jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Oliver T Unke | Debasish Koner | Sarbani Patra | Silvan Käser | Markus Meuwly
Source: Machine Learning Science and Technology (article, 2020-02-25)
Field: Materials Science — Materials Chemistry

High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning Abstract An overview of computational methods to describe high-dimensional potential energy surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy, computability, transferability and extensibility of the methods discussed. They include empirical force fields, representations based on reproducing kernels, using permutationally invariant polynomials, neural network-learned representations and combinations thereof. Future directions and potential improvements are discussed primarily from a practical, application-oriented perspective. Transferability ; Invariant (physics) ; Energy (signal processing) ; Potential energy ; Empiricism ; Artificial neural network ; Computation Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Zhilong Wang | Haikuo Zhang | Jinjin Li
Source: Nano Energy (article, 2020-12-08)
Field: Materials Science — Materials Chemistry

Accelerated discovery of stable spinels in energy systems via machine learning Materials science ; Spinel ; Photovoltaics ; Semiconductor ; Nanotechnology ; Band gap ; Throughput ; Optoelectronics ; Engineering physics ; Computer science ; Photovoltaic system ; Electrical engineering ; Physics ; Wireless Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Chalcogenide Semiconductor Thin Films
Authors: Joseph R. Cendagorta | Jocelyn Tolpin | Elia Schneider | Robert Q. Topper | Mark E. Tuckerman
Source: The Journal of Physical Chemistry B (article, 2020-04-10)
Field: Materials Science — Materials Chemistry

Comparison of the Performance of Machine Learning Models in Representing High-Dimensional Free Energy Surfaces and Generating Observables Free energy surfaces of chemical and physical systems are often generated using a popular class of enhanced sampling methods that target a set of collective variables (CVs) chosen to distinguish the characteristic features of these surfaces. While some of these approaches are typically limited to low (∼1-3)-dimensional CV subspaces, methods such as driven adiabatic free-energy dynamics/temperature-accelerated molecular dynamics have been shown to be capable of generating free energy surfaces of quite high dimension by sampling the associated marginal probability distribution via full sweeps over the CV landscape. These approaches repeatedly visit conformational basins, producing a scattering of points within the basins on each visit. Consequently, they are particularly amenable to synergistic combination with regression machine learning methods for filling in the surfaces between the sampled points and for providing a compact and continuous (or semicontinuous) representation of the surfaces that can be easily stored and used for further computation of observable properties. Given the central role of machine learning techniques in this combined approach, it is timely to provide a detailed comparison of the performance of different machine learning strategies and models, including neural networks, kernel ridge regression, support vector machines, and weighted neighbor schemes, for their ability to learn these high-dimensional surfaces as a function of the amount of sampled training data and, once trained, to subsequently generate accurate ensemble averages corresponding to observable properties of the systems. In this article, we perform such a comparison on a set of oligopeptides, in both gas and aqueous phases, corresponding to CV spaces of 2-10 dimensions and assess their ability to provide a global representation of the free energy surfaces and to generate accurate ensemble averages. Observable ; Energy (signal processing) ; Computer science ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Scientific Research and Discoveries ; Electron and X-Ray Spectroscopy Techniques
Authors: Scott M. Woodley | Graeme M. Day | C. Richard A. Catlow
Source: Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2020-10-26)
Field: Materials Science — Materials Chemistry

Structure prediction of crystals, surfaces and nanoparticles We review the current techniques used in the prediction of crystal structures and their surfaces and of the structures of nanoparticles. The main classes of search algorithm and energy function are summarized, and we discuss the growing role of methods based on machine learning. We illustrate the current status of the field with examples taken from metallic, inorganic and organic systems. This article is part of a discussion meeting issue ‘Dynamic in situ microscopy relating structure and function’. Nanoparticle ; Field (mathematics) ; Computer science ; Function (biology) ; Nanotechnology ; Current (fluid) ; Materials science ; Physics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Johannes Klicpera | Shankari Giri | Johannes T. Margraf | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2020-11-28)
Field: Materials Science — Materials Chemistry

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules. Many important tasks in chemistry revolve around molecules during reactions. This requires predictions far from the equilibrium, while most recent work in machine learning for molecules has been focused on equilibrium or near-equilibrium states. In this paper we aim to extend this scope in three ways. First, we propose the DimeNet++ model, which is 8x faster and 10% more accurate than the original DimeNet on the QM9 benchmark of equilibrium molecules. Second, we validate DimeNet++ on highly reactive molecules by developing the challenging COLL dataset, which contains distorted configurations of small molecules during collisions. Finally, we investigate ensembling and mean-variance estimation for uncertainty quantification with the goal of accelerating the exploration of the vast space of non-equilibrium structures. Our DimeNet++ implementation as well as the COLL dataset are available online. Benchmark (surveying) ; Computer science ; Scope (computer science) ; Variance (accounting) ; Molecule ; Work (physics) ; Chemistry ; Physics ; Thermodynamics ; Economics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yaping Wen | Lulu Fu | Gongqiang Li | Jing Ma | Haibo Ma
Source: Solar RRL (article, 2020-04-14)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Potential Organic Dyes for Dye‐Sensitized Solar Cells by Interpretable Machine Learning Models and Virtual Screening The development of highly efficient dye‐sensitized solar cells (DSSCs) is greatly hindered by the lack of a reliable and understandable quantitative structure–property relationship (QSPR) model. Herein, an accurate, robust, and interpretable QSPR model is established by combining the machine learning technique and computational quantum chemistry, and with this model, virtual screening as well as the assessment of synthetic accessibility is performed to identify new efficient and synthetically accessible organic dyes for DSSCs. Finally, eight promising organic dyes with high power conversion efficiency and synthetic accessibility are screened out from ≈10 000 candidates. Meanwhile, the interpretability of the model is used for deducing reasonable chemical rules for high‐performance organic dyes, which are expected to contribute to further innovations for the practical applications of DSSCs. Interpretability ; Quantitative structure–activity relationship ; Virtual screening ; Computer science ; Quantum chemical ; Artificial intelligence ; Dye-sensitized solar cell ; Machine learning ; Organic dye ; Biochemical engineering ; Organic solar cell ; Photovoltaic system ; Biological system ; Chemistry ; Drug discovery ; Engineering ; Organic chemistry ; Molecule ; Chemical engineering Advanced Nanomaterials in Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation ; TiO2 Photocatalysis and Solar Cells
Authors: Patrizio Graziosi | Chathurangi Kumarasinghe | Neophytos Neophytou
Source: ACS Applied Energy Materials (article, 2020-05-22)
Field: Materials Science — Materials Chemistry

Material Descriptors for the Discovery of Efficient Thermoelectrics The predictive performance screening of novel compounds can significantly promote the discovery of efficient, cheap, and nontoxic thermoelectric (TE) materials. Large efforts to implement machine-learning techniques coupled to materials databases are currently being undertaken, but the adopted computational methods can dramatically affect the outcome. With regards to electronic transport and power factor (PF) calculations, the most widely adopted and computationally efficient method is the constant relaxation time approximation (CRT). This work goes beyond the CRT and adopts the proper, full energy and momentum dependencies of electron–phonon and ionized impurity scattering to compute the electronic transport and perform PF optimization for a group of half-Heusler alloys. Then, the material parameters that determine the optimal PF based on this more advanced treatment are identified. This enables the development of a set of significantly improved descriptors that can be used in material screening studies, which offer deeper insights into the underlying nature of high-performance TE materials. We have identified nvεr/Do2mcond as the most useful and generic descriptor, a combination of the number of valleys, the dielectric constant, the conductivity effective mass, and the deformation potential for the dominant electron–phonon process. The proposed descriptors can accelerate the discovery of new efficient and environment-friendly TE materials in a much more accurate and reliable manner, and some predictions for very high-performance materials are presented.&#13;\n&#13;\n Thermoelectric materials ; Work (physics) ; Power (physics) ; Dielectric ; Thermoelectric effect ; Relaxation (psychology) ; Materials informatics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties
Authors: Zimo Wang | Faissal Chegdani | Neehar Yalamarti | Behrouz Takabi | Bruce L. Tai | Mohamed El Mansori | Satish Bukkapatnam
Source: Journal of Manufacturing Science and Engineering (article, 2020-01-11)
Field: Materials Science — Polymers and Plastics

Acoustic Emission Characterization of Natural Fiber Reinforced Plastic Composite Machining Using a Random Forest Machine Learning Model Abstract Natural fiber reinforced plastic (NFRP) composites are eliciting an increased interest across industrial sectors, as they combine a high degree of biodegradability and recyclability with unique structural properties. These materials are machined to create components that meet the dimensional and surface finish tolerance specifications for various industrial applications. The heterogeneous structure of these materials—resulting from different fiber orientations and their complex multiscale structure—introduces a distinct set of material removal mechanisms that inherently vary over time. This structure has an adverse effect on the surface integrity of machined NFRPs. Therefore, a real-time monitoring approach is desirable for timely intervention for quality assurance. Acoustic emission (AE) sensors that capture the elastic waves generated from the plastic deformation and fracture mechanisms have potential to characterize these abrupt variations in the material removal mechanisms. However, the relationship connecting AE waveform patterns with these NFRP material removal mechanisms is not currently understood. This paper reports an experimental investigation into how the time–frequency patterns of AE signals connote the various cutting mechanisms under different cutting speeds and fiber orientations. Extensive orthogonal cutting experiments on unidirectional flax fiber NFRP samples with various fiber orientations were conducted. The experimental setup was instrumented with a multisensor data acquisition system for synchronous collection of AE and vibration signals during NFRP cutting. A random forest machine learning approach was employed to quantitatively relate the AE energy over specific frequency bands to machining conditions and hence the process microdynamics, specifically, the phenomena of fiber fracture and debonding that are peculiar to NFRP machining. Results from this experimental study suggest that the AE energy over these frequency bands can correctly predict the cutting conditions to ∼95% accuracies, as well as the underlying material removal regimes. Acoustic emission ; Machining ; Materials science ; Fiber ; Waveform ; Acoustics ; Fracture (geology) ; Process (computing) ; Computer science ; Structural engineering ; Composite material ; Engineering Natural Fiber Reinforced Composites ; Advanced machining processes and optimization ; Advanced Machining and Optimization Techniques
Authors: Jenke Scheen | Wilson Wu | Antonia S. J. S. Mey | Paolo Tosco | Mark Mackey | Julien Michel
Source: Journal of Chemical Information and Modeling (article, 2020-07-08)
Field: Materials Science — Materials Chemistry

Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations and to flag molecules which will benefit the most from bespoke force field parametrization efforts. Bespoke ; Parametrization (atmospheric modeling) ; Computer science ; Range (aeronautics) ; Computation ; Set (abstract data type) ; Fraction (chemistry) ; Field (mathematics) ; Energy (signal processing) ; Artificial intelligence ; Machine learning ; Algorithm ; Chemistry ; Materials science ; Mathematics ; Physics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Qidong Lin | Yaolong Zhang | Bin Zhao | Bin Jiang
Source: The Journal of Chemical Physics (article, 2020-04-17)
Field: Materials Science — Materials Chemistry

Automatically growing global reactive neural network potential energy surfaces: A trajectory-free active learning strategy An efficient and trajectory-free active learning method is proposed to automatically sample data points for constructing globally accurate reactive potential energy surfaces (PESs) using neural networks (NNs). Although NNs do not provide the predictive variance as the Gaussian process regression does, we can alternatively minimize the negative of the squared difference surface (NSDS) given by two different NN models to actively locate the point where the PES is least confident. A batch of points in the minima of this NSDS can be iteratively added into the training set to improve the PES. The configuration space is gradually and globally covered without the need to run classical trajectory (or equivalently molecular dynamics) simulations. Through refitting the available analytical PESs of H<sub>3</sub> and OH<sub>3</sub> reactive systems, we demonstrate the efficiency and robustness of this new strategy, which enables fast convergence of the reactive PESs with respect to the number of points in terms of quantum scattering probabilities. Maxima and minima ; Artificial neural network ; Robustness (evolution) ; Gaussian process ; Convergence (economics) ; Trajectory ; AC power ; Energy (signal processing) ; Set (abstract data type) ; Data point Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Chemical Physics Studies
Authors: Hojoon Ryu | Kai Xu | Dawei Li | Xia Hong | Wenjuan Zhu
Source: Applied Physics Letters (article, 2020-08-24)
Field: Materials Science — Materials Chemistry

Empowering 2D nanoelectronics via ferroelectricity Introducing ferroelectricity to two-dimensional van der Waals (vdW) materials such as graphene, transition metal dichalcogenides, and black phosphorous presents a promising route for developing high-speed and low-power nanoelectronics. This Perspective reviews two actively pursued materials strategies, ferroelectric/vdW heterostructures and vdW ferroelectric materials. The topics discussed include their application potential and performance limitations as memory, logic, sensing, and optical devices, as well as the challenges and outlook of the field. Nanoelectronics ; Ferroelectricity ; Nanotechnology ; Materials science ; Graphene ; Heterojunction ; Engineering physics ; Optoelectronics ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Mohammad Ali Nasseri | Hamideh Keshtkar | Milad Kazemnejadi | Ali Allahresani
Source: SN Applied Sciences (article, 2020-03-16)
Field: Materials Science — Materials Chemistry

Phytochemical properties and antioxidant activity of Echinops persicus plant extract: green synthesis of carbon quantum dots from the plant extract Phytochemical ; Flavonoid ; DPPH ; Chemistry ; Phenols ; Botany ; Carotenoid ; Traditional medicine ; Antioxidant ; Food science ; Biology ; Organic chemistry Carbon and Quantum Dots Applications ; Selenium in Biological Systems ; Quantum Dots Synthesis And Properties
Authors: R.K. Cheng
Source: Crystals (article, 2020-03-19)
Field: Materials Science — Materials Chemistry

Towards an Optimal Sample Delivery Method for Serial Crystallography at XFEL The advent of the X-ray free electron laser (XFEL) in the last decade created the discipline of serial crystallography but also the challenge of how crystal samples are delivered to X-ray. Early sample delivery methods demonstrated the proof-of-concept for serial crystallography and XFEL but were beset with challenges of high sample consumption, jet clogging and low data collection efficiency. The potential of XFEL and serial crystallography as the next frontier of structural solution by X-ray for small and weakly diffracting crystals and provision of ultra-fast time-resolved structural data spawned a huge amount of scientific interest and innovation. To utilize the full potential of XFEL and broaden its applicability to a larger variety of biological samples, researchers are challenged to develop better sample delivery methods. Thus, sample delivery is one of the key areas of research and development in the serial crystallography scientific community. Sample delivery currently falls into three main systems: jet-based methods, fixed-target chips, and drop-on-demand. Huge strides have since been made in reducing sample consumption and improving data collection efficiency, thus enabling the use of XFEL for many biological systems to provide high-resolution, radiation damage-free structural data as well as time-resolved dynamics studies. This review summarizes the current main strategies in sample delivery and their respective pros and cons, as well as some future direction. Sample (material) ; Nanotechnology ; Computer science ; Data collection ; Materials science ; Physics ; Statistics ; Mathematics Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Yoshihiko Ozaki | Yuta Suzuki | Takafumi Hawai | Kotaro Saito | Masaki Onishi | Kanta Ono
Source: npj Computational Materials (article, 2020-06-05)
Field: Materials Science — Materials Chemistry

Automated crystal structure analysis based on blackbox optimisation Abstract In the present study, we show that time-consuming manual tuning of parameters in the Rietveld method, one of the most frequently used crystal structure analysis methods in materials science, can be automated by considering the entire trial-and-error process as a blackbox optimisation problem. The automation is successfully achieved using Bayesian optimisation, which outperforms both a human expert and an expert-system type automation despite the absence of expertise. This approach stabilises the analysis quality by eliminating human-origin variance and bias, and can be applied to various analysis methods in other areas which also suffer from similar tiresome and unsystematic manual tuning of extrinsic parameters and human-origin variance and bias. Automation ; Computer science ; Variance (accounting) ; Process (computing) ; Bayesian probability ; Data mining ; Human error ; Quality (philosophy) ; Artificial intelligence ; Machine learning ; Statistics ; Mathematics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Severin Eder | Katrin Müller | Paride Azzari | Andrea Arcifa | Mohammad Peydayesh | Laura Nyström
Source: Chemical Engineering Journal (article, 2020-08-03)
Field: Chemistry — Organic Chemistry

Mass Transfer Mechanism and Equilibrium Modelling of Hydroxytyrosol Adsorption on Olive Pit–Derived Activated Carbon The rise in olive oil production coincides with an increase in olive oil side-streams, such as olive mill wastewater and olive pits, enabling the exploitation of higher value-added products through proper processes. Adsorption techniques, common in wastewater treatment, facilitate the recovery of valuable compounds found in these by-products, such as the strong natural antioxidant hydroxytyrosol present in olive mill wastewater. Owing to their chemical composition, olive pits are a promising precursor for activated carbon production intended for adsorption purposes. However, a complementary side-stream strategy requires an in-depth analysis of the equilibrium state and the governing mass transfer mechanism based on sophisticated mathematical models for a reliable investigation of the processes occurring during hydroxytyrosol adsorption on olive pit–derived activated carbon (OPAC). Here we show the technological suitability of OPAC for adsorption of hydroxytyrosol in comparison to an existing commercial activated carbon (CAC). We found that the removal efficiency of OPAC was superior to CAC with increasing initial concentrations of hydroxytyrosol. The Redlich-Peterson isotherm provided the best fit to the adsorption equilibrium data based on the Akaike Information Criterion (AIC). Elucidation of the adsorption kinetics by means of the pore volume and surface diffusion model (PVSDM) showed that intraparticle diffusion resistance dominated hydroxytyrosol adsorption on OPAC. In addition, investigations over a broad temperature and pH range revealed the versatile applicability of OPAC and indicated a physisorption governed interaction. Our results demonstrate the fundamental parameters associated with hydroxytyrosol adsorption on OPAC, crucial for the development of adsorption systems to convert by-products into opportunities. Hydroxytyrosol ; Adsorption ; Activated carbon ; Chemistry ; Wastewater ; Physisorption ; Mass transfer ; Chemical engineering ; Pulp and paper industry ; Organic chemistry ; Chromatography ; Environmental engineering ; Antioxidant ; Environmental science Edible Oils Quality and Analysis ; Dye analysis and toxicity ; Adsorption and biosorption for pollutant removal