jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiang Huang | Shengluo Ma | Chenxiao Zhao | Hong Wang | Shenghong Ju
Source: arXiv (Cornell University) (preprint, 2023-01-08)
Field: Materials Science — Materials Chemistry

Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors The efficient and economical exploitation of polymers with high thermal conductivity is essential to solve the issue of heat dissipation in organic devices. Currently, the experimental preparation of functional thermal conductivity polymers remains a trial and error process due to the multi-degrees of freedom during the synthesis and characterization process. In this work, we have proposed a high-throughput screening framework for polymer chains with high thermal conductivity via interpretable machine learning and physical-feature engineering. The polymer thermal conductivity datasets for training were first collected by molecular dynamics simulation. Inspired by the drug-like small molecule representation and molecular force field, 320 polymer monomer descriptors were calculated and the 20 optimized descriptors with physical meaning were extracted by hierarchical down-selection. All the machine learning models achieve a prediction accuracy R2 greater than 0.80, which is superior to that of represented by traditional graph descriptors. Further, the cross-sectional area and dihedral stiffness descriptors were identified for positive/negative contribution to thermal conductivity, and 107 promising polymer structures with thermal conductivity greater than 20.00 W/mK were obtained. Mathematical formulas for predicting the polymer thermal conductivity were also constructed by using symbolic regression. The high thermal conductivity polymer structures are mostly π-conjugated, whose overlapping p-orbitals enable easily to maintain strong chain stiffness and large group velocities. The proposed data-driven framework should facilitate the theoretical and experimental design of polymers with desirable properties. Thermal conductivity ; Polymer ; Artificial intelligence ; Conductivity ; Computer science ; Machine learning ; Materials science ; Composite material ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal properties of materials
Authors: Esther Heid | Charles J. McGill | Florence H. Vermeire | William H. Green
Source: ChemRxiv (preprint, 2023-05-16)
Field: Materials Science — Materials Chemistry

Characterizing Uncertainty in Machine Learning for Chemistry Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further dividing epistemic uncertainty into model bias and variance contributions. We systematically address the influence of noise, model bias, and model variance in the context of chemical property predictions, where the diverse nature of target properties and the vast chemical chemical space give rise to many different distinct sources of prediction error. We demonstrate that different sources of error can each be significant in different contexts and must be individually addressed during model development. Through controlled experiments on datasets of molecular properties, we show important trends in model performance associated with the level of noise in the dataset, size of the dataset, model architecture, molecule representation, ensemble size, and dataset splitting. In particular, we show that 1) noise in the test set can limit a model's observed performance when the actual performance is much better, 2) using size-extensive model aggregation structures is crucial for extensive property prediction, and 3) ensembling is a reliable tool for uncertainty quantification and improvement specifically for the contribution of model variance. We develop general guidelines on how to improve an underperforming model when falling into different uncertainty contexts. Robustness (evolution) ; Variance (accounting) ; Computer science ; Machine learning ; Uncertainty quantification ; Artificial intelligence ; Context (archaeology) ; Noise (video) ; Representation (politics) ; Property (philosophy) ; Ensemble learning ; Reliability (semiconductor) ; Algorithm ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Reisel Millán | Estefanía Bello‐Jurado | Manual Moliner | Mercedes Boronat | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2023-05-22)
Field: Materials Science — Materials Chemistry

Effect of framework composition and NH3 on the diffusion of Cu+ in Cu-CHA catalysts predicted by machine-learning accelerated molecular dynamics Cu-exchanged zeolites rely on mobile solvated Cu+ cations for their catalytic activity, but the role of framework composition on transport is not fully understood. Ab initio molecular dynamics simulations can provide quantitative atomistic insight but are too computationally expensive to explore large length- and time-scales or diverse compositions. We report a machine-learning interatomic potential that accurately reproduces ab initio results and effectively generalizes to allow multi-nanosecond simulations of large supercells and diverse chemical compositions. Biased and unbiased simulations of [Cu(NH3)2]+ mobility show that aluminum pairing in eight-membered rings accelerates local hopping, and demonstrate that increased NH3 concentration enhances long-range diffusion. The probability of finding two [Cu(NH3)2]+ complexes in the same cage - key for SCR-NOx reaction - increases with Cu content and Al content, but does not correlate with the long-range mobility of Cu+. Supporting experimental evidence was obtained from reactivity tests of Cu-CHA catalysts with controlled chemical composition. Molecular dynamics ; Catalysis ; Diffusion ; Ab initio ; Reactivity (psychology) ; Chemistry ; Chemical physics ; Copper ; Chemical composition ; Computational chemistry ; Thermodynamics ; Physics ; Organic chemistry Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Mingjian Wen | Matthew K. Horton | Jason M. Munro | Patrick Huck | Kristin A. Persson
Source: arXiv (Cornell University) (preprint, 2023-07-28)
Field: Materials Science — Materials Chemistry

An equivariant graph neural network for the elasticity tensors of all seven crystal systems The elasticity tensor that describes the elastic response of a material to external forces is among the most fundamental properties of materials. The availability of full elasticity tensors for inorganic crystalline compounds, however, is limited due to experimental and computational challenges. Here, we report the materials tensor (MatTen) model for rapid and accurate estimation of the full fourth-rank elasticity tensors of crystals. Based on equivariant graph neural networks, MatTen satisfies the two essential requirements for elasticity tensors: independence of the frame of reference and preservation of material symmetry. Consequently, it provides a unified treatment of elasticity tensors for all seven crystal systems across diverse chemical spaces, without the need to deal with each separately.. MatTen was trained on a dataset of first-principles elasticity tensors garnered by the Materials Project over the past several years (we are releasing the data herein) and has broad applications in predicting the isotropic elastic properties of polycrystalline materials, examining the anisotropic behavior of single crystals, and discovering new materials with exceptional mechanical properties. Using MatTen, we have discovered a hundred new crystals with extremely large maximum directional Young's modulus and eleven polymorphs of elemental cubic metals with unconventional spatial orientation of Young's modulus. Equivariant map ; Graph ; Elasticity (physics) ; Mathematics ; Pure mathematics ; Computer science ; Discrete mathematics ; Materials science ; Composite material Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Electric Power Systems and Control
Authors: Hongjun Xiang | Hongyu Yu | Yang Zhong | Liangliang Hong | Changsong Xu | Wei Ren | Xin-Gao Gong
Source: Research Square (preprint, 2023-05-11)
Field: Materials Science — Materials Chemistry

Spin-Dependent Graph Neural Network Potential for Magnetic Materials Abstract The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals. However, creating interatomic potentials for magnetic systems that account for both magnetic moments and structural degrees of freedom remains a challenge. This work introduces SpinGNN, a spin-dependent interatomic potential approach that employs the graph neural network (GNN) to describe magnetic systems. SpinGNN consists of two types of edge GNNs: Heisenberg edge GNN (HEGNN) and spin-distance edge GNN (SEGNN). HEGNN is tailored to capture Heisenberg-type spin-lattice interactions, while SEGNN accurately models multi-body and high-order spin-lattice coupling. The effectiveness of SpinGNN is demonstrated by its exceptional precision in fitting a high-order spin Hamiltonian and two complex spin-lattice Hamiltonians with great precision. Furthermore, it successfully models the subtle spin-lattice coupling in BiFeO 3 and performs large-scale spin-lattice dynamics simulations, predicting its antiferromagnetic ground state, magnetic phase transition, and domain wall energy landscape with high accuracy. Our study broadens the scope of graph neural network potentials to magnetic systems, serving as a foundation for carrying out large-scale spin-lattice dynamic simulations of such systems. Heisenberg model ; Condensed matter physics ; Physics ; Interatomic potential ; Lattice (music) ; Antiferromagnetism ; Hamiltonian (control theory) ; Ground state ; Statistical physics ; Artificial neural network ; Computer science ; Molecular dynamics ; Quantum mechanics ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Quantum many-body systems ; Physics of Superconductivity and Magnetism
Authors: Jianan Zhang | Aditya Koneru | Subramanian K. R. S. Sankaranarayanan | Carmen M. Lilley
Source: ChemRxiv (preprint, 2023-02-16)
Field: Materials Science — Materials Chemistry

Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials Grain boundaries (GBs) in two-dimensional (2D) materials are known to dramatically impact material properties ranging from the physical, chemical, mechanical, electronic, and optical to name a few. Predicting a range of physically realistic GB structures for 2D materials is critical to exercising control over their properties. This, however, is non-trivial given the vast structural and configurational (defect) search space between lateral 2D sheets with varying misfits. Here, in a departure from traditional evolutionary search methods, we introduce a workflow that combines the Graph Neural Network (GNN) and an evolutionary algorithm for the discovery and design of novel 2D lateral interfaces. We use a representative 2D material, blue phosphorene (BP), and identify 2D GB structures to test the efficacy of our GNN model. The GNN was trained with a computationally inexpensive machine learning bond order potential (Tersoff formalism), and density functional theory (DFT). Systematic downsampling of the training datasets indicates that our model can predict structural energy under 0.5% mean absolute error with sparse (< 2000) DFT generated energy labels for training. We further couple the GNN model with a multi-objective genetic algorithm (MOGA) and demonstrate strong accuracy in the ability of the GNN to predict GBs. Our method is generalizable, material agnostic, and is anticipated to accelerate the discovery of 2D GB structures. Computer science ; Workflow ; Artificial neural network ; Evolutionary algorithm ; Algorithm ; Artificial intelligence ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Tomomi Shimazaki | Masanori Tachikawa
Source: Chemical Physics Letters (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

Time-dependent dielectric density functional theory predictions for excited-state molecular properties using neural networks guided by lower-level quantum chemistry Excited state ; Quantum chemistry ; Artificial neural network ; Density functional theory ; Quantum ; Chemistry ; Dielectric ; Computational chemistry ; Statistical physics ; Computer science ; Quantum mechanics ; Molecule ; Physics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Pakawat Sungphueng | Kittiphong Amnuyswat
Source: Current Applied Science and Technology (article, 2023-04-19)
Field: Materials Science — Materials Chemistry

Thermoelectric Prediction from Material Descriptors Using Machine Learning Technique In this work, we employed a machine learning framework to predict the thermoelectric power factors of materials based on their composition and structure. To generate a broad range of materials for analysis, we sourced an existing dataset from the Materials Project database. The electronic transport properties, which serve as the output variables, were obtained from the same database via a Boltzmann transport theory calculation beyond ab-initio method. These properties were used to generate input data, or material descriptors, which rely solely on atomic information and crystal structure without recourse to density functional theory calculations. The descriptors were transformed into numerical features using the open-source software Matminer. Non-linear machine learning regression models were trained and tested on the transformed datasets, and their performance was evaluated. The optimized random forest model produced the most accurate predictions, with a yield of 88%. The ultimate goals of this research were to develop material selection strategies that bypass the need for self-consumption in density functional theory calculations, and to demonstrate the potential of machine learning models to describe the thermoelectric properties of existing materials datasets. Computer science ; Machine learning ; Artificial intelligence ; Random forest ; Thermoelectric effect ; Boltzmann machine ; Work (physics) ; Ab initio ; Range (aeronautics) ; Density functional theory ; Thermoelectric materials ; Software ; Data mining ; Materials science ; Deep learning ; Chemistry ; Mechanical engineering ; Physics ; Engineering Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Kianoosh Sattari | Lucas Eddy | Jacob L. Beckham | Kevin M. Wyss | Richard Byfield | Long Qian | James M. Tour | Jian Lin
Source: arXiv (Cornell University) (preprint, 2023-03-22)
Field: Materials Science — Materials Chemistry

Scientific Machine Learning Framework to Understand Flash Graphene Synthesis Flash Joule heating (FJH) is a far-from-equilibrium (FFE) processing method for converting low-value carbon-based materials to flash graphene (FG). Despite its promise in scalability and performance, attempts to explore the reaction mechanism have been limited due to complexity involved in the FFE process. Data-driven machine learning (ML) models effectively account for this complexity, but the model training requires considerable amount of experimental data. To tackle this challenge, we constructed a scientific ML (SML) framework trained by using both direct processing variables and indirect, physics-informed variables to predict the FG yield. The indirect variables include current-derived features (final current, maximum current, and charge density) predicted from the proxy ML models and reaction temperatures simulated from multi-physics modeling. With the combined indirect features, the final ML model achieves an average R2 score of 0.81 +/- 0.05 and an average RMSE of 12.1% +/- 2.0% in predicting the FG yield, which is significantly higher than the model trained without them (R2 of 0.73 +/- 0.05 and an RMSE of 14.3% +/- 2.0%). Feature importance analysis validates the key roles of these indirect features in determining the reaction outcome. These results illustrate the promise of this SML to elucidate FFE material synthesis outcomes, thus paving a new avenue to processing other datasets from the materials systems involving the same or different FFE processes. Scalability ; Graphene ; Joule heating ; Computer science ; Machine learning ; Yield (engineering) ; Flash (photography) ; Artificial intelligence ; Algorithm ; Materials science ; Nanotechnology ; Physics Machine Learning in Materials Science ; Graphene research and applications ; Advancements in Battery Materials
Authors: Matthew Witman | N. C. Bartelt | Sanliang Ling | Pin-Wen Guan | Lauren Way | Mark D. Allendorf | Vitalie Stavila
Source: ChemRxiv (preprint, 2023-11-21)
Field: Materials Science — Materials Chemistry

Phase diagrams of alloys and their hydrides via on-lattice graph neural networks and limited training data Efficient prediction of sampling-intensive thermodynamic properties in both closed and open systems is needed to evaluate material performance and permit high-throughput materials discovery for a diverse array of technology applications, from rational design of low-density high entropy alloys, to optimizing battery cathodes, to modulating hydrogen absorption equilibria in complex metals. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient, but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict, and can even extrapolate to out-of-train-distribution, formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training datasets. Two exemplars, optimizing thermodynamic stability of low-density high entropy alloys and the modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which will be extendable to a variety of materials discovery and modeling problems. Computer science ; Artificial neural network ; Cluster expansion ; Density functional theory ; Lattice (music) ; Complex system ; Statistical physics ; Artificial intelligence ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Masahiro Kusumi | Bunta Inoue | Yoshihiko Hirai | Masaaki Yasuda
Source: Microscopy (article, 2023-06-10)
Field: Materials Science — Surfaces, Coatings and Films

Prediction of secondary electron yield for metal materials using deep learning This article describes a neural network system for predicting the secondary electron yield of metallic materials. For bulk metals, experimental values are used as training data. Due to the strong correlation between the secondary electron yield and the work function, deep learning predicts the secondary electron yield with relatively high accuracy even with a small amount of training data. Our approach demonstrates the importance of the work function in predicting the secondary electron yield. For the secondary electron yield of thin metal films on metal substrates, deep learning predictions are generated using training data obtained by Monte Carlo simulations. The accuracy of the secondary yield predictions of thin films on substrates could be improved by adding experimental values of bulk metals to the training data. Yield (engineering) ; Secondary electrons ; Work function ; Monte Carlo method ; Materials science ; Electron ; Work (physics) ; Metal ; Artificial neural network ; Machine learning ; Computer science ; Metallurgy ; Physics ; Mathematics ; Statistics ; Thermodynamics ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Surface and Thin Film Phenomena
Authors: Akihiro Kishimoto | Hiroshi Kajino | Masataka Hirose | Junta Fuchiwaki | Indra Priyadarsini | Lisa Hamada | Hajime Shinohara | Daiju Nakano | Seiji Takeda
Source: arXiv (Cornell University) (preprint, 2023-09-28)
Field: Materials Science — Materials Chemistry

MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network Property prediction plays an important role in material discovery. As an initial step to eventually develop a foundation model for material science, we introduce a new autoencoder called the MHG-GNN, which combines graph neural network (GNN) with Molecular Hypergraph Grammar (MHG). Results on a variety of property prediction tasks with diverse materials show that MHG-GNN is promising. Hypergraph ; Computer science ; Graph ; Property (philosophy) ; Autoencoder ; Artificial neural network ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Discrete mathematics Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Minjie Chen | Haoran Li | Diego Serrano | Shukai Wang
Source:  (preprint, 2023-05-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MagNet-AI: Neural Network as Datasheet for Magnetics Modeling and Material Recommendation &lt;p&gt;This paper presents the MagNet-AI platform as an online platform to demonstrate the “Neural Network as Datasheet” concept for B–H loop modeling and material recommendation of power magnetics across wide operation range. Instead of directly presenting the measured characteristics of magnetic core materials as time sequences, we employ a neural network to capture the B–H loop mapping relationships of magnetic materials under different excitation waveforms at different temperatures and dc-bias. Both LSTM and Transformer based neural network models are developed, verified, and compared. The training and inference process of the neural network are fully automated to minimize the impact of human error. The neural network can be used to rapidly predict B–H loops under different operating conditions, compare materials, and recommend materials for design. Neural networks are also effective in compressing the information contained in the raw database, and enable rapid material evaluation and comparison.&lt;/p&gt; Datasheet ; Artificial neural network ; Transformer ; Inference ; Computer science ; Magnet ; Waveform ; Process (computing) ; Toolchain ; Artificial intelligence ; Machine learning ; Engineering ; Voltage ; Electrical engineering ; Telecommunications Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Non-Destructive Testing Techniques