jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziteng Liu | Liqiang Lin | Qingqing Jia | Zheng Cheng | Yanyan Jiang | Yanwen Guo | Jing Ma
Source: ChemRxiv (preprint, 2021-02-25)
Field: Materials Science — Materials Chemistry

Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science. However, predicting global electronic structure properties like frontier molecular orbital HOMO and LUMO energy levels and their HOMO-LUMO gaps from the small-sized molecule data to larger molecules remains a challenge. Here we develop a multi-level attention neural network, named DeepMoleNet, to enable chemical interpretable insights being fused into multi-task learning through (1) weighting contributions from various atoms and (2) taking the atom-centered symmetry functions (ACSFs) as the teacher descriptor. The efficient prediction of 12 properties including dipole moment, HOMO, and Gibbs free energy within chemical accuracy is achieved by using multiple benchmarks, both at the equilibrium and non-equilibrium geometries, including up to 110,000 records of data in QM9, 400,000 records in MD17 and 280,000 records in ANI-1ccx for random split evaluation. The good transferability for predicting larger molecules outside the training set is demonstrated in both equilibrium QM9 and Alchemy datasets at density functional theory (DFT) level. Additional tests on non-equilibrium molecular conformations from DFT-based MD17 dataset and ANI-1ccx dataset with coupled cluster accuracy as well as the public test sets of singlet fission molecules, biomolecules, long oligomers, and protein with up to 140 atoms show reasonable predictions for thermodynamics and electronic structure properties. The proposed multi-level attention neural network is applicable to high-throughput screening of numerous chemical species in both equilibrium and non-equilibrium molecular spaces to accelerate rational designs of drug-like molecules, material candidates, and chemical reactions. HOMO/LUMO ; Artificial neural network ; Chemistry ; Density functional theory ; Computer science ; Statistical physics ; Machine learning ; Coupled cluster ; Dipole ; Molecule ; Artificial intelligence ; Computational chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Juan Iván Gómez-Peralta | Nidia G. García-Peña | Xim Bokhimi
Source: Crystals (article, 2021-08-29)
Field: Materials Science — Materials Chemistry

Crystal-Site-Based Artificial Neural Networks for Material Classification In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the different atomic environments in a crystal compound can be constructed as input data for artificial neural networks (ANNs). In this article, we show the performance of a series of ANNs developed using crystal-site-based features. These ANNs were developed to classify compounds into halite, garnet, fluorite, hexagonal perovskite, ilmenite, layered perovskite, -o-tp- perovskite, perovskite, and spinel structures. Using crystal-site-based features, the ANNs were able to classify the crystal compounds with a 93.72% average precision. Furthermore, the ANNs were able to retrieve missing compounds with one of these archetypical structure types from a database. Finally, we showed that the developed ANNs were also suitable for a multitask learning paradigm, since the extracted information in the hidden layers linearly correlated with lattice parameters of the crystal structures. Crystal structure ; Artificial neural network ; Crystal (programming language) ; Artificial intelligence ; Materials science ; Fluorite ; Perovskite (structure) ; Machine learning ; Crystallography ; Computer science ; Chemistry ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Geochemistry and Geologic Mapping
Authors: Michael Rinderle | Alessio Gagliardi
Source:  (article, 2021-09-13)
Field: Materials Science — Materials Chemistry

Machine Learning &amp; multiscale simulations: toward fast screening of organic semiconductor materials Organic semiconductor devices promise cost-efficient processability at low temperatures, but the usually amorphous materials suffer from low charge carrier mobility. The search for high mobility organic semiconductor materials has thrived data science and Machine Learning approaches to screen the vast amount of possible organic materials. We present a multiscale simulation model based on machine learned transfer integrals to compute the charge carrier mobility in organic thin films. Organic semiconductor ; Electron mobility ; Semiconductor ; Charge (physics) ; Materials science ; Charge carrier ; Computer science ; Amorphous solid ; Optoelectronics ; Nanotechnology ; Artificial intelligence ; Electronic engineering ; Engineering ; Physics ; Chemistry Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Chang Liu | Erina Fujita | Yukari Katsura | Yuki Inada | Asuka Ishikawa | Ryuji Tamura | Kaoru Kimura | Ryo Yoshida
Source: Research Square (preprint, 2021-02-17)
Field: Chemistry — Physical and Theoretical Chemistry

Machine learning to predict quasicrystals from chemical compositions Quasicrystal ; Materials science ; Physics ; Condensed matter physics Crystallography and molecular interactions ; Mineralogy and Gemology Studies ; Quasicrystal Structures and Properties
Authors: Arun Mannodi‐Kanakkithodi | Xiaofeng Xiang | Laura M. Jacoby | Robert Biegaj | Scott T. Dunham | Daniel R. Gamelin | Maria K. Y. Chan
Source: Research Square (preprint, 2021-07-26)
Field: Materials Science — Materials Chemistry

Universal Machine Learning Framework for Defect Predictions in Zinc Blende Semiconductors Abstract Point defects or impurities are either naturally present in semiconductors or may be intentionally introduced to tune their electronic and optical properties. The nature of impurity energy levels can strongly influence the performance of a semiconductor in applications ranging from solar cells to photodiodes to infrared sensors to qubits for quantum computing. In this work, we develop a framework powered by machine learning (ML) and high-throughput density functional theory (DFT) computations for the prediction and screening of functional impurities in group IV, III-V, and II-VI zinc blende semiconductors. Elements spanning the length and breadth of the periodic table are considered as impurity atoms at the cation, anion, or interstitial sites in supercells of 34 candidate semiconductors, leading to a chemical space of 12,000 points, 10% of which are used to generate a DFT dataset of charge dependent defect formation energies. Descriptors based on tabulated elemental properties, defect coordination environment, and relevant semiconductor properties are used to train ML regression models for the DFT computed properties, resulting in statistical predictions of the neutral state formation energies and charge transition levels of all possible impurities in the given set of compounds. Kernel ridge regression, Gaussian process regression, and neural networks, with appropriate feature selection and hyperparameter optimization, are seen to yield similar predictive performances and meaningful uncertainty estimates. We apply the ML framework to screen all impurities with lower formation energy than dominant native defects in all group IV, III-V, and II-VI zinc blende semiconductors. An online tool resulting from this work for predicting and visualizing defect properties in semiconductors is made available on github. Semiconductor ; Zinc ; Materials science ; Condensed matter physics ; Computer science ; Artificial intelligence ; Physics ; Optoelectronics ; Metallurgy Machine Learning in Materials Science ; Semiconductor materials and devices ; Semiconductor materials and interfaces
Authors: Hitarth Choubisa | Petar Todorović́ | João M. Pina | Darshan H. Parmar | Ziliang Li | Oleksandr Voznyy | Isaac Tamblyn | Edward H. Sargent
Source: arXiv (Cornell University) (preprint, 2021-01-12)
Field: Materials Science — Materials Chemistry

Interpretable discovery of new semiconductors with machine learning Machine learning models of materials$^{1-5}$ accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated results at one hundred thousandths of the cost of DFT$^{6}$. To provide guidance in experimental materials synthesis, these need to be coupled with an accurate yet effective search algorithm and training data consistent with experimental observations. Here we report an evolutionary algorithm powered search which uses machine-learned surrogate models trained on high-throughput hybrid functional DFT data benchmarked against experimental bandgaps: Deep Adaptive Regressive Weighted Intelligent Network (DARWIN). The strategy enables efficient search over the materials space of ~10$^8$ ternaries and 10$^{11}$ quaternaries$^{7}$ for candidates with target properties. It provides interpretable design rules, such as our finding that the difference in the electronegativity between the halide and B-site cation being a strong predictor of ternary structural stability. As an example, when we seek UV emission, DARWIN predicts K$_2$CuX$_3$ (X = Cl, Br) as a promising materials family, based on its electronegativity difference. We synthesized and found these materials to be stable, direct bandgap UV emitters. The approach also allows knowledge distillation for use by humans. Artificial intelligence ; Computer science ; Machine learning ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Materials Characterization Techniques
Authors: Omid Khalaj | Moslem Ghobadi | Ehsan Saebnoori | Alireza Zarezadeh | Mohammadreza Shishesaz | Bohuslav Mašek | Ctibor Štádler | Jiřı́ Svoboda
Source: Materials (article, 2021-11-08)
Field: Materials Science — Metals and Alloys

Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets. Adaptive neuro fuzzy inference system ; Toughness ; Materials science ; Artificial neural network ; Ultimate tensile strength ; Alloy ; Mean squared error ; Fuzzy logic ; Composite material ; Machine learning ; Computer science ; Mathematics ; Artificial intelligence ; Statistics ; Fuzzy control system Hydrogen embrittlement and corrosion behaviors in metals ; Catalytic Processes in Materials Science ; Nuclear Materials and Properties
Authors: Santiago Vargas | Matthew R. Hennefarth | Zhihao Liu | Anastassia N. Alexandrova
Source: ChemRxiv (preprint, 2021-06-03)
Field: Materials Science — Materials Chemistry

Machine Learning to Predict Diels-Alder Reaction Barriers from the Reactant State Electron Density &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactions are so seminal in chemistry, that countless variants, with or without catalysts, have been studied and their barriers have been computed or measured experimentally. This wealth of data represents a perfect opportunity to leverage machine learning models, which could quickly predict barriers without explicit calculations or measurement. Here, we show that the topological descriptors of the quantum mechanical charge density in the reactant state constitute a set that is both rigorous and continuous, and can be used effectively for prediction of reaction barrier energies to a high degree of accuracy. We demonstrate this on the Diels-Alder reaction, highly important in biology and medicinal chemistry, and as such, studied extensively. This reaction exhibits a range of barriers as large as 270 kJ/mol. While we trained our single-objective supervised (labeled) regression algorithms on simpler Diels-Alder reactions in solution, they predict reaction barriers also in significantly more complicated contexts, such a Diels-Alder reaction catalyzed by an artificial enzyme and its evolved variants, in agreement with experimental changes in &lt;i&gt;k&lt;sub&gt;cat&lt;/sub&gt;&lt;/i&gt;. We expect this tool to apply broadly to a variety of reactions in solution or in the presence of a catalyst, for screening and circumventing heavily involved computations or experiments. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Leverage (statistics) ; Catalysis ; Reactivity (psychology) ; Chemistry ; Computational chemistry ; Computer science ; Machine learning ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Natural Products and Biosynthesis
Authors: Esben Jannik Bjerrum | Tobias Rastemo | Ross Irwin | Christos Kannas | Samuel Genheden
Source:  (preprint, 2021-06-29)
Field: Materials Science — Materials Chemistry

PySMILESUtils – Enabling deep learning with the SMILES chemical language Recent years have seen a large interest in using the Simplified Molecular Input Line Entry System (SMILES) chemical language as input for deep learning architectures solving chemical tasks. Many successful applications have been demonstrated within de novo molecular design, quantitative structure-activity relationship modelling, forward reaction prediction and single-step retrosynthetic planning as examples. PySMILESUtils aims to enable these tasks by providing readyto- use and adaptable Python classes for tokenization, augmentation, dataset, and dataloader creation. Classes for handling datasets larger than memory and speeding up training by minimizing padding are also provided. The framework subclasses PyTorch dataset and dataloaders but should be adaptable for other deep learning frameworks. The project is open-sourced with a permissive license and made available at GitHub: https://github.com/MolecularAI/pysmilesutils Computer science ; Python (programming language) ; Artificial intelligence ; Deep learning ; MIT License ; Machine learning ; Programming language ; Natural language processing ; Software engineering ; Software Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Ryan Henderson | Djork-Arné Clevert | Floriane Montanari
Source: arXiv (Cornell University) (preprint, 2021-05-11)
Field: Materials Science — Materials Chemistry

Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity Rationalizing which parts of a molecule drive the predictions of a molecular graph convolutional neural network (GCNN) can be difficult. To help, we propose two simple regularization techniques to apply during the training of GCNNs: Batch Representation Orthonormalization (BRO) and Gini regularization. BRO, inspired by molecular orbital theory, encourages graph convolution operations to generate orthonormal node embeddings. Gini regularization is applied to the weights of the output layer and constrains the number of dimensions the model can use to make predictions. We show that Gini and BRO regularization can improve the accuracy of state-of-the-art GCNN attribution methods on artificial benchmark datasets. In a real-world setting, we demonstrate that medicinal chemists significantly prefer explanations extracted from regularized models. While we only study these regularizers in the context of GCNNs, both can be applied to other types of neural networks Regularization (linguistics) ; Orthonormal basis ; Computer science ; Graph ; Convolutional neural network ; Artificial intelligence ; Benchmarking ; Artificial neural network ; Benchmark (surveying) ; Machine learning ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ksenia R. Briling | Alberto Fabrizio | Clémence Corminbœuf
Source: The Journal of Chemical Physics (article, 2021-07-09)
Field: Materials Science — Materials Chemistry

Impact of quantum-chemical metrics on the machine learning prediction of electron density Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain a proper balance between the black-box nature of ML frameworks and the physics of the target properties. One of the most appealing quantum-chemical properties for regression models is the electron density, and some of us recently proposed a transferable and scalable model based on the decomposition of the density onto an atom-centered basis set. The decomposition, as well as the training of the model, is at its core a minimization of some loss function, which can be arbitrarily chosen and may lead to results of different quality. Well-studied in the context of density fitting (DF), the impact of the metric on the performance of ML models has not been analyzed yet. In this work, we compare predictions obtained using the overlap and the Coulomb-repulsion metrics for both decomposition and training. As expected, the Coulomb metric used as both the DF and ML loss functions leads to the best results for the electrostatic potential and dipole moments. The origin of this difference lies in the fact that the model is not constrained to predict densities that integrate to the exact number of electrons N. Since an a posteriori correction for the number of electrons decreases the errors, we proposed a modification of the model, where N is included directly into the kernel function, which allowed lowering of the errors on the test and out-of-sample sets. Quantum chemical ; Electron density ; Electron ; Quantum ; Machine learning ; Artificial intelligence ; Computer science ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Fan Wang | Linxiang Wang
Source: Journal of Intelligent Material Systems and Structures (article, 2021-02-21)
Field: Materials Science — Materials Chemistry

A phenomenological constitutive model for one-dimensional shape memory alloys based on artificial neural network Shape Memory Alloys (SMA) have become a material with great application prospects because of their unique characteristics and superior properties. A phenomenological constitutive model for SMA is constructed in the current paper. The proposed constitutive model is based on the phenomena observed in the experiment, and Artificial Neural Networks (ANN) are used to simulate part of the characteristics of SMA. The parameter identification method is also proposed, where Back-Propagation (BP) algorithm and the nonlinear optimisation algorithm are used at the same time. The numerical experiment has been carried out, which can well capture the constitutive relationship curve obtained from uniaxial tension and compression experiments of SMA, thus the model can be verified. The model can also describe the phase transformation characteristics of SMA well. SMA* ; Constitutive equation ; Shape-memory alloy ; Artificial neural network ; Phenomenological model ; Nonlinear system ; Compression (physics) ; Computer science ; Materials science ; Algorithm ; Artificial intelligence ; Structural engineering ; Mathematics ; Engineering ; Finite element method ; Physics ; Composite material Shape Memory Alloy Transformations
Authors: Mohamad Amin Bin Hamid | Hoe Guan Beh | Afeez Oluwatobi Yusuff | Xiao Yan Chew | Saba Ayub
Source: Applied Sciences (article, 2021-08-10)
Field: Materials Science — Materials Chemistry

Neutron-Induced Nuclear Cross-Sections Study for Plasma Facing Materials via Machine Learning: Molybdenum Isotopes In this work, we apply a machine learning algorithm to the regression analysis of the nuclear cross-section of neutron-induced nuclear reactions of molybdenum isotopes, 92Mo at incident neutron energy around 14 MeV. The machine learning algorithms used in this work are the Random Forest (RF), Gaussian Process Regression (GPR), and Support Vector Machine (SVM). The performance of each algorithm is determined and compared by evaluating the root mean square error (RMSE) and the correlation coefficient (R2). We demonstrate that machine learning can produce a better regression curve of the nuclear cross-section for the neutron-induced nuclear reaction of 92Mo isotopes compared to the simulation results using EMPIRE 3.2 and TALYS 1.9 from the previous literature. From our study, GPR is found to be better compared to RF and SVM algorithms, with R2=1 and RMSE =0.33557. We also employed the crude estimation of property (CEP) as inputs, which consist of simulation nuclear cross-section from TALYS 1.9 and EMPIRE 3.2 nuclear code alongside the experimental data obtained from EXFOR (1 April 2021). Although the Experimental only (EXP) dataset generates a more accurate cross-section, the use of CEP-only data is found to generate an accurate enough regression curve which indicates a potential use in training machine learning models for the nuclear reaction that is unavailable in EXFOR. Neutron ; Support vector machine ; Nuclear data ; Mean squared error ; Nuclear physics ; Algorithm ; Nuclear reaction ; Artificial intelligence ; Physics ; Computer science ; Machine learning ; Mathematics ; Statistics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications