jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dávid Péter Kovács | William McCorkindale | Alpha A. Lee
Source: ChemRxiv (preprint, 2020-10-07)
Field: Materials Science — Materials Chemistry

Quantitative Interpretation Explains Machine Learning Models for Chemical Reaction Prediction and Uncovers Bias Organic synthesis remains a stumbling block in drug discovery. Although a plethora of machine learning models have been proposed as solutions in the literature, they suffer from being opaque black-boxes. It is neither clear if the models are making correct predictions because they inferred the salient chemistry, nor is it clear which training data they are relying on to reach a prediction. This opaqueness hinders both model developers and users. In this paper, we quantitatively interpret the Molecular Transformer, the state-of-the-art model for reaction prediction. We develop a framework to attribute predicted reaction outcomes both to specific parts of reactants, and to reactions in the training set. Furthermore, we demonstrate how to retrieve evidence for predicted reaction outcomes, and understand counterintuitive predictions by scrutinising the data. Additionally, we identify ”Clever Hans” predictions where the correct prediction is reached for the wrong reason due to dataset bias. We present a new debiased dataset that provides a more realistic assessment of model performance, which we propose as the new standard benchmark for comparing reaction prediction models. Computer science ; Salient ; Machine learning ; Artificial intelligence ; Counterintuitive ; Benchmark (surveying) ; Interpretation (philosophy) ; Predictive modelling ; Set (abstract data type) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Xiao Hu | Yaqiong Zhang | Shuaiyu Fan | Xin Li | Zhenjie Zhao | Chao He | Yong-Hong Zhao | Yong Liu | Wenhui Xie
Source: Journal of Physics Condensed Matter (article, 2020-01-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Searching high spin polarization ferromagnet in Heusler alloy via machine learning In order to search for stable ferromagnets with high spin polarization in Heusler alloys for spintronic applications, we develop an efficient machine learning workflow based on a deep neural network, whose training data were collected from the open quantum materials database and high throughput calculation by first-principle calculations. The lattice constants, formation energy and spin polarization of 10 577 candidate materials were predicted, and 192 materials with high spin polarization were selected according to a spin polarization greater than 0.87 and formation energy less than 80 meV/atom. 57 of these alloys have been reported as Half-metal (100% spin polarization) according to previous researches, and 18 have been reported as semiconductors. Especially, 6 Heusler alloys were identified as promising half-metallic ferromagnets, and some of them have high Curie temperature above room temperature. Our study suggests this approach is an efficient method for the discovery of superior spintronic materials, which should be also suitable for exploring other functional materials. Spintronics ; Spin polarization ; Ferromagnetism ; Condensed matter physics ; Materials science ; Curie temperature ; Polarization (electrochemistry) ; Alloy ; Spin engineering ; Engineering physics ; Metallurgy ; Physics ; Chemistry ; Quantum mechanics ; Electron ; Physical chemistry Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Stephen Wu | H. Yamada | Y. Hayashi | Massimiliano Zamengo | Ryo Yoshida
Source: arXiv (Cornell University) (preprint, 2020-10-15)
Field: Materials Science — Materials Chemistry

Potentials and challenges of polymer informatics: exploiting machine learning for polymer design There has been rapidly growing demand of polymeric materials coming from different aspects of modern life because of the highly diverse physical and chemical properties of polymers. Polymer informatics is an interdisciplinary research field of polymer science, computer science, information science and machine learning that serves as a platform to exploit existing polymer data for efficient design of functional polymers. Despite many potential benefits of employing a data-driven approach to polymer design, there has been notable challenges of the development of polymer informatics attributed to the complex hierarchical structures of polymers, such as the lack of open databases and unified structural representation. In this study, we review and discuss the applications of machine learning on different aspects of the polymer design process through four perspectives: polymer databases, representation (descriptor) of polymers, predictive models for polymer properties, and polymer design strategy. We hope that this paper can serve as an entry point for researchers interested in the field of polymer informatics. Informatics ; Materials informatics ; Polymer ; Computer science ; Process (computing) ; Data science ; Field (mathematics) ; Polymer architecture ; Representation (politics) ; Nanotechnology ; Engineering informatics ; Health informatics ; Systems engineering ; Materials science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Paraskevi Gkeka | Gabriel Stoltz | Amir Barati Farimani | Zineb Belkacemi | Michele Ceriotti | John D. Chodera | Aaron R. Dinner | Andrew L. Ferguson | Jean‐Bernard Maillet | Hervé Minoux | Christine Peter | Fabio Pietrucci | Ana Silveira | Alexandre Tkatchenko | Zofia Trstanova | Rafal Wiewiora | Tony Lelièvre
Source: Journal of Chemical Theory and Computation (preprint, 2020-06-19)
Field: Materials Science — Materials Chemistry

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling. Computer science ; Computation ; Field (mathematics) ; Force field (fiction) ; Set (abstract data type) ; Machine learning ; Molecular dynamics ; Artificial intelligence ; Data science ; Algorithm ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Yue Li | Bjørn Holmedal | Boyu Liu | Hongxiang Li | Linzhong Zhuang | Jishan Zhang | Qiang Du | Jianxin Xie
Source: Calphad (article, 2020-12-03)
Field: Materials Science — Materials Chemistry

Towards high-throughput microstructure simulation in compositionally complex alloys via machine learning The coupling of computational thermodynamics and kinetics has been the central research theme in Integrated Computational Material Engineering (ICME). Two major bottlenecks in implementing this coupling and performing efficient ICME-guided high-throughput multi-component industrial alloys discovery or process parameters optimization, are slow responses in kinetic calculations to a given set of compositions and processing conditions and the quality of a large amount of calculated thermodynamic data. Here, we employ machine learning techniques to eliminate them, including (1) intelligent corrupt data detection and re-interpolation (i.e. data purge/cleaning) to a big tabulated thermodynamic dataset based on an unsupervised learning algorithm and (2) parameterization via artificial neural networks of the purged big thermodynamic dataset into a non-linear equation consisting of base functions and parameterization coefficients. The two techniques enable the efficient linkage of high-quality data with a previously developed microstructure model. This proposed approach not only improves the model performance by eliminating the interference of the corrupt data and stability due to the boundedness and continuity of the obtained non-linear equation but also dramatically reduces the running time and demand for computer physical memory simultaneously. The high computational robustness, efficiency, and accuracy, which are prerequisites for high-throughput computing, are verified by a series of case studies on multi-component aluminum, steel, and high-entropy alloys. The proposed data purge and parameterization methods are expected to apply to various microstructure simulation approaches or to bridging the multi-scale simulation where handling a large amount of input data is required. It is concluded that machine learning is a valuable tool in fueling the development of ICME and high throughput materials simulations. Computer science ; Big data ; Algorithm ; Data mining Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Shin Kiyohara | Teruyasu Mizoguchi
Source: Journal of the Physical Society of Japan (article, 2020-09-29)
Field: Materials Science — Materials Chemistry

Radial Distribution Function from X-ray Absorption near Edge Structure with an Artificial Neural Network Extended X-ray absorption fine structure (EXAFS) is one of the common tools used to determine the local atomic coordination and bond length around an objective element via data conversion from the EXAFS profile to a radial distribution function (RDF). Thus, EXAFS has been widely used for the investigation of catalytic reactions, battery degradation, and other material developments. However, the data conversion from EXAFS to RDF involves three difficulties: weak signals, necessity of a wide energy range (∼1000 eV), and fitting parameters in reference samples. Recently, the direct estimation technique of RDF from EXAFS using machine learning was successfully developed, which overcomes the third problem. Here, we used an artificial neural network (ANN) to directly predict RDF from the near-edge region of the spectrum (X-ray absorption near-edge structure: XANES), which enables overcoming all of the difficulties. The ANN can correctly generate RDF only from XANES. XANES includes information about the bond length and coordination numbers in the range of ∼5 Å. We applied our prediction model to an experimental spectrum and confirmed its accuracy. The method proposed here is greatly beneficial for measuring the local bond length and coordination of materials whose extended energy region is difficult to be measured. Extended X-ray absorption fine structure ; XANES ; Absorption (acoustics) ; Materials science ; Coordination number ; Artificial neural network ; Bond length ; Radial distribution function ; Range (aeronautics) ; Surface-extended X-ray absorption fine structure ; Computer science ; Absorption spectroscopy ; Optics ; Physics ; Chemistry ; Artificial intelligence ; Crystallography ; Spectroscopy ; Computational chemistry ; Molecular dynamics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Zekun Ren | Juhwan Noh | Siyu Tian | Felipe Oviedo | Guangzong Xing | Qiaohao Liang | Armin G. Aberle | Yi Liu | Qianxiao Li | J. Senthilnath | Kedar Hippalgaonkar | Yousung Jung | Tonio Buonassisi
Source: arXiv (Cornell University) (preprint, 2020-05-15)
Field: Materials Science — Materials Chemistry

Inverse design of crystals using generalized invertible crystallographic representation Deep learning has fostered many novel applications in materials informatics. However, the inverse design of inorganic crystals, $\textit{i.e.}$ generating new crystal structure with targeted properties, remains a grand challenge. An important ingredient for such generative models is an invertible representation that accesses the full periodic table. This is challenging due to limited data availability and the complexity of 3D periodic crystal structures. In this paper, we present a generalized invertible representation that encodes the crystallographic information into the descriptors in both real space and reciprocal space. Combining with a generative variational autoencoder (VAE), a wide range of crystallographic structures and chemistries with desired properties can be inverse-designed. We show that our VAE model predicts novel crystal structures that do not exist in the training and test database (Materials Project) with targeted formation energies and band gaps. We validate those predicted crystals by first-principles calculations. Finally, to design solids with practical applications, we address the sparse label problem by building a semi-supervised VAE and demonstrate its successful prediction of unique thermoelectric materials Invertible matrix ; Representation (politics) ; Inverse ; Crystal (programming language) ; Autoencoder ; Crystal structure prediction ; Range (aeronautics) ; Computer science ; Space (punctuation) ; Crystal structure ; Mathematics ; Crystallography ; Theoretical computer science ; Materials science ; Artificial intelligence ; Pure mathematics ; Artificial neural network ; Chemistry ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Mohammad Babar | Holden Parks | Gregory Houchins | Venkatasubramanian Viswanathan
Source: Journal of Physics Energy (article, 2020-11-11)
Field: Materials Science — Materials Chemistry

An accurate machine learning calculator for the lithium-graphite system Abstract Machine-learning potentials are accelerating the development of energy materials, especially in identifying phase diagrams and other thermodynamic properties. In this work, we present a neural network potential based on atom-centered symmetry function descriptors to model the energetics of lithium intercalation into graphite. The potential was trained on a dataset of over 9000 diverse lithium–graphite configurations that varied in applied stress and strain, lithium concentration, lithium–carbon and lithium–lithium bond distances, and stacking order to ensure wide sampling of the potential atomic configurations during intercalation. We calculated the energies of these structures using density functional theory (DFT) through the Bayesian error estimation functional with van der Waals correlation exchange-correlation functional, which can accurately describe the van der Waals interactions that are crucial to determining the thermodynamics of this phase space. Bayesian optimization, as implemented in Dragonfly , was used to select optimal set of symmetry function parameters, ultimately resulting in a potential with a prediction error of 8.24 meV atom −1 on unseen test data. The potential can predict energies, structural properties, and elastic constants at an accuracy comparable to other DFT exchange-correlation functionals at a fraction of the computational cost. The accuracy of the potential is also comparable to similar machine-learned potentials describing other systems. We calculate the open circuit voltage with the calculator and find good agreement with experiment, especially in the regime x ≥ 0.3, for x in Li x C 6 . This study further illustrates the power of machine learning potentials, which promises to revolutionize design and optimization of battery materials. van der Waals force ; Density functional theory ; Lithium (medication) ; Machine learning ; Statistical physics ; Chemistry ; Artificial intelligence ; Thermodynamics ; Computer science ; Algorithm ; Physics ; Computational chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advancements in Battery Materials
Authors: Emre Topal | Zhongquan Liao | Markus Löffler | Jürgen Gluch | Jian Zhang | Xinliang Feng | Ehrenfried Zschech
Source: BMC Materials (article, 2020-03-06)
Field: Materials Science — Materials Chemistry

Multi-scale X-ray tomography and machine learning algorithms to study MoNi4 electrocatalysts anchored on MoO2 cuboids aligned on Ni foam Abstract For a systematic materials selection and for design and synthesis of systems for electrochemical energy conversion with specific properties, it is essential to clarify the general relationship between physicochemical properties of the materials and the electrocatalytic performance and stability of the system or device. The design of highly performant and durable 3D electrocatalysts requires an optimized hierarchical morphology and surface structures with high activity. A systematic approach to determine the 3D morphology of hierarchically structured materials with high accuracy is described, based on a multi-scale X-ray tomography study. It is applied to a novel transition-metal-based materials system: MoNi 4 electrocatalysts anchored on MoO 2 cuboids aligned on Ni foam. The high accuracy of 3D morphological data of the formed micro- and nanostructures is ensured by applying machine learning algorithms for the correction of imaging artefacts of high-resolution X-ray tomography such as beam hardening and for the compensation of experimental inaccuracies such as misalignment and motions of samples and tool components. This novel approach is validated based on the comparison of virtual cross-sections through the MoNi 4 electrocatalysts and real FIB cross-sections imaged in the SEM. Materials science ; Nanostructure ; Algorithm ; Tomography ; Nanotechnology ; Computer science ; Nanoscopic scale ; X-ray ; Cuboid ; Mechanical engineering ; Optics ; Physics ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Electron Microscopy Techniques and Applications
Authors: Andrea Guzmán-Mesa | Daniel Kitzmann | Chloe Fisher | Adam J. Burgasser | H. J. Hoeijmakers | Márquez Neila, Pablo | Simon L. Grimm | Avi M. Mandell | Raphael Sznitman | Kevin Heng
Source: Bern Open Repository and Information System (University of Bern) (article, 2020-06-12)
Field: Chemistry — Spectroscopy

Information Content of JWST NIRSpec Transmission Spectra of Warm Neptunes Warm Neptunes offer a rich opportunity for understanding exo-atmospheric chemistry. With the upcoming James Webb Space Telescope (JWST), there is a need to elucidate the balance between investments in telescope time versus scientific yield. We use the supervised machine-learning method of the random forest to perform an information content (IC) analysis on a 11-parameter model of transmission spectra from the various NIRSpec modes. The three bluest medium-resolution NIRSpec modes (0.7–1.27 μm, 0.97–1.84 μm, 1.66–3.07 μm) are insensitive to the presence of CO. The reddest medium-resolution mode (2.87–5.10 μm) is sensitive to all of the molecules assumed in our model: CO, CO2, CH4, C2H2, H2O, HCN, and NH3. It competes effectively with the three bluest modes on the information encoded on cloud abundance and particle size. It is also competitive with the low-resolution prism mode (0.6–5.3 μm) on the inference of every parameter except for the temperature and ammonia abundance. We recommend astronomers to use the reddest medium-resolution NIRSpec mode for studying the atmospheric chemistry of 800–1200 K warm Neptunes; its corresponding high-resolution counterpart offers diminishing returns. We compare our findings to previous JWST IC analyses that favor the blue orders and suggest that the reliance on chemical equilibrium could lead to biased outcomes if this assumption does not apply. A simple, pressure-independent diagnostic for identifying chemical disequilibrium is proposed based on measuring the abundances of H2O, CO, and CO2. James Webb Space Telescope ; Abundance (ecology) ; Spectral line ; Resolution (logic) ; Debris ; Chemistry ; Astrophysics ; Telescope ; Environmental science ; Physics ; Analytical Chemistry (journal) ; Computer science ; Meteorology ; Astronomy ; Artificial intelligence ; Environmental chemistry ; Biology ; Ecology Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Astro and Planetary Science
Authors: Takamichi Terao
Source: Soft Materials (article, 2020-01-20)
Field: Materials Science — Materials Chemistry

A machine learning approach to analyze the structural formation of soft matter via image recognition ABSTRACT A novel method is developed to analyze the structural formation of colloidal particles based on image recognition via a convolutional neural network (CNN). This makes it possible to analyze various complex structures that are difficult to study using a traditional bond-order parameter analysis. Molecular dynamics simulations on soft colloidal systems are performed in quasi two-dimensional and three-dimensional systems, and the efficiency of the proposed method is demonstrated. Computer science ; Soft matter ; Image (mathematics) ; Convolutional neural network ; Artificial intelligence ; Colloid ; Biological system ; Artificial neural network ; Materials science ; Pattern recognition (psychology) ; Engineering ; Chemical engineering Material Dynamics and Properties ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Loı̈c M. Roch | Semion K. Saikin | Florian Häse | Pascal Friederich | Randall H. Goldsmith | Salvador León | Alán Aspuru‐Guzik
Source: ACS Nano (article, 2020-04-27)
Field: Materials Science — Polymers and Plastics

From Absorption Spectra to Charge Transfer in Nanoaggregates of Oligomers with Machine Learning Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes of Bayesian machine learning (ML) models to correlate electronic absorption spectra of nanoaggregates with the strength of intermolecular electronic couplings in organic conducting and semiconducting materials. As a specific model system, we consider poly(3,4-ethylenedioxythiophene) (PEDOT) polystyrene sulfonate, a cornerstone material for organic electronic applications, and so analyze the couplings between charged dimers of closely packed PEDOT oligomers that are at the heart of the material's unrivaled conductivity. We demonstrate that ML algorithms can identify correlations between the coupling strengths and the electronic absorption spectra. We also show that ML models can be trained to be transferable across a broad range of spectral resolutions and that the electronic couplings can be predicted from the simulated spectra with an 88% accuracy when ML models are used as classifiers. Although the ML models employed in this study were trained on data generated by a multiscale computational workflow, they were able to leverage experimental data. Charge (physics) ; Materials science ; Absorption (acoustics) ; Chemical physics ; Absorption spectroscopy ; Spectral line ; Nanotechnology ; Photochemistry ; Optoelectronics ; Chemistry ; Optics ; Physics ; Composite material Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing