jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziteng Liu | Liqiang Lin | Qingqing Jia | Zheng Cheng | Yanyan Jiang | Yanwen Guo | Jing Ma
Source: ChemRxiv (preprint, 2020-07-01)
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 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 strategy that enables chemical interpretable insights to be fused into multi-task learning of up to 110,000 records of data in QM9 for random split evaluation. The good transferability for predicting larger molecules outside the training set is demonstrated in both QM9 and Alchemy datasets. The efficient and accurate prediction of 12 properties including dipole moment, HOMO, and Gibbs free energy within chemical accuracy is achieved by using our specifically designed interpretable multi-level attention neural network, named as DeepMoleNet. Remarkably, the present multi-task deep learning model adopts the atom-centered symmetry functions (ACSFs) descriptor as one of the prediction targets, rather than using ACSFs as input in the conventional way. The proposed multi-level attention neural network is applicable to high-throughput screening of numerous chemical species to accelerate rational designs of drug, material, and chemical reactions. Computer science ; Artificial neural network ; Artificial intelligence ; HOMO/LUMO ; Machine learning ; Task (project management) ; Set (abstract data type) ; Quantum chemical ; Deep learning ; Molecular descriptor ; Quantitative structure–activity relationship ; Molecule ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: O. Anatole von Lilienfeld | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: Nature Reviews Chemistry (preprint, 2020-06-12)
Field: Materials Science — Materials Chemistry

Exploring chemical compound space with quantum-based machine learning Chemical space ; Computer science ; Quantum chemical ; Perspective (graphical) ; Space (punctuation) ; Set (abstract data type) ; Field (mathematics) ; Key (lock) ; Nanotechnology ; Data science ; Artificial intelligence ; Biochemical engineering ; Molecule ; Chemistry ; Materials science ; Physics ; Drug discovery ; Mathematics ; Engineering ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Frank Noé
Source: Lecture notes in physics (preprint, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Molecular Dynamics on Long Timescales Computer science ; Field (mathematics) ; Representation (politics) ; Molecular dynamics ; Estimator ; Machine learning ; Sampling (signal processing) ; Artificial intelligence ; Dynamics (music) ; Mathematics ; Physics ; Chemistry ; Computational chemistry ; Statistics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Neural Networks and Applications
Authors: Alessandro Lunghi
Source: Applied Magnetic Resonance (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

Insights into the Spin-Lattice Dynamics of Organic Radicals Beyond Molecular Tumbling: A Combined Molecular Dynamics and Machine-Learning Approach Molecular dynamics ; Hamiltonian (control theory) ; Statistical physics ; Solid-state physics ; Spin engineering ; Lattice (music) ; Tensor (intrinsic definition) ; Spin (aerodynamics) ; Physics ; Chemistry ; Chemical physics ; Quantum mechanics ; Spin polarization ; Thermodynamics ; Mathematics ; Electron Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Advanced Chemical Physics Studies
Authors: Tien-Lam Pham | Duong‐Nguyen Nguyen | Minh-Quyet Ha | Hiori Kino | Takashi Miyake | Hieu‐Chi Dam
Source: IUCrJ (article, 2020-09-22)
Field: Materials Science — Materials Chemistry

Explainable machine learning for materials discovery: predicting the potentially formable Nd–Fe–B crystal structures and extracting the structure–stability relationship New Nd-Fe-B crystal structures can be formed via the elemental substitution of <i>LA</i>-<i>T</i>-<i>X</i> host structures, including lanthanides (<i>LA</i>), transition metals (<i>T</i>) and light elements, <i>X</i> = B, C, N and O. The 5967 samples of ternary <i>LA</i>-<i>T</i>-<i>X</i> materials that are collected are then used as the host structures. For each host crystal structure, a substituted crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe and all light-element sites with B. High-throughput first-principles calculations are applied to evaluate the phase stability of the newly created crystal structures, and 20 of them are found to be potentially formable. A data-driven approach based on supervised and unsupervised learning techniques is applied to estimate the stability and analyze the structure-stability relationship of the newly created Nd-Fe-B crystal structures. For predicting the stability for the newly created Nd-Fe-B structures, three supervised learning models: kernel ridge regression, logistic classification and decision tree model, are learned from the <i>LA</i>-<i>T</i>-<i>X</i> host crystal structures; the models achieved maximum accuracy and recall scores of 70.4 and 68.7%, respectively. On the other hand, our proposed unsupervised learning model based on the integration of descriptor-relevance analysis and a Gaussian mixture model achieved an accuracy and recall score of 72.9 and 82.1%, respectively, which are significantly better than those of the supervised models. While capturing and interpreting the structure-stability relationship of the Nd-Fe-B crystal structures, the unsupervised learning model indicates that the average atomic coordination number and coordination number of the Fe sites are the most important factors in determining the phase stability of the new substituted Nd-Fe-B crystal structures. Stability (learning theory) ; Computer science ; Data science ; Nanotechnology ; Chemistry ; Machine learning ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: L. Chen | Alexander Kaiser | Michael Probst | Shokirbek Shermukhamedov
Source: Nuclear Fusion (article, 2020-11-12)
Field: Materials Science — Materials Chemistry

Sputtering of the beryllium tungsten alloy Be<sub>2</sub>W by deuterium atoms: molecular dynamics simulations using machine learned forces Abstract Material erosion and fuel retention will limit the life and the performance of thermonuclear fusion reactors. In this work, sputtering, reflection and retention processes are atomistically modeled by simulating the non-cumulative sputtering by deuterium projectiles on a beryllium–tungsten alloy surface. The forces for the molecular dynamics trajectories were machine learned from density functional theory with a neural network architecture. Our data confirms and supplements previous results for simulated sputtering rates. In the non-cumulative scenario we simulate, we did not observe reaction mechanisms leading to swift chemical sputtering. Thus, our sputtering rates at low impact energies are smaller than in comparable non-cumulative studies. The sputtering yields of the Be 2 W alloy are generally lower than those of pure beryllium. We found a strong dependence of the sputtering yield on the incident angle with an increase by about a factor of 3 for larger incident angles at 100 eV impact energy. In the pristine surface, a large majority of the impacting hydrogen projectiles at perpendicular impact remains in the surface. Sputtering ; Beryllium ; Tungsten ; Materials science ; Projectile ; Deuterium ; Thermonuclear fusion ; Atomic physics ; Fusion power ; Yield (engineering) ; Molecular dynamics ; Alloy ; Nuclear physics ; Plasma ; Metallurgy ; Physics ; Nanotechnology ; Thin film Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear Physics and Applications
Authors: Jie Zhang | Rocío Mercado | Ola Engkvist | Hongming Chen
Source: ChemRxiv (preprint, 2020-12-18)
Field: Materials Science — Materials Chemistry

Comparative Study of Deep Generative Models on Chemical Space Coverage In recent years, deep molecular generative models have emerged as novel methods for de novo molecular design. Thanks to the rapid advance of deep learning techniques, deep learning architectures such as recurrent neural networks, generative autoencoders, and adversarial networks, to give a few examples, have been employed for constructing generative models. However, so far the metrics used to evaluate these deep generative models are not discriminative enough to separate the performance of various state-of-the-art generative models. This work presents a novel metric for evaluating deep molecular generative models; this new metric is based on the chemical space coverage of a reference database, and compares not only the molecular structures, but also the ring systems and functional groups, reproduced from a reference dataset of 1M structures. In this study, the performance of 7 different molecular generative models was compared by calculating their structure and substructure coverage of the GDB-13 database while using a 1M subset of GDB-13 for training. Our study shows that the performance of various generative models varies significantly using the benchmarking metrics introduced herein, such that generalization capability of the generative model can be clearly differentiated. Additionally, the coverage of ring systems and functional groups existing in GDB-13 was also compared between the models. Our study provides a useful new metric that can be used for evaluating and comparing generative models. Generative grammar ; Computer science ; Artificial intelligence ; Metric (unit) ; Deep learning ; Generative model ; Discriminative model ; Machine learning ; Generative Design ; Generalization ; Benchmark (surveying) ; Chemical space ; Mathematics ; Engineering ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Daniel G. A. Smith | Doaa Altarawy | Lori A. Burns | Matthew Welborn | Levi N. Naden | Logan Ward | Sam Ellis | Thomas O. Crawford
Source: ChemRxiv (preprint, 2020-03-04)
Field: Materials Science — Materials Chemistry

The MolSSI QCArchive Project: An Open-Source Platform to Compute, Organize, and Share Quantum Chemistry Data The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python-based software infrastructure that powers automated computation and storage of quantum chemistry results.The MolSSI-hosted central server provides the computational molecular sciences community a location to freely access tens of millions of quantum chemistry computations for machine learning, methodology assessment, force-field fitting, and more through a Python interface.Facile, user-friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at https://qcarchive.molssi.org.The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of quantum chemistry computations for individuals or groups of researchers at any scale.The QCArchive Infrastructure is open-source (BSD-3C), code repositories can be found at https://github.com/MolSSI, and releases can be downloaded via PyPI and Conda. Python (programming language) ; Computer science ; Software ; Quantum computer ; Computational science ; Computation ; Interface (matter) ; Quantum ; Quantum chemistry ; World Wide Web ; Source code ; Chemistry ; Operating system ; Physics ; Programming language ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: David K. Kreutter | Philippe Schwaller | Jean‐Louis Reymond
Source: ChemRxiv (preprint, 2020-10-30)
Field: Materials Science — Materials Chemistry

Predicting Enzymatic Reactions with a Molecular Transformer The use of enzymes for organic synthesis allows for simplified, more economical and selective synthetic routes not accessible to conventional reagents. However, predicting whether a particular molecule might undergo a specific enzyme transformation is very difficult. Here we exploited recent advances in computer assisted synthetic planning (CASP) by considering the Molecular Transformer, which is a sequence-to-sequence machine learning model that can be trained to predict the products of organic transformations, including their stereochemistry, from the structure of reactants and reagents. We used multi-task transfer learning to train the Molecular Transformer with one million reactions from the US Patent Office (USPTO) database as a source of general chemistry knowledge combined with 32,000 enzymatic transformations, each one annotated with a text description of the enzyme. We show that the resulting Enzymatic Transformer model predicts the products formed from a given substrate and enzyme with remarkable accuracy, including typical kinetic resolution processes. Transformer ; Reagent ; Enzyme ; Computer science ; Combinatorial chemistry ; Chemistry ; Organic molecules ; CASP ; Artificial intelligence ; Molecule ; Protein structure ; Biochemistry ; Organic chemistry ; Engineering ; Protein structure prediction ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: D. Šević | Ana Vlašić | M.S. Rabasović | Svetlana Savić-Šević | Mihailo D. Rabasović | Marko G. Nikolić | Branka Murić | Bratislav P. Marinković | Janez Križan
Source: Tehnika (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Temperature effects on luminescent properties of Sr2CeO4:Eu3+ nanophosphor: A machine learning approach In this paper we analyze possibilities of application of Sr2CeO4:Eu3+ nanopowder for temperature sensing using machine learning. The material was prepared by simple solution combustion synthesis. Photoluminescence technique has been used to measure the optical emission temperature dependence of the prepared material. Principal Component Analysis, the basic machine learning algorithm, provided insight into temperature dependent spectral data from another point of view than usual approach. Photoluminescence ; Luminescence ; Principal component analysis ; Materials science ; Combustion ; Measure (data warehouse) ; Point (geometry) ; Computer science ; Spectral properties ; Process engineering ; Simple (philosophy) ; Biological system ; Artificial intelligence ; Optoelectronics ; Chemistry ; Data mining ; Mathematics ; Physical chemistry ; Engineering Luminescence Properties of Advanced Materials ; Gas Sensing Nanomaterials and Sensors ; Radiation Detection and Scintillator Technologies
Authors: Eszter Benes | Dávid Bajusz | Attila Gere | Marietta Fodor | Anita Rácz
Source: LWT (article, 2020-08-27)
Field: Chemistry — Analytical Chemistry

Comprehensive chemometric classification of snack products based on their near infrared spectra Artificial neural network ; Robustness (evolution) ; Artificial intelligence ; Computer science ; Backpropagation ; Machine learning ; Partial least squares regression ; Linear discriminant analysis ; Quality assurance ; Pattern recognition (psychology) ; Data mining ; Algorithm ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: M.J. Jiménez–Come | María de la Luz Martín | Victoria Matres | Jesús Daniel Mena Baladés
Source: Corrosion Reviews (article, 2020-07-27)
Field: Materials Science — Metals and Alloys

The use of artificial neural networks for modelling pitting corrosion behaviour of EN 1.4404 stainless steel in marine environment: data analysis and new developments Abstract Stainless steel has proved to be an important material to be used in a wide range of applications. For this reason, ensuring the durability of this alloy is essential. In this work, pitting corrosion behaviour of EN 1.4404 stainless steel is evaluated in marine environment in order to develop a model capable of predicting its pitting corrosion status by an automatic way. Although electrochemical techniques and microscopic analysis have been shown to be very useful tools for corrosion studies, these techniques may present some limitationus. With the aim to solve these drawbacks, a three-step model based on Artificial Neural Networks (ANNs) is proposed. The results reveal that the model can be used to predict pitting corrosion status of this alloy with satisfactory sensitivity and specificity with no need to resort to electrochemical tests or microscopic analysis. Therefore, the proposed model becomes a useful tool to predict the behaviour of the material against pitting corrosion in saline environment automatically. Pitting corrosion ; Materials science ; Corrosion ; Metallurgy ; Durability ; Alloy ; Artificial neural network ; Computer science ; Composite material ; Artificial intelligence Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques
Authors: Emmanuel Okafor | David O. Obada | Yusuf Ibrahim | David Dodoo‐Arhin
Source: Engineering Reports (article, 2020-10-15)
Field: Materials Science — Materials Chemistry

Prediction of the reflection intensity of natural hydroxyapatite using generalized linear model and ensemble learning methods Abstract Laboratory data acquisition and analysis of X‐ray diffraction (XRD) data involves a lot of tedious human engineering and is time‐consuming. To put in context, a summation of the material synthesis procedure leading to the analysis of the structure of the material can span several days. To curb this challenge and to enhance innovations in engineering pedagogy, this article investigates an alternative method that uses supervised learning algorithms based on ensemble techniques and a generalized linear model (GLM) for predicting reflection intensity (XRD patterns) of raw and natural hydroxyapatite under varying sintering temperature conditions given Bragg angles as input to the machine learning algorithms. For the experiment, we trained GLM and ensemble learning models (CatBoost, LightGBM, and two variants of XGBoost based on manual and genetic algorithm for tuning of the hyperparameters). The results show that most instances of the XGBoost yielded a robust performance that surpasses all other approaches, when predicting X‐ray reflection intensities ascribed to the biomaterials subjected to varying sintering temperature conditions. In addition, the results show that all the ensemble techniques significantly outperform the GLM, this indicates that the former exhibits better generalization capacity. The ensemble learning techniques and the GLM presents a reduced computational complexity. Ensemble learning ; Generalization ; Reflection (computer programming) ; Generalized linear model ; Context (archaeology) ; Hyperparameter ; Computer science ; Ensemble forecasting ; Intensity (physics) ; Artificial intelligence ; Machine learning ; Algorithm ; Mathematics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Tori Cox | Volodymyr Gvozdetskyi | Mark Bertolami | Shannon Lee | Kristian Shipley | Oleg I. Lebedev | Julia V. Zaikina
Source: Angewandte Chemie International Edition (article, 2020-09-16)
Field: Materials Science — Materials Chemistry

Clathrate XI K<sub>58</sub>Zn<sub>122</sub>Sb<sub>207</sub>: A New Branch on the Clathrate Family Tree The compositional screening of K-Zn-Sb ternary system aided by machine learning, rapid exploratory synthesis using KH salt-like precursor and in situ powder X-ray diffraction yielded a novel clathrate type XI K<sub>58</sub> Zn<sub>122</sub> Sb<sub>207</sub> . This clathrate consists of a 3D Zn-Sb framework hosting K<sup>+</sup> ions inside polyhedral cages, some of which are reminiscent of known clathrate types while others are unique to this structure type. The complex non-centrosymmetric structure in the tetragonal space group <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>I</mml:mi> <mml:mover><mml:mn>4</mml:mn> <mml:mo>‾</mml:mo></mml:mover> <mml:mn>2</mml:mn> <mml:mi>m</mml:mi></mml:mrow> </mml:math> was solved by means of single crystal X-ray diffraction as a 6-component twin due to pseudocubic symmetry and further confirmed by high-resolution synchrotron powder X-ray diffraction and state-of-the-art scanning transmission electron microscopy. The electron-precise composition of this clathrate yields narrow-gap p-type semiconductor with extraordinarily low thermal conductivity due to displacement or "rattling" of K cations inside oversized cages and as well as to twinning, stacking faults and antiphase boundary defects. Clathrate hydrate ; Crystal twinning ; Crystallography ; Tetragonal crystal system ; Powder diffraction ; Chemistry ; Crystal structure ; Materials science ; Hydrate Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Solid-state spectroscopy and crystallography
Authors: Amanda Parker | Benyamin Motevalli | George Opletal | Amanda S. Barnard
Source: Nanotechnology (article, 2020-11-20)
Field: Materials Science — Materials Chemistry

The pure and representative types of disordered platinum nanoparticles from machine learning The development of interpretable structure/property relationships is a cornerstone of nanoscience, but can be challenging when the structural diversity and complexity exceeds our ability to characterise it. This is often the case for imperfect, disordered and amorphous nanoparticles, where even the nomenclature can be unspecific. Disordered platinum nanoparticles have exhibited superior performance for some reactions, which makes a systematic way of describing them highly desirable. In this study we have used a diverse set of disorder platinum nanoparticles and machine learning to identify the pure and representative structures based on their similarity in 121 dimensions. We identify two prototypes that are representative of separable classes, and seven archetypes that are the pure structures on the convex hull with which all other possibilities can be described. Together these nine nanoparticles can explain all of the variance in the set, and can be described as either single crystal, twinned, spherical or branched; with or without roughened surfaces. This forms a robust sub-set of platinum nanoparticle upon which to base further work, and provides a theoretical basis for discussing structure/property relationships of platinum nanoparticles that are not geometrically ideal. Materials science ; Nanoparticle ; Platinum ; Platinum nanoparticles ; Nanotechnology ; Amorphous solid ; Set (abstract data type) ; Chemical physics ; Computer science ; Crystallography Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Surface Chemistry and Catalysis