jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leonardo C. Pacheco‐Londoño | Eric Warren | Nataly J. Galán‐Freyle | Reynaldo Villarreal-González | Joaquin Aparicio | María L. Ospina-Castro | Wei‐Chuan Shih | Samuel P. Hernández‐Rivera
Source: Preprints.org (preprint, 2020-05-31)
Field: Chemistry — Analytical Chemistry

Mid-infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (2,4-DNT) with concentrations from 0% to 20% w/w were investigated using three types of soils: bentonite, synthetic soil, and natural soil. A Partial least squares regression model was generated for predicting 2,4-DNT concentrations. To increase its selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as PETN, RDX, and TNT and non-explosives such as benzoic acid and ibuprofen. For detection, mixes of different explosives in soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with those of soils recorded in a database to identify the most similar soils based on QCL spectroscopy. Next, a Classical Least Squares preprocessing (Pre-CLS) was applied to soils spectra selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was then used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A Random Forest model worked best with 0.997 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils. Soil water ; Partial least squares regression ; Explosive material ; Chemometrics ; Multivariate statistics ; Soil test ; Linear discriminant analysis ; Environmental science ; Artificial intelligence ; Chemistry ; Analytical Chemistry (journal) ; Soil science ; Computer science ; Machine learning ; Environmental chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Spectroscopy and Laser Applications
Authors: Andrew Rosen | Shaelyn Iyer | Debmalya Ray | Zhenpeng Yao | Alán Aspuru‐Guzik | Laura Gagliardi | Justin M. Notestein | Randall Q. Snurr
Source: ChemRxiv (preprint, 2020-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning the Quantum-Chemical Properties of Metal–Organic Frameworks for Accelerated Materials Discovery with a New Electronic Structure Database Metal–organic frameworks (MOFs) are a widely investigated class of crystalline solids with tunable structures that make it possible to impart specific chemical functionality tailored for a given application. However, the enormous number of possible MOFs that can be synthesized makes it difficult to determine which materials would be the most promising candidates, especially for applications governed by electronic structure properties that are often computationally demanding to simulate and time-consuming to probe experimentally. Here, we have developed the first publicly available quantum-chemical database for MOFs (the “QMOF database”), which consists of properties derived from density functional theory (DFT) for over 14,000 experimentally synthesized MOFs. Throughout this study, we demonstrate how this new database can be used to identify MOFs with targeted electronic structure properties. As a proof-of-concept, we use the QMOF database to evaluate the performance of several machine learning models for the prediction of DFT-computed band gaps and find that crystal graph convolutional neural networks are capable of achieving superior predictive performance, making it possible to circumvent computationally expensive quantum-chemical calculations. We also show how unsupervised learning methods can aid the discovery of otherwise subtle structure–property relationships using the computational findings in this work. We conclude by highlighting several MOFs with low band gaps, a challenging task given the electronically insulating nature of most MOF structures. The data and predictive models generated in this work, as well as the database of MOF structures, should be highly useful to other researchers interested in the predictive design and discovery of MOFs for the many applications dictated by quantum-chemical phenomena. Computer science ; Quantum chemical ; Property (philosophy) ; Density functional theory ; Metal-organic framework ; Database ; Machine learning ; Artificial intelligence ; Molecule ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Yu. V. Klunnikova | Maxim Anikeev | A. V. Filimonov | Ravi Kumar
Source: Journal of Electronic Science and Technology (article, 2020-03-01)
Field: Materials Science — Materials Chemistry

Machine learning application for prediction of sapphire crystals defects We investigate the impact of different numbers of positive and negative examples on machine learning for sapphire crystals defects prediction. We obtain the models of crystal growth parameters influence on the sapphire crystal growth. For example, these models allow predicting the defects that occur due to local overcooling of crucible walls in the thermal node leading to the accelerated crystal growth. We also develop the prediction models for obtained crystal weight, blocks, cracks, bubbles formation, and total defect characteristics. The models were trained on all data sets and later tested for generalization on testing sets, which did not overlap the training set. During training and testing, we find the recall, precision of prediction and analyze the correlation among the features. The results have shown that the precision of neural network method for predicting defects formed by local overcooling of the crucible reached 0.94. Crucible (geodemography) ; Sapphire ; Crystal (programming language) ; Generalization ; Materials science ; Artificial neural network ; Node (physics) ; Artificial intelligence ; Set (abstract data type) ; Computer science ; Algorithm ; Biological system ; Mathematics ; Engineering ; Structural engineering ; Optics ; Chemistry ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics
Authors: R.I. Zubatyuk | Justin S. Smith | Benjamin Nebgen | Sergei Tretiak | Olexandr Isayev
Source: ChemRxiv (preprint, 2020-07-28)
Field: Materials Science — Materials Chemistry

Teaching a Neural Network to Attach and Detach Electrons from Molecules Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry. In particular, interatomic potentials derived with Machine Learning algorithms such as Deep Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. The applicability domain of DNN potentials is usually limited by the type of training data. As such, transferable models are aimed to be extensible in the description of chemical and conformational diversity of organic molecules. However, most DNN potentials, such as the AIMNet model we proposed previously, were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we extend our AIMNet framework toward open-shell anions and cations. This model explores a new dimension of transferability by adding the charge-spin space. The resulting AIMNet model is capable of reproducing reference QM energies for cations, neutrals and anions with errors of 4.1, 2.1, 2.8 kcal/mol, respectively, compared to the reference QM simulations. The spin-charges have errors 0.01-0.06 electrons for small organic molecules containing nine chemical elements {H, C, N, O, F, Si, P, S and Cl}. Thus the proposed AIMNet model allows researchers to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regionselectivity in electrophilic aromatic substitution reactions. Electronegativity ; Density functional theory ; Open shell ; Chemical space ; Electron affinity (data page) ; Ion ; Molecule ; Artificial neural network ; Ionization ; Electron ; Chemistry ; Physics ; Computational chemistry ; Statistical physics ; Computer science ; Chemical physics ; Atomic physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Advanced Chemical Physics Studies
Authors: Manav Ramprasad | Chiho Kim
Source: Journal of Emerging Investigators (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures The success of the Materials Genome Initiative has led to opportunities for data-driven approaches for materials discovery. The recent development of Polymer Genome (PG), which is a machine learning (ML) based data-driven informatics platform for polymer property prediction, has significantly increased the efficiency of polymer design. Nevertheless, continuous expansion of the ‘training data’ is necessary to improve the robustness, versatility, and accuracy of the ML predictions. Accurate prediction of polymer properties, such as glass transition temperature (Tg), is advantageous for the design of polymers, particularly for high temperature applications. We hypothesized that by adding more data with increased chemical diversity to the dataset, the predictive capabilities of the PG model would improve. In order to test the performance and transferability of the predictive model for Tg (previously trained on a dataset of 450 polymers), we have carefully collected additional experimental Tg data for 871 polymers from multiple data sources. The Tg values predicted by the present PG models for the polymers in the newly collected dataset were compared directly with the experimental Tg to estimate the accuracy of the present model. Using the full dataset of 1321 polymers, a new ML model for Tg was built following past work. The root mean square error (RMSE) of prediction for the extended dataset, when compared to the earlier one, decreased to 27 K from 57 K, thereby supporting our initial hypothesis that increasing the dataset would improve the predictions. To further improve the performance of the Tg prediction model, we are continuing to accumulate new data and exploring new ML approaches. Polymer ; Mean squared error ; Predictive modelling ; Glass transition ; Machine learning ; Robustness (evolution) ; Computer science ; Transferability ; Materials science ; Artificial intelligence ; Biological system ; Mathematics ; Statistics ; Chemistry ; Composite material Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: David Zagaceta | Howard Yanxon | Qiang Zhu
Source: Journal of Applied Physics (article, 2020-07-28)
Field: Materials Science — Materials Chemistry

Spectral neural network potentials for binary alloys In this work, we present a numerical implementation to compute the atom-centered descriptors introduced by Bartok et al. [Phys. Rev. B 87, 184115 (2013)] based on the harmonic analysis of the atomic neighbor density function. Specifically, we focus on two types of descriptors, the smooth SO(3) power spectrum with the explicit inclusion of a radial basis and the SO(4) bispectrum obtained through mapping the radial component onto a polar angle of a four dimensional hypersphere. With these descriptors, various interatomic potentials for binary Ni–Mo alloys are obtained based on linear and neural network regression models. Numerical experiments suggest that both descriptors produce similar results in terms of accuracy. For linear regression, the smooth SO(3) power spectrum is superior to the SO(4) bispectrum when a large band limit is used. In neural network regression, better accuracy can be achieved with even less number of expansion components for both descriptors. As such, we demonstrate that spectral neural network potentials are feasible choices for large scale atomistic simulations. Bispectrum ; Artificial neural network ; Spectral density ; Binary number ; Hypersphere ; Polar ; Statistical physics ; Radial basis function ; Focus (optics) ; Mathematics ; Physics ; Algorithm ; Computer science ; Artificial intelligence ; Optics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Christian Devereux | Justin S. Smith | Kate Davis | Kipton Barros | R.I. Zubatyuk | Olexandr Isayev | Adrián E. Roitberg
Source: ChemRxiv (preprint, 2020-02-07)
Field: Materials Science — Materials Chemistry

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, S) make up ~90% of drug like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and non-bonded interactions. ANI-2x is shown to accurately predict molecular energies compared to DFT with a ~106 factor speedup and a negligible slowdown compared to ANI-1x. The resulting model is a valuable tool for drug development that can potentially replace both quantum calculations and classical force fields for myriad applications. Dihedral angle ; Computer science ; Benchmark (surveying) ; Organic molecules ; Molecule ; Halogen ; Computational chemistry ; Chemistry ; Computational science Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Yan Qi Huan | Yincheng Liu | Kuan Eng Johnson Goh | Swee Liang Wong | Chit Siong Lau
Source: Nanotechnology (article, 2020-12-23)
Field: Materials Science — Materials Chemistry

Deep learning-enabled prediction of 2D material breakdown Characterizing electrical breakdown limits of materials is a crucial step in device development. However, methods for repeatable measurements are scarce in two-dimensional materials, where breakdown studies have been limited to destructive methods. This restricts our ability to fully account for variability in local electronic properties induced by surface contaminants and the fabrication process. To tackle this, we implement a two-step deep-learning model to predict the breakdown mechanism and breakdown voltage of monolayer MoS<sub>2</sub>devices with varying channel lengths and resistances using current measured in the low-voltage regime as inputs. A deep neural network (DNN) first classifies between Joule and avalanche breakdown mechanisms using partial current traces from 0 to 20 V. Following this, a convolutional long short-term memory network (CLSTM) predicts breakdown voltages of these classified devices based on partial current traces. We test our model with electrical measurements collected using feedback-control of the applied voltage to prevent device destruction, and show that the DNN classifier achieves an accuracy of 79% while the CLSTM model has a 12% error when requiring only 80% of the current trace as inputs. Our results indicate that information encoded in the current behavior far from the breakdown point can be used for breakdown predictions, which will enable non-destructive and rapid material characterization for 2D material device development. Materials science ; Breakdown voltage ; Electrical breakdown ; Joule heating ; Current (fluid) ; Voltage ; Avalanche diode ; Computer science ; Optoelectronics ; Process (computing) ; Artificial neural network ; Engineering physics ; Electronic engineering ; Artificial intelligence ; Electrical engineering ; Composite material 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Graphene research and applications
Authors: Cheol Woo Park | Mordechai Kornbluth | Jonathan Vandermause | Chris Wolverton | Boris Kozinsky | Jonathan P. Mailoa
Source: arXiv (Cornell University) (preprint, 2020-07-28)
Field: Materials Science — Materials Chemistry

Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture Data includes the the ab initio molecular dynamic simulation of Li7P3S11 that was used to measure the performance of the GNNFF. The data is divided into training and testing sets. Brief descirption of the work: Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems. Molecular dynamics ; Scalability ; Force field (fiction) ; Computer science ; Covariant transformation ; Artificial neural network ; Invariant (physics) ; Limiting ; Computational science ; Topology (electrical circuits) ; Artificial intelligence ; Physics ; Computational chemistry ; Mathematics ; Chemistry ; Engineering ; Mechanical engineering ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Nuclear Materials and Properties
Authors: Dean Sumner | Jiazhen He | Amol Thakkar | Ola Engkvist | Esben Jannik Bjerrum
Source: ChemRxiv (preprint, 2020-06-29)
Field: Materials Science — Materials Chemistry

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction &lt;p&gt;SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as &lt;i&gt;attentional gain &lt;/i&gt;– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.&lt;/p&gt; Levenshtein distance ; Computer science ; Artificial intelligence ; Similarity (geometry) ; Transformer ; Sequence (biology) ; Machine learning ; Biology ; Engineering ; Genetics Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: Ruocheng Han | Sandra Luber
Source: Molecular Physics (article, 2020-07-04)
Field: Materials Science — Materials Chemistry

Trajectory-based machine learning method and its application to molecular dynamics Ab initio molecular dynamics (AIMD) has become a popular simulation technique but long simulation times are often hampered due to its high computational effort. Alternatively, classical molecular dynamics (MD) based on force fields may be used, which, however, has certain shortcomings compared to AIMD. In order to alleviate that situation, a trajectory-based machine learning (TrajML) approach is introduced for the construction of force fields by learning from AIMD trajectories. Only nuclear trajectories are required, which can be obtained by other methods beyond AIMD as well. We developed an easy-to-use MD machine learning package (TrajML MD) for instant modelling of the force field and system-focussed prediction of molecular configurations for MD trajectories. It consumes similar computational resources as classical MD but can simulate complex systems with a higher accuracy due to the targeted learning on the system of interest. Molecular dynamics ; Force field (fiction) ; Computer science ; Trajectory ; Field (mathematics) ; Complex system ; Artificial intelligence ; Work (physics) ; Dynamics (music) ; Statistical physics ; Machine learning ; Physics ; Chemistry ; Mathematics ; Computational chemistry ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications