jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chuannan Li | Han‐Pu Liang | Yifeng Duan | Zijing Lin
Source: arXiv (Cornell University) (preprint, 2023-02-07)
Field: Materials Science — Materials Chemistry

Machine-Learning Accelerated Annealing with Fitting-Search Style for Multi-alloy Structure Predictions Structural prediction for the discovery of novel materials is a long sought after goal of computational physics and materials sciences. The success is rather limited for methods such as the simulated annealing method (SA) that require expensive density functional theory (DFT) calculations and follow unintelligent search paths. Here a machine-learning based crystal combinatorial optimization program (CCOP) with a fitting-search style is proposed to drastically improve the efficiency of structural search in SA. CCOP uses a graph neural network energy prediction model to reduce the DFT cost and a deep reinforcement learning algorithm to direct the search path. Tests on six multi-alloys show the energy prediction model is capable of extracting the bonding characteristics of the complex alloys to achieve interpretability. It also achieves high accuracy with a tiny training set (an increment of 30 samples per iteration) by active learning in less than 5 iterations. Comparison with a few conventional methods shows that CCOP finds the lowest-energy structures with the smallest number of search steps. CCOP cuts the computing cost of SA by two orders of magnitude, while providing better search results than SA. CCOP is promising for serving as a broadly applicable tool for the efficient crystal structure predictions. Interpretability ; Simulated annealing ; Computer science ; Artificial neural network ; Artificial intelligence ; Annealing (glass) ; Machine learning ; Algorithm ; Shortest path problem ; Graph ; Materials science ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yongle Li | Feng Xu | Long Hou | Luchao Sun | Haijun Su | Xi Li | Wei Ren
Source: arXiv (Cornell University) (preprint, 2023-03-01)
Field: Materials Science — Materials Chemistry

Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion The force field describing the calculated interaction between atoms or molecules is the key to the accuracy of many molecular dynamics (MD) simulation results. Compared with traditional or semi-empirical force fields, machine learning force fields have the advantages of faster speed and higher precision. We have employed the method of atomic cluster expansion (ACE) combined with first-principles density functional theory (DFT) calculations for machine learning, and successfully obtained the force field of the binary Fe-Co alloy. Molecular dynamics simulations of Fe-Co alloy carried out using this ACE force field predicted the correct phase transition range of Fe-Co alloy. Molecular dynamics ; Force field (fiction) ; Cluster expansion ; Alloy ; Cluster (spacecraft) ; Field (mathematics) ; Materials science ; Transformation (genetics) ; Density functional theory ; Statistical physics ; Computational chemistry ; Computer science ; Physics ; Thermodynamics ; Chemistry ; Metallurgy ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: James Damewood | Jessica Karaguesian | Jaclyn R. Lunger | Aik Rui Tan | Mingrou Xie | Jiayu Peng | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2023-01-20)
Field: Materials Science — Materials Chemistry

Representations of Materials for Machine Learning High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by a machine learning model. Datasets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and property of interests. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs of machine learning models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus, require further investigation. Computer science ; Machine learning ; Representation (politics) ; Scope (computer science) ; Fidelity ; Context (archaeology) ; Artificial intelligence ; Property (philosophy) ; Ranging ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: А. Н. Залога | Vladimir Stanovov | Oksana E. Bezrukovа | P. S. Dubinin | Igor S. Yakimov
Source: AIP conference proceedings (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Possibilities for neural network powder diffraction analysis of the crystal structure of chemical compounds Some possibilities of using convolutional artificial neural networks (ANNs) for powder diffraction structural analysis of crystalline substances are investigated. First, ANNs were used to classify crystal systems and space groups of symmetry based on the full-profile diffractograms calculated from the crystal structures of the ICSD 2017 database. The ICSD database contains 192004 structures, of which 80% were used for deep network learning, and 20% for independent testing of recognition accuracy. The accuracy of classification by a network of crystalline systems was 87.9%, and space groups was 77.2%. Second, ANN was applied for a similar classification of structural models generated by a stochastic evolutionary algorithm in the search for triclinic crystal structures of test compounds K4SnO4 and Rb4SnO4 from their full-profile diffraction patterns. The classification criterion was the hit of one or more atoms in their correct crystallographic positions in the structure of the substance. Independent deep learning of the network was performed on 100 thousand structural models of the triclinic structure of K4SnO4, generated in several runs of the evolutionary algorithm. The classification accuracy of the structural models of these substances was 72.2% and 76.9%, respectively. Triclinic crystal system ; Diffraction ; Crystal structure ; Artificial neural network ; Artificial intelligence ; Convolutional neural network ; Powder diffraction ; Computer science ; Pattern recognition (psychology) ; Crystallography ; Materials science ; Deep learning ; Crystal (programming language) ; Algorithm ; Chemistry ; Physics ; Optics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Yandong Guo | Yingying Song | Junwu Chen | Menglei Yuan | Kun Dong
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning Assisted High Throughput Screening of Ionic Liquid Electrolytes for Nrr and Co2rr Throughput ; Ionic liquid ; Ionic bonding ; Computer science ; Electrolyte ; High-throughput screening ; Chemistry ; Telecommunications ; Ion ; Electrode ; Catalysis ; Biochemistry ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science
Authors: Abbigayle E. Cuomo | Sebastian Ibarraran | Sanil Sreekumar | Haote Li | Jungmin Eun | Jan Paul Menzel | Pengpeng Zhang | Frédéric G. Buono | Jeff Song | Robert H. Crabtree | Víctor S. Batista | Timothy R. Newhouse
Source: ChemRxiv (preprint, 2023-01-30)
Field: Materials Science — Materials Chemistry

Feed Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction Density functional theory (DFT) has become a popular method to model transition state (TS) energies to predict enantioselectivity, but the associated errors present challenges. Machine learning has emerged as a powerful tool to model enantioselectivity but generally requires large datasets for training. Herein, we describe the development of a feed forward neural network for predicting enantioselectivity of the Negishi cross-coupling reaction with Boehringer Ingelheim (BI)-type phosphines. The selectivity predicted from DFT TS energies is upgraded through the neural network based on input features including geometries, electron population, and dispersive interactions. This new approach to modeling enantioselectivity is compared to conventional approaches, including exclusive use of DFT energies, and data science approaches using features from ligands or ground states with simple neural network architectures. Negishi coupling ; Artificial neural network ; Density functional theory ; Computer science ; Population ; Coupling (piping) ; Computational chemistry ; Chemistry ; Topology (electrical circuits) ; Machine learning ; Materials science ; Mathematics ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Quanzhou Luo | Yifan Shen | Meng‐Bo Luo | NFTGo, Hangzhou 310013, China
Source: Acta Physica Sinica (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Computer simulation and machine learning of polymer collapse and critical adsorption phase transitions Collapse and critical adsorption of polymers are two crucial phase transitions in polymer science, both are accompanied by significant changes in polymer conformation. In this paper, Langevin dynamics and dynamic Monte Carlo methods are used to simulate the collapse and critical adsorption of polymer, respectively, and corresponding phase transition temperatures are estimated. Meanwhile, a large number of polymer conformations at different temperatures are obtained. In the machine learning method, a large number of extended random coil and collapsed spherical, desorption and adsorption conformations are used to train the neural network, so that the neural network can learn the characteristics of different states of the polymer, and it can quickly and accurately analyze the polymer conformations at different temperatures and obtain the corresponding collapse phase transition temperature and critical adsorption temperature. The results demonstrate that machine learning can correctly calculate the phase transition temperature of polymer system, which provides new ideas and methods for machine learning technology in the study of polymer phase transitions. Polymer ; Phase transition ; Adsorption ; Materials science ; Polymer adsorption ; Desorption ; Chemical physics ; Artificial neural network ; Polymer network ; Monte Carlo method ; Phase (matter) ; Thermodynamics ; Statistical physics ; Langevin dynamics ; Computer science ; Physical chemistry ; Physics ; Artificial intelligence ; Chemistry ; Mathematics ; Composite material ; Organic chemistry Machine Learning in Materials Science
Authors: Takuya Taniguchi
Source: ChemRxiv (preprint, 2023-10-25)
Field: Materials Science — Materials Chemistry

Exploration of elastic moduli of molecular crystals via database screening by pretrained neural network potential This paper presents the database screening of elastic moduli of molecular crystals using a pretrained neural network potential. The calculated elastic moduli were sufficiently consistent with experiment values, better than Hartree-Fock calculations. The database screening suggested crystals with large and small moduli. Moduli ; Elastic modulus ; Artificial neural network ; Database ; Materials science ; Physics ; Computer science ; Artificial intelligence ; Composite material ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Shuai Yuan | Ken‐ichi Nomura
Source:  (article, 2023-04-20)
Field: Materials Science — Materials Chemistry

Using machine learning to predict the band gap of semiconductors To accelerate the speed of finding new materials and to predict the attributes of materials, Machine Learning has emerged as a very useful tool in recent times. Predicting the band gap of a new material plays a significant role in evaluating the unknown material. To predict the band gap, several regression methods of supervised learning in machine learning were applied, such as linear regression, k-nearest neighbors-regression, random forest regression, support vector machine and decision tree regression. Besides, comparative results illustrated that the predictions of the random forest and the decision tree methods were very good. This enables the possibility to understand thousands of unknown band gaps of non-metals, especially in case of semiconductors. Decision tree ; Random forest ; Machine learning ; Support vector machine ; Artificial intelligence ; Regression ; Computer science ; Regression analysis ; Tree (set theory) ; Linear regression ; Statistics ; Mathematics Machine Learning in Materials Science
Authors: Vivin Vinod | Ulrich Kleinekathöfer | Peter Zaspel
Source: arXiv (Cornell University) (preprint, 2023-12-09)
Field: Materials Science — Materials Chemistry

Optimized Multifidelity Machine Learning for Quantum Chemistry Machine learning (ML) provides access to fast and accurate quantum chemistry (QC) calculations for various properties of interest such as excitation energies. It is often the case that high accuracy in prediction using an ML model, demands a large and costly training set. Various solutions and procedures have been presented to reduce this cost. These include methods such as $Δ$-ML, hierarchical-ML, and multifidelity machine learning (MFML). MFML combines various $Δ$-ML like sub-models for various fidelities according to a fixed scheme derived from the sparse grid combination technique. In this work we implement an optimization procedure to combine multifidelity models in a flexible scheme resulting in optimized MFML (o-MFML) that provides superior prediction capabilities. This hyper-parameter optimization is carried out on a holdout validation set of the property of interest. This work benchmarks the o-MFML method in predicting the atomization energies on the QM7b dataset, and again in the prediction of excitation energies for three molecules of growing size. The results indicate that o-MFML is a strong methodological improvement over MFML and provides lower error of prediction. Even in cases of poor data distributions and lack of clear hierarchies among the fidelities, which were previously identified as issues for multifidelity methods, the o-MFML provides advantage to the prediction of quantum chemical properties. Computer science ; Set (abstract data type) ; Excitation ; Quantum chemical ; Quantum ; Grid ; Hyperparameter optimization ; Work (physics) ; Algorithm ; Scheme (mathematics) ; Quantum chemistry ; Artificial intelligence ; Machine learning ; Molecule ; Mathematics ; Physics ; Quantum mechanics ; Support vector machine Machine Learning in Materials Science ; Machine Learning and Data Classification
Authors: Anatoly I. Frenkel
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2023-01-23)
Field: Chemistry — Analytical Chemistry

System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra A method of supervised machine learning-based spectrum analysis information, using a neural network trained with spectrum information, to identify a specified feature of a given material, a system for supervised machine learning-based spectrum analysis, and a method of training a neural network to analyze spectrum data. The method of supervised machine learning-base spectrum analysis comprises inputting into the neural network spectrum data obtained from a sample of the given material; and the neural network processing the spectrum data, in accordance with the training of the neural network, and outputting one or more values for the specified feature of the sample of the material. In an embodiment, the training set of data includes x-ray absorption spectroscopy data for the given material. In an embodiment, the training set of data includes electron energy loss spectra (EELS) data. Characterization (materials science) ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Materials science ; Nanotechnology Spectroscopy and Chemometric Analyses
Authors: Weiqiang Fu | Yujie Mo | Xiao Yi | Chang Liu | Feng Zhou | Yan Wang | Jielong Zhou | Yingsheng Zhang
Source: ChemRxiv (preprint, 2023-09-20)
Field: Materials Science — Materials Chemistry

Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential Exclusively prioritizing the precision of energy prediction frequently proves inadequate in satisfying multifaceted requirements. A heightened focus is warranted on assessing the rationality of potential energy curves predicted by machine learning-based force fields (MLFF), alongside evaluating the pragmatic utility of these MLFF. This study introduces SWANI, an optimized Neural Network Potential (NNP) stemming from the ANI framework. Through the incorporation of supplementary physical constraints, SWANI aligns more cohesively with chemical expectations, yielding rational potential energy profiles. It also exhibits superior predictive precision compared to the ANI model. Additionally, a comprehensive comparison is conducted between SWANI and a prominent Graph Neural Network (GNN)-based model. The findings indicate that SWANI outperforms the latter, particularly for molecules exceeding the dimensions of the training set. This outcome underscores SWANI's exceptional capacity for generalization and its proficiency in handling larger molecular systems. Generalization ; Artificial neural network ; Rationality ; Artificial intelligence ; Computer science ; Machine learning ; Set (abstract data type) ; Energy (signal processing) ; Potential energy ; Graph ; Training set ; Focus (optics) ; Theoretical computer science ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tom McDonald | Calvin Tsay | Artur M. Schweidtmann | Neil Yorke‐Smith
Source: arXiv (Cornell University) (preprint, 2023-12-02)
Field: Materials Science — Materials Chemistry

Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems. However, previous works are mostly limited to MLPs. Graph neural networks (GNNs) can learn from non-euclidean data structures such as molecular structures efficiently and are thus highly relevant to computer-aided molecular design (CAMD). We propose a bilinear formulation for ReLU Graph Convolutional Neural Networks and a MILP formulation for ReLU GraphSAGE models. These formulations enable solving optimisation problems with trained GNNs embedded to global optimality. We apply our optimization approach to an illustrative CAMD case study where the formulations of the trained GNNs are used to design molecules with optimal boiling points. Computer science ; Integer programming ; Graph ; Artificial neural network ; Mathematical optimization ; Bilinear interpolation ; Linear programming ; Integer (computer science) ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Mathematics ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Shun Muroga | Yasuaki Miki | Kenji Hata
Source: arXiv (Cornell University) (preprint, 2023-03-29)
Field: Materials Science — Materials Chemistry

A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials We present a multimodal deep learning (MDL) framework for predicting physical properties of a 10-dimensional acrylic polymer composite material by merging physical attributes and chemical data. Our MDL model comprises four modules, including three generative deep learning models for material structure characterization and a fourth model for property prediction. Our approach handles an 18-dimensional complexity, with 10 compositional inputs and 8 property outputs, successfully predicting 913,680 property data points across 114,210 composition conditions. This level of complexity is unprecedented in computational materials science, particularly for materials with undefined structures. We propose a framework to analyze the high-dimensional information space for inverse material design, demonstrating flexibility and adaptability to various materials and scales, provided sufficient data is available. This study advances future research on different materials and the development of more sophisticated models, drawing us closer to the ultimate goal of predicting all properties of all materials. Flexibility (engineering) ; Property (philosophy) ; Computer science ; Adaptability ; Generative grammar ; Artificial intelligence ; Machine learning ; Deep learning ; Space (punctuation) ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Composite Material Mechanics
Authors: Teddy Koker | Keegan Quigley | Eric Taw | Kevin Tibbetts | Lin Li
Source: arXiv (Cornell University) (preprint, 2023-12-08)
Field: Materials Science — Materials Chemistry

Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-standing challenge. We introduce ChargE3Net, an E(3)-equivariant graph neural network for predicting electron density in atomic systems. ChargE3Net enables the learning of higher-order equivariant feature to achieve high predictive accuracy and model expressivity. We show that ChargE3Net exceeds the performance of prior work on diverse sets of molecules and materials. When trained on the massive dataset of over 100K materials in the Materials Project database, our model is able to capture the complexity and variability in the data, leading to a significant 26.7% reduction in self-consistent iterations when used to initialize DFT calculations on unseen materials. Furthermore, we show that non-self-consistent DFT calculations using our predicted charge densities yield near-DFT performance on electronic and thermodynamic property prediction at a fraction of the computational cost. Further analysis attributes the greater predictive accuracy to improved modeling of systems with high angular variations. These results illuminate a pathway towards a machine learning-accelerated ab initio calculations for materials discovery. Initialization ; Density functional theory ; Statistical physics ; Electron ; Artificial neural network ; Charge density ; Electron density ; Equivariant map ; Computer science ; Point particle ; Tensor (intrinsic definition) ; Quantum ; Work (physics) ; Physics ; Mathematics ; Quantum mechanics ; Artificial intelligence ; Pure mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions