jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryotaro Okabe | Abhijatmedhi Chotrattanapituk | Artittaya Boonkird | Nina Andrejevic | Xiang Fu | Tommi Jaakkola | Qichen Song | Thanh Bình Nguyễn | Nathan C. Drucker | Sai Mu | Bolin Liao | Yongqiang Cheng | Mingda Li
Source: arXiv (Cornell University) (preprint, 2023-01-05)
Field: Materials Science — Materials Chemistry

Virtual Node Graph Neural Network for Full Phonon Prediction The structure-property relationship plays a central role in materials science. Understanding the structure-property relationship in solid-state materials is crucial for structure design with optimized properties. The past few years witnessed remarkable progress in correlating structures with properties in crystalline materials, such as machine learning methods and particularly graph neural networks as a natural representation of crystal structures. However, significant challenges remain, including predicting properties with complex unit cells input and material-dependent, variable-length output. Here we present the virtual node graph neural network to address the challenges. By developing three types of virtual node approaches - the vector, matrix, and momentum-dependent matrix virtual nodes, we achieve direct prediction of $Γ$-phonon spectra and full dispersion only using atomic coordinates as input. We validate the phonon bandstructures on various alloy systems, and further build a $Γ$-phonon database containing over 146,000 materials in the Materials Project. Our work provides an avenue for rapid and high-quality prediction of phonon spectra and bandstructures in complex materials, and enables materials design with superior phonon properties for energy applications. The virtual node augmentation of graph neural networks also sheds light on designing other functional properties with a new level of flexibility. Phonon ; Computer science ; Artificial neural network ; Node (physics) ; Graph ; Topology (electrical circuits) ; Materials science ; Artificial intelligence ; Theoretical computer science ; Computational science ; Physics ; Mathematics ; Condensed matter physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Ruichen Li | Haotian Ye | Du Jiang | Xuelan Wen | Chuwei Wang | Zhe Li | Xiang Li | Di He | Ji Chen | Weiluo Ren | Liwei Wang
Source: arXiv (Cornell University) (preprint, 2023-07-17)
Field: Materials Science — Materials Chemistry

Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of existing approaches hinders their applications in realistic chemistry problems. Here, we report the development of a new NN-VMC method that achieves a remarkable speed-up by more than one order of magnitude, thereby greatly extending the applicability of NN-VMC to larger systems. Our key design is a novel computational framework named Forward Laplacian, which computes the Laplacian associated with neural networks, the bottleneck of NN-VMC, through an efficient forward propagation process. We then demonstrate that Forward Laplacian is not only versatile but also facilitates more developments of acceleration methods across various aspects, including optimization for sparse derivative matrix and efficient neural network design. Empirically, our approach enables NN-VMC to investigate a broader range of atoms, molecules and chemical reactions for the first time, providing valuable references to other ab initio methods. The results demonstrate a great potential in applying deep learning methods to solve general quantum mechanical problems. Computer science ; Artificial neural network ; Bottleneck ; Ab initio ; Monte Carlo method ; Quantum ; Quantum Monte Carlo ; Laplace operator ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Kumar Ayush | Pooja Sahu | Sk. Musharaf Ali | Tarak K. Patra
Source: arXiv (Cornell University) (preprint, 2023-08-22)
Field: Materials Science — Ceramics and Composites

Predicting Pair Correlation Functions of Glasses using Machine Learning Glasses offer a broad range of tunable thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relation of glasses due to their enormous composition and chemical space. Here, we address this problem and develop a metamodel of composition-atomistic structure relation of a class of glassy material via a machine learning (ML) approach. Within this ML framework, an unsupervised deep learning technique, viz. convolutional neural network (CNN) autoencoder, and a regression algorithm, viz. random forest (RF), are integrated into a fully automated pipeline to predict the spatial distribution of atoms in a glass. The RF regression model predicts the pair correlation function of a glass in a latent space. Subsequently, the decoder of the CNN converts the latent space representation to the actual pair correlation function of the given glass. The atomistic structures of silicate (SiO2) and sodium borosilicate (NBS) based glasses with varying compositions and dopants are collected from molecular dynamics (MD) simulations to establish and validate this ML pipeline. The model is found to predict the atom pair correlation function for many unknown glasses very accurately. This method is very generic and can accelerate the design, discovery, and fundamental understanding of composition-atomistic structure relations of glasses and other materials. Pipeline (software) ; Representation (politics) ; Artificial intelligence ; Convolutional neural network ; Atom (system on chip) ; Autoencoder ; Computer science ; Deep learning ; Function (biology) ; Algorithm ; Materials science ; Machine learning ; Statistical physics ; Physics Glass properties and applications
Authors: Santosh Adhikari | Christopher J. Bartel | Christopher Sutton
Source: arXiv (Cornell University) (preprint, 2023-07-14)
Field: Materials Science — Materials Chemistry

Interpretable machine learning to understand the performance of semi local density functionals for materials thermochemistry This study investigates the use of machine learning (ML) to correct the enthalpy of formation (Hf) from two separate DFT functionals, PBE and SCAN, to the experimental Hf across 1011 solid-state compounds. The ML model uses a set of 25 properties that characterize the electronic structure as calculated using PBE and SCAN. The ML model significantly decreases the error in PBE-calculated Hf values from an mean absolute error (MAE) of 195 meV/atom to an MAE = 80 meV/atom when compared to the experiment. For PBE, the PDP+GAM analysis shows compounds with a high ionicity (I), i.e., I&gt;0.22, have errors in Hf that are twice as large as compounds having I &lt; 0.22 (246 meV/atom compared to 113 meV/atom). Conversely, no analogous trend is observed for SCAN-calculated Hfs, which explains why the ML model for PBE can more easily correct the systematic error in calculated Hfs for PBE but not for SCAN. Although the literature suggests PBE is reliable for intermetallics but less so for oxides and halides, our analysis reveals intermetallics pose a challenge for PBE only when the charge transfer is significant (I &gt;0.22). Meanwhile, oxides and halides may be described accurately by PBE for systems in which charge transfer is relatively low (I &lt; 0.22). Intermetallic ; Thermochemistry ; Atom (system on chip) ; Halide ; Chemistry ; Mean absolute error ; Standard enthalpy of formation ; Density functional theory ; Enthalpy ; Standard enthalpy change of formation ; Computational chemistry ; Atomic physics ; Materials science ; Physical chemistry ; Thermodynamics ; Mathematics ; Physics ; Computer science ; Inorganic chemistry ; Alloy ; Mean squared error ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Thermal and Kinetic Analysis
Authors: Guangxuan Song | Dongmei Fu | Zhongwei Qiu | Zijiang Yang | Jiaxin Dai | Lingwei Ma | Dawei Zhang
Source: arXiv (Cornell University) (preprint, 2023-12-15)
Field: Materials Science — Materials Chemistry

Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction Using machine learning (ML) techniques to predict material properties is a crucial research topic. These properties depend on numerical data and semantic factors. Due to the limitations of small-sample datasets, existing methods typically adopt ML algorithms to regress numerical properties or transfer other pre-trained knowledge graphs (KGs) to the material. However, these methods cannot simultaneously handle semantic and numerical information. In this paper, we propose a numerical reasoning method for material KGs (NR-KG), which constructs a cross-modal KG using semantic nodes and numerical proxy nodes. It captures both types of information by projecting KG into a canonical KG and utilizes a graph neural network to predict material properties. In this process, a novel projection prediction loss is proposed to extract semantic features from numerical information. NR-KG facilitates end-to-end processing of cross-modal data, mining relationships and cross-modal information in small-sample datasets, and fully utilizes valuable experimental data to enhance material prediction. We further propose two new High-Entropy Alloys (HEA) property datasets with semantic descriptions. NR-KG outperforms state-of-the-art (SOTA) methods, achieving relative improvements of 25.9% and 16.1% on two material datasets. Besides, NR-KG surpasses SOTA methods on two public physical chemistry molecular datasets, showing improvements of 22.2% and 54.3%, highlighting its potential application and generalizability. We hope the proposed datasets, algorithms, and pre-trained models can facilitate the communities of KG and AI for materials. Computer science ; Modal ; Generalizability theory ; Bridging (networking) ; Artificial neural network ; Graph ; Data mining ; Artificial intelligence ; Machine learning ; Algorithm ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Bo Qiang | Yiran Zhou | Yuheng Ding | Ningfeng Liu | Song Song | Liangren Zhang | Bo Huang | Zhenming Liu
Source: arXiv (Cornell University) (preprint, 2023-03-13)
Field: Materials Science — Materials Chemistry

Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model Chemical reactions are the fundamental building blocks of drug design and organic chemistry research. In recent years, there has been a growing need for a large-scale deep-learning framework that can efficiently capture the basic rules of chemical reactions. In this paper, we have proposed a unified framework that addresses both the reaction representation learning and molecule generation tasks, which allows for a more holistic approach. Inspired by the organic chemistry mechanism, we develop a novel pretraining framework that enables us to incorporate inductive biases into the model. Our framework achieves state-of-the-art results on challenging downstream tasks. By possessing chemical knowledge, our generative framework overcome the limitations of current molecule generation models that rely on a small number of reaction templates. In the extensive experiments, our model generates synthesizable drug-like structures of high quality. Overall, our work presents a significant step toward a large-scale deep-learning framework for a variety of reaction-based applications. Bridging (networking) ; Computer science ; Variety (cybernetics) ; Template ; Generative grammar ; Representation (politics) ; Artificial intelligence ; Chemistry ; Biochemical engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: I.B.C.M. Rocha | P. Kerfriden | F.P. van der Meer
Source: arXiv (Cornell University) (preprint, 2023-01-31)
Field: Materials Science — Materials Chemistry

Machine learning of evolving physics-based material models for multiscale solid mechanics In this work we present a hybrid physics-based and data-driven learning approach to construct surrogate models for concurrent multiscale simulations of complex material behavior. We start from robust but inflexible physics-based constitutive models and increase their expressivity by allowing a subset of their material parameters to change in time according to an evolution operator learned from data. This leads to a flexible hybrid model combining a data-driven encoder and a physics-based decoder. Apart from introducing physics-motivated bias to the resulting surrogate, the internal variables of the decoder act as a memory mechanism that allows path dependency to arise naturally. We demonstrate the capabilities of the approach by combining an FNN encoder with several plasticity decoders and training the model to reproduce the macroscopic behavior of fiber-reinforced composites. The hybrid models are able to provide reasonable predictions of unloading/reloading behavior while being trained exclusively on monotonic data. Furthermore, in contrast to traditional surrogates mapping strains to stresses, the specific architecture of the hybrid model allows for lossless dimensionality reduction and straightforward enforcement of frame invariance by using strain invariants as the feature space of the encoder. Dimensionality reduction ; Curse of dimensionality ; Artificial intelligence ; Computer science ; Encoder ; Theoretical computer science ; Algorithm ; Physics Machine Learning in Materials Science ; Composite Material Mechanics ; Model Reduction and Neural Networks
Authors: Ruard van Workum | João Pedro Malhado | Philipp Marquetand
Source: ChemRxiv (preprint, 2023-06-27)
Field: Materials Science — Materials Chemistry

CASNet: Learning Complete Active Space Orbitals using Message Passing Neural Networks Machine learning has demonstrated success in predicting molecular orbitals obtained from common single-configurational quantum chemistry methods, such as Hartree-Fock or Kohn-Sham Density Functional Theory. In this work, we present an extension of this supervised learning framework to multi-configurational quantum chemistry methods and compare different approaches for learning excited-state molecular wavefunctions. More specifically, by utilizing recent advances in message passing neural networks designed for learning molecular properties, we investigate the learning of molecular orbitals from State-Averaged Complete Active Space Self Consistent Field as a means of speeding up the corresponding calculations. We demonstrate the advantage of this general approach, referred to as \textit{CASNet}, over traditional orbital initialization techniques on different datasets of pentafulvene and evaluate its practical utility. Atomic orbital ; Initialization ; Complete active space ; Computer science ; Wave function ; Artificial neural network ; Space (punctuation) ; Molecular orbital ; Density functional theory ; Quantum chemistry ; Excited state ; Artificial intelligence ; Quantum mechanics ; Physics ; Molecule Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Various Chemistry Research Topics
Authors: Shun Takashige | M. Hanai | Toyotaro Suzumura | Limin Wang | Kenjiro Taura
Source: arXiv (Cornell University) (preprint, 2023-08-16)
Field: Materials Science — Materials Chemistry

Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction? The prediction of material properties plays a crucial role in the development and discovery of materials in diverse applications, such as batteries, semiconductors, catalysts, and pharmaceuticals. Recently, there has been a growing interest in employing data-driven approaches by using machine learning technologies, in combination with conventional theoretical calculations. In material science, the prediction of unobserved values, commonly referred to as extrapolation, is particularly critical for property prediction as it enables researchers to gain insight into materials beyond the limits of available data. However, even with the recent advancements in powerful machine learning models, accurate extrapolation is still widely recognized as a significantly challenging problem. On the other hand, self-supervised pretraining is a machine learning technique where a model is first trained on unlabeled data using relatively simple pretext tasks before being trained on labeled data for target tasks. As self-supervised pretraining can effectively utilize material data without observed property values, it has the potential to improve the model's extrapolation ability. In this paper, we clarify how such self-supervised pretraining can enhance extrapolation performance.We propose an experimental framework for the demonstration and empirically reveal that while models were unable to accurately extrapolate absolute property values, self-supervised pretraining enables them to learn relative tendencies of unobserved property values and improve extrapolation performance. Extrapolation ; Property (philosophy) ; Machine learning ; Artificial intelligence ; Computer science ; Supervised learning ; Mathematics ; Statistics ; Artificial neural network Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Chun-I Wang | Charlie Maier | Nicholas E. Jackson
Source: ChemRxiv (preprint, 2023-04-27)
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 non-linear vibrational modes. Leverage (statistics) ; Computer science ; Electronic structure ; Degrees of freedom (physics and chemistry) ; Theoretical computer science ; Statistical physics ; Artificial intelligence ; Algorithm ; Chemistry ; Physics ; Quantum mechanics ; Computational chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Surface Chemistry and Catalysis
Authors: Shaymaa A. Qenawy | Eid Aldawsri | Ahmed A. Hossam‐Eldin | Loai Nasrat | Hossam Kotb
Source: International Journal of Membrane Science and Technology (article, 2023-10-18)
Field: Materials Science — Materials Chemistry

Machine Learning Technique to Predict Flashover Voltage of Nanocomposite Materials Epoxy resin is frequently employed in medium- and high- voltage transmission insulation, because of its low dielectric losses and excellent temperature resistance. At low temperatures, epoxy resin has high chemical resistance. Epoxy resin insulated types have largely superseded the conventional, paper-insulated varieties in numerous cable sectors owing to several advantages. Numerous studies have been conducted to enhance the properties of epoxy resin. With the addition of silicon dioxide (SiO2) nanofiller, the electrical and physical properties of epoxy resin are intended to be improved in this research. Epoxy resin composites with SiO2 filler were created with lengths of 5, 10, 15, and 20 mm and concentrated at 7wt%. Subsequently the key findings of this research are outlined, highlighting the significance of this study's focus on polymer utilized in the highly competitive and technologically advanced power industry, which is used globally. Epoxy ; Materials science ; Composite material ; Filler (materials) ; Nanocomposite ; Composite epoxy material ; Dielectric ; Polymer ; Dielectric strength ; Polymer nanocomposite ; Optoelectronics High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Infrastructure Maintenance and Monitoring
Authors: Linlin Jia | Éric Brémond | Larissa Zaida | Benoît Gaüzère | Vincent Tognetti | Laurent Joubert
Source: ChemRxiv (preprint, 2023-11-09)
Field: Materials Science — Materials Chemistry

Predicting Redox Potentials by Graph-Based Machine Learning Methods The evaluation of oxidation and reduction potentials is a pivotal task in various chemical fields. However, their accurate prediction by theoretical computations, which is a complementary task and sometimes the only alternative to experimental measurement, may be often resource-intensive and time-consuming. This paper addresses this challenge through the application of machine learning techniques, with a particular focus on graph-based methods (such as graph edit distances, graph kernels, and graph neural networks) that are reviewed to enlighten their deep links with theoreti- cal chemistry. To this aim, we establish the ORedOx159 database, a comprehensive, homogeneous (with reference values stemming from density functional theory calculations), and reliable resource containing 318 one-electron reduction and oxidation reactions and featuring 159 large organic com- pounds. Subsequently, we provide an instructive overview of the good practice in machine learning and of commonly utilized machine learning models. We then assess their predictive performances on the ORedOx159 dataset through extensive analyses. Our simulations using descriptors that are computed in an almost instantaneous way result in a notable improvement in prediction accuracy, with mean absolute error (MAE) values equal to 5.6 kcal mol−1 for reduction and 7.2 kcal mol−1 for oxidation potentials, which paves a way toward efficient in silico design of new electrochemical systems. Computer science ; Graph ; Homogeneous ; Machine learning ; Artificial intelligence ; Computation ; Reduction (mathematics) ; Task (project management) ; Graph theory ; Algorithm ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks