks with effective core potential The essence of atomic structure theory, quantum chemistry, and computational materials science is solving the multi-electron stationary Schrödinger equation. The Quantum Monte Carlo-based neural network wave function method has surpassed traditional post-Hartree-Fock methods in precision across various systems. However, its energy uncertainty is limited to 0.01%, posing challenges in accurately determining excited states and ionization energies, especially for elements beyond the fourth period. Using effective core potentials to account for inner electrons enhances the precision of vertical excitation and ionization energies. This approach has proved effective in computing ground state energies for elements like Lithium to Gallium and in calculating energy levels and wave functions for atoms and molecules with second and fourth period elements. Additionally, by integrating effective core potentials with Ferminet, we've achieved multiple excited state calculations with a precision comparable to experimental results, marking a significant advancement in practical applications and setting a new standard for theoretical excited state calculations. Excited state ; Wave function ; Ionization ; Atomic physics ; Physics ; Ionization energy ; Variational Monte Carlo ; Quantum ; Electron ; Excitation ; Computational physics ; Statistical physics ; Quantum mechanics ; Ion Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Kaizheng Yang | Yichao Zhu
Source: International Journal of Plasticity (article, 2023-12-12)
Field: Materials Science — Materials Chemistry

A general scheme for point defect sink strength calculation and related machine-learning-based expressions Materials science ; Sink (geography) ; Dislocation ; Void (composites) ; Mechanics ; Hydrostatic equilibrium ; Statistical physics ; Computer science ; Composite material ; Physics Nuclear Materials and Properties ; Fusion materials and technologies ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Javier F. Troncoso | Yang Hu | Nicolò Maria della Ventura | Amit Sharma | Xavier Maeder | Vladyslav Turlo
Source: Computational Materials Science (article, 2023-06-19)
Field: Materials Science — Materials Chemistry

Machine learning of twin/matrix interfaces from local stress field Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic simulations an attractive method for investigating the role of individual twin/matrix interfaces such as twin boundaries and basal-prismatic interfaces in twin growth kinetics. Unfortunately, there is no single framework that allows researchers to differentiate such interfaces automatically, neither in experimental in-situ transmission electron microscopy analysis images nor in atomistic simulations. Moreover, the presence of alloying elements introduces substantial noise to local atomic environments, making it nearly impossible to identify which atoms belong to which interface. Here, with the help of advanced machine learning methods, we provide a proof-of-concept way of using the local stress field distribution as an indicator for the presence of interfaces and for determining their types. We apply such an analysis to the growth of twin embryos in Mg-10 at.% Al alloys under constant stress and constant strain conditions, corresponding to two extremes of high and low strain rates, respectively. We discover that the kinetics of such growth is driven by high-energy basal-prismatic interfaces, in line with our experimental observations for pure Mg. • Applied ML to atomistic simulations of twin embryo growth in Mg alloy. • Identified and tracked twin-matrix interfaces using stress components. • Low strain rate: growth driven by low-energy twin boundaries. • High strain rate: growth driven by high-energy basal-prismatic interfaces. • Results are experimentally validated for later twin growth stages. Crystal twinning ; Materials science ; Stress (linguistics) ; Field (mathematics) ; Stress field ; Transmission electron microscopy ; Matrix (chemical analysis) ; Deformation (meteorology) ; Characterization (materials science) ; Crystallography ; Chemical physics ; Nanotechnology ; Chemistry ; Physics ; Thermodynamics ; Composite material ; Finite element method ; Microstructure ; Mathematics Microstructure and mechanical properties ; Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties
Authors: Shenglin Huang | Zequn He | Nicolas Dirr | Johannes Zimmer | Celia Reina
Source: arXiv (Cornell University) (preprint, 2023-12-10)
Field: Materials Science — Materials Chemistry

Statistical-Physics-Informed Neural Networks (Stat-PINNs): A Machine Learning Strategy for Coarse-graining Dissipative Dynamics Machine learning, with its remarkable ability for retrieving information and identifying patterns from data, has emerged as a powerful tool for discovering governing equations. It has been increasingly informed by physics, and more recently by thermodynamics, to further uncover the thermodynamic structure underlying the evolution equations, i.e., the thermodynamic potentials driving the system and the operators governing the kinetics. However, despite its great success, the inverse problem of thermodynamic model discovery from macroscopic data is in many cases non-unique, meaning that multiple pairs of potentials and operators can give rise to the same macroscopic dynamics, which significantly hinders the physical interpretability of the learned models. In this work, we propose a machine learning framework, named as Statistical-Physics-Informed Neural Networks (Stat-PINNs), which further encodes knowledge from statistical mechanics and resolves this non-uniqueness issue for the first time. The framework is here developed for purely dissipative isothermal systems. It only uses data from short-time particle simulations to learn the thermodynamic structure, which can be used to predict long-time macroscopic evolutions. We demonstrate the approach for particle systems with Arrhenius-type interactions, common to a wide range of phenomena, such as defect diffusion in solids, surface absorption and chemical reactions. Stat-PINNs can successfully recover the known analytic solution for the case with long-range interaction and discover the hitherto unknown potential and operator governing the short-range interaction cases. We compare our results with an analogous approach that solely excludes statistical mechanics, and observe that, in addition to recovering the unique thermodynamic structure, statistical mechanics relations can increase the robustness and predictability of the learning strategy. Interpretability ; Statistical mechanics ; Dissipative system ; Statistical physics ; Artificial neural network ; Computer science ; Range (aeronautics) ; Granularity ; Physics ; Artificial intelligence ; Thermodynamics ; Engineering Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Zhanpeng Sun | Zijun Qi | Kang Liang | Xiang Sun | Zhaofu Zhang | Lijie Li | Qijun Wang | Gai Wu | Wei Shen
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Neuroevolution Potential for Predicting the Thermal Conductivity of Α, Β, and Ε-Ga2o3 Neuroevolution ; Thermal conductivity ; Computer science ; Artificial intelligence ; Artificial neural network ; Thermodynamics ; Physics Ga2O3 and related materials ; Thermal Expansion and Ionic Conductivity ; Transition Metal Oxide Nanomaterials
Authors: Bojun Liu | Mingyi Xue | Yunrui Qiu | Kirill A. Konovalov | Michael B. O’Connor | Xuhui Huang
Source: ChemRxiv (preprint, 2023-05-18)
Field: Materials Science — Biomaterials

GraphVAMPnets for Uncovering Slow Collective Variables of Self-Assembly Dynamics Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack the high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenge. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, like tICA, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets which combines graph neural networks with variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in a high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory that studies Markov processes without forcing detailed balance constraint, which addresses the out-of-equilibrium challenge in self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: aggregation of two hydrophobic molecules and self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely to applied to molecular self-assembly. Pairwise comparison ; Computer science ; Detailed balance ; Homogeneous space ; Dimensionality reduction ; Graph ; Statistical physics ; Theoretical computer science ; Algorithm ; Mathematics ; Physics ; Artificial intelligence ; Geometry Supramolecular Self-Assembly in Materials ; Pickering emulsions and particle stabilization ; Machine Learning in Materials Science
Authors: Davor Donevski | Tamara Tomašegović | Sanja Mahović Poljaček
Source: Machines (article, 2023-06-03)
Field: Chemistry — Organic Chemistry

Optimizing the Neural Network Architecture for Automation of the Tailored UV Post-Treatment of Photopolymer Printing Plates In this work, three types of photopolymer printing plates for packaging printing were subjected to varied UV (ultraviolet radiation) post-treatments, and their surface free energy (SFE) components were calculated. SFE of the photopolymer printing plate is crucial in the process of transferring the ink from the printing plate to the substrate. Calculated polar and dispersive SFE components were used to build and optimize artificial neural networks for the prediction of the surface properties of different photopolymer materials after the performed UVA and UVC post-treatments. In this way, the production of printing plates with tailored SFE components could be automated and optimized. Consequently, products with improved qualitative properties could be printed. Results of the research have shown that the choice of the neural network’s activation function is most significant for the minimization of the mean squared error (MSE), while the number of neurons and hidden layers in neural networks has less influence on MSE. The optimized neural networks applied for common photopolymer materials in this work have the potential to be applied for the automation of the printing plates’ post-treatment process and the production of printing plates with surface properties tailored to specific printing systems. Photopolymer ; Materials science ; Inkwell ; Artificial neural network ; 3D printing ; Automation ; Process (computing) ; Substrate (aquarium) ; Ultraviolet ; Computer science ; Process engineering ; Composite material ; Mechanical engineering ; Optoelectronics ; Artificial intelligence ; Polymer ; Engineering Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies ; Color Science and Applications
Authors: Jingqi Wang | Jiapeng Liu | Hongshuai Wang | Guolin Ke | Linfeng Zhang | Jian Wu | Zhifeng Gao | Diannan Lu
Source: ChemRxiv (preprint, 2023-04-25)
Field: Chemistry — Inorganic Chemistry

Metal-organic frameworks meet Uni-MOF: a revolutionary gas adsorption detector Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks(MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for universal multi-gas prediction. Specifically, Uni-MOF serves as a versatile "gas adsorption detector" for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Impressively, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties. Overfitting ; Metal-organic framework ; Adsorption ; Computer science ; Representation (politics) ; Scale (ratio) ; Feature (linguistics) ; Process engineering ; Materials science ; Nanotechnology ; Artificial intelligence ; Artificial neural network ; Chemistry ; Engineering ; Physics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Kelly Cristine da Silveira | M H S Siqueira | João Matheus Ramos Gama | Jonathan N. Gois | Cláudio Fabiano Motta Toledo | Antônio José da Silva Neto
Source: VETOR - Revista de Ciências Exatas e Engenharias (article, 2023-06-28)
Field: Materials Science — Polymers and Plastics

A Comparison of Machine Learning Approaches in Predicting Viscosity for Partially Hydrolyzed Polyacrylamide Derivatives Partially hydrolyzed polyacrylamides (HPAM) are widely used to modulate the viscosity of formulations. The appropriate application of a viscosity model can facilitate the idealization of new macromolecules and contribute to a better understanding of the structure-property relationship. In the present study, machine learning approaches, Multiple Linear Regression (MLR) and Random Forest (RF), were compared to model the viscosifying effect of HPAM derivatives, based on their chemical composition and concentration in an aqueous solution. The evaluated data come from a previous experimental study, which explores a post-synthetic polymer modification methodology. The relative importance of the variables was investigated, determining the features with the greatest influence on viscosity, including variations in chemical composition, with emphasis on the more hydrophobic groups (C7 and C12). The accuracy of the models was evaluated using statistical criteria, the coefficient of determination (R2) and the Root Mean Square Error (RMSE). The Random Forest approach outperformed Multiple Linear Regression, with values of 0.97 and 0.30 for R2 and RMSE, respectively, compared to 0.83 and 0.67 for Multiple Linear Regression. Applying the Random Forest model, it was possible to generate a set of hypothetical macromolecules, with potential viscosifying effects. These macromolecules were idealized focusing on mixed compositions of C7 and C12 with a maximum structural variation of 10 mol%. Additionally, this structural mapping provided insights for designing promising polymers by inserting cyclic structures, such as CYCLOPROP, which could overcome the solubility limitation observed in the literature. Viscosity ; Macromolecule ; Biological system ; Linear regression ; Mean squared error ; Chemistry ; Polymer ; Solubility ; Hydrolysis ; Regression ; Coefficient of determination ; Aqueous solution ; Random forest ; Thermodynamics ; Mathematics ; Artificial intelligence ; Computer science ; Machine learning ; Organic chemistry ; Statistics Polymer crystallization and properties ; Environmental Impact and Sustainability ; Asphalt Pavement Performance Evaluation
Authors: Xiangkun Wu | Yu Liu
Source: The Journal of Physical Chemistry Letters (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Predicting Gas Adsorption without the Knowledge of Pore Structures: A Machine Learning Method Based on Classical Density Functional Theory Predicting gas adsorption from the pore structure is an intuitive and widely used methodology in adsorption. However, in real-world systems, the structural information is usually very complicated and can only be approximately obtained from the characterization data. In this work, we developed a machine learning (ML) method to predict gas adsorption form the raw characterization data of N<sub>2</sub> adsorption. The ML method is modeled by a convolutional neural network and trained by a large number of data that are generated from a classical density functional theory, and the model gives a very accurate prediction of Ar adsorption. Though the training set is limited to modeling slit pores, the model can be applied to three-dimensional structured pores and real-world materials. The good agreements suggest that there is a universal relationship among adsorption isotherms of different adsorbates, which could be captured by the ML model. Adsorption ; Density functional theory ; Characterization (materials science) ; Set (abstract data type) ; Artificial neural network ; Computer science ; Work (physics) ; Convolutional neural network ; Artificial intelligence ; Materials science ; Biological system ; Chemistry ; Thermodynamics ; Nanotechnology ; Physical chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics