jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhiwen Pan | Jan Dellith | Lothar Wondraczek
Source: ChemRxiv (preprint, 2023-03-05)
Field: Materials Science — Materials Chemistry

Genome mining in glass chemistry using linear component analysis of ion conductivity data Understanding the multivariate origin of physical properties is particularly complex for polyionic glasses. As a concept, the term genome has been used to describe the entirety of structure-property relations in solid materials, based on functional genes acting as descriptors for a particular property, for example, for input in regression analysis or other machine-learning tools. Here, we present the genes of ionic conductivity in polyionic sodium-conducting glasses as fictive chemical entities with a characteristic stoichiometry, derived from strong linear component analysis (SLCA) of a uniquely consistent dataset. SLCA is based on a twofold optimization problem that maximizes the quality of linear regression between a property (here: ionic conductivity) and champion candidates from all possible combinations of elements. Family trees and matrix rotation analysis are subsequently used to filter for essential elemental combinations, and from their characteristic mean composition, the essential genes. These genes reveal the intrinsic relationships within the multivariate input data. While they do not require a structural representation in real space, we finally demonstrate how possible structural interpretations agree with intuitive understanding of structural entities known from spectroscopic experiments. Multivariate statistics ; Compositional data ; Linear regression ; Property (philosophy) ; Component (thermodynamics) ; Conductivity ; Computer science ; Chemistry ; Physics ; Machine learning ; Thermodynamics ; Physical chemistry Machine Learning in Materials Science
Authors: Shampa Raghunathan | Sai Ajay Kashyap Nakirikanti
Source: Machine Learning Science and Technology (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Intramolecular proton transfer reaction dynamics using machine-learned ab initio potential energy surfaces Abstract Hydrogen bonding interactions, which are central to various physicochemical processes, are investigated in the present study using ab initio -based machine learning potential energy surfaces. Abnormally strong intramolecular O–H⋯O hydrogen bonds, occurring in β -diketone enols of malonaldehyde and its derivatives, with substituents ranging from various electron-withdrawing to electron-donating functional groups, are studied. Machine learning force fields were constructed using a kernel-based force learning model employing ab initio molecular dynamics reference data. These models were used for molecular dynamics simulations at finite temperature, and dynamical properties were determined by computing proton transfer free-energy surfaces. The chemical systems studied here show progression toward barrier-less proton transfer events at an accuracy of correlated electronic structure methods. Markov state models of the conformational states indicate shorter intramolecular hydrogen bonds exhibiting higher proton transfer rates. We demonstrate how functional group substitution can modulate the strength of intramolecular hydrogen bonds by studying the thermodynamic and kinetic properties. Intramolecular force ; Ab initio ; Hydrogen bond ; Chemistry ; Proton ; Molecular dynamics ; Computational chemistry ; Electron transfer ; Ab initio quantum chemistry methods ; Density functional theory ; Proton-coupled electron transfer ; Chemical physics ; Molecule ; Physical chemistry ; Stereochemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Emma Palm | Parviel Chirsir | Jessy Krier | Paul Thiessen | Jian Zhang | Evan Bolton | Emma Schymanski
Source: ChemRxiv (preprint, 2023-09-06)
Field: Chemistry — Physical and Theoretical Chemistry

ShinyTPs: Curating transformation products from text mining results Transformation product (TP) information is essential to accurately evaluate the hazards compounds pose to human health and the environment. However, information about TPs is often limited, and existing data is often not fully Findable, Accessible, Interoperable and Reusable (FAIR). FAIRifying existing TP knowledge is a relatively easy path towards improving access to data for identification workflows and for machine learning-based algorithms. ShinyTPs was developed to curate existing transformation information derived from text-mined data within the PubChem database. The application (available as an R package) visualizes the text-mined chemical names to facilitate user validation of the automatically extracted reactions. ShinyTPs was applied to a case study using 436 tentatively identified compounds to prioritize TP retrieval. This resulted in the extraction of 645 reactions (associated with 496 compounds), of which 319 reactions were not previously available in PubChem. The curated reactions were added to the PubChem Transformations library, which was used as a TP suspect list for identification of TPs using the open-source workflow patRoon. In total 72 compounds from the library were tentatively identified, 18% of which were curated using ShinyTPs, showing that the app can help support TP identification in non-target analysis workflows. PubChem ; Workflow ; Computer science ; Identification (biology) ; Transformation (genetics) ; Information retrieval ; Information extraction ; Data mining ; World Wide Web ; Database ; Computational biology ; Chemistry History and advancements in chemistry
Authors: Shuai Zhang | Wenjie Du | Di Wu | Jiahe Li | Yang Wang
Source:  (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Improving efficiency in rationale discovery for Out-of-Distribution molecular representations Graph Neural Networks (GNNs), as the dominant approach in Molecular Representation Learning (MRL), have exhibited remarkable efficacy in diverse tasks, including molecular property prediction and drug discovery. Considering the dynamic and diverse nature of test molecules in real-world contexts, the previous independently and identically distributed (i.i.d.) assumption for training and test molecules is not align with the requirements of practical applications in chemistry. Rationalization has been proposed to enhance the Out-of-Distribution (OOD) generalization of GNN, but a critical concern remains unexplored: low efficiency in rationale discovery. We consider this bottleneck is due to large search space and overly flexible modeling. Here, we introduce a framework called Molecule Stratifier and Invariant Rational Improver (MSIRI) to overcome the challenge. Specifically, MSIRI adopt vector quantization to obtain invariant rationales and the search space is narrowed by substructure and two assumptions based on subgraph matching. Experimental results on ten datasets demonstrate that our approach both achieves significant improvements across various GNN backbones and outperforms other six Out-of-Distribution method, clearly demonstrating its effectiveness in addressing the OOD challenges in MRL. Computer science ; Bottleneck ; Theoretical computer science ; Chemical space ; Machine learning ; Artificial intelligence ; Data mining ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ronald Machaka | Precious M. Radingoana
Source: Data in Brief (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

Datasets of narrow thermal hysteresis behaviour Ti-Ni-based HT-SMAs and the predicted accumulated local effects This article refers to data derived from a research article entitled "Prediction of narrow HT-SMA thermal hysteresis behaviour using explainable machine learning" [1]. It is based on the knowledge that alloying Ti-Ni-based shape memory alloys (SMAs) with additional ternary or multicomponent elements can alter the SMAs' characteristic transformation temperatures, including the thermal hystereses. Two datasets are reported. The first and primary dataset documents experimental Ti-Ni-based shape memory alloys' high-transformation temperature characteristics reported in the literature. The second auxiliary dataset presented in this article was obtained following the explainable prediction of the narrow high-temperature thermal hysteresis behaviour in Ti-Ni-based high-transformation temperature SMAs (HT-SMAs). The second dataset is intended to generalise and summarise the ML prediction and visualisation of the thermal hysteresis behaviour as also observed experimentally in multiple reports elsewhere. The datasets are provided as supplementary files and the second dataset is also visualised as an intuitive marginal effects plot. We believe that these data will find applications in advancing experimental and theoretical HT-SMA research. Shape-memory alloy ; Hysteresis ; Ternary operation ; Transformation (genetics) ; Thermal ; Materials science ; Computer science ; Plot (graphics) ; Thermal hysteresis ; SMA* ; Artificial intelligence ; Thermodynamics ; Physics ; Mathematics ; Chemistry ; Algorithm ; Phase transition ; Condensed matter physics ; Statistics Shape Memory Alloy Transformations ; High Entropy Alloys Studies
Authors: Lei Chen | Andreas Erlebach | Federico Brivio | Lukáš Grajciar | Zdeněk Tošner | Christopher J. Heard | Petr Nachtigall
Source: ChemRxiv (preprint, 2023-03-09)
Field: Chemistry — Spectroscopy

The need for operando modelling of 27Al NMR in zeolites Solid state (ss-) 27Al NMR is one of the most valuable tools for experimental characterization of zeolites, owing to its high sensitivity and the detailed structural information which can be extracted from the spectra. Unfortunately, the interpretation of ss-NMR is complex, and the determination of aluminum distributions remains generally unfeasible. As a result, computational modelling of 27Al ss-NMR spectra has grown increasingly popular as a means to support experimental characterization. However, a number of simplifying assumptions are commonly made in NMR modelling, several of which are not fully justified. In this work, we systematically evaluate the effects of various common models on the prediction of 27Al NMR chemical shifts in zeolites CHA and MOR. We demonstrate the necessity of operando modelling; in particular, taking into account the effects of water loading, temperature and the character of the charge-compensating cation. We observe that conclusions drawn from simple, high symmetry model systems such as CHA do not transfer well to more complex zeolites and can lead to qualitatively wrong interpretations of peak positions, Al assignment and even the number of signals. We use machine learning regression to develop a simple yet robust relationship between chemical shift and local structural parameters in Al-zeolites. This work highlights the need for sophisticated models and high-quality sampling in the field of NMR modelling and provides correlations which allow for the accurate prediction of chemical shifts from dynamical simulations. Characterization (materials science) ; Chemical shift ; Interpretation (philosophy) ; NMR spectra database ; Work (physics) ; Spectral line ; Simple (philosophy) ; Field (mathematics) ; Chemistry ; Biological system ; Computer science ; Statistical physics ; Materials science ; Physical chemistry ; Thermodynamics ; Physics ; Mathematics ; Nanotechnology Advanced NMR Techniques and Applications ; Zeolite Catalysis and Synthesis ; NMR spectroscopy and applications
Authors: Marcel F. Langer | Florian Knoop | Christian Carbogno | Matthias Scheffler | Matthias Rupp
Source: arXiv (Cornell University) (preprint, 2023-03-25)
Field: Materials Science — Materials Chemistry

Heat flux for semi-local machine-learning potentials The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials. However, it requires an accurate description of the potential-energy surface and carefully converged statistics. Machine-learning potentials can achieve the accuracy of first-principles simulations while allowing to reach well beyond their simulation time and length scales at a fraction of the cost. In this paper, we explain how to apply the GK approach to the recent class of message-passing machine-learning potentials, which iteratively consider semi-local interactions beyond the initial interaction cutoff. We derive an adapted heat flux formulation that can be implemented using automatic differentiation without compromising computational efficiency. The approach is demonstrated and validated by calculating the thermal conductivity of zirconium dioxide across temperatures. Computer science ; Heat flux ; Thermal conductivity ; Flux (metallurgy) ; Class (philosophy) ; Thermal ; Fraction (chemistry) ; Energy (signal processing) ; Cutoff ; Mathematical optimization ; Machine learning ; Artificial intelligence ; Heat transfer ; Mathematics ; Mechanics ; Physics ; Materials science ; Thermodynamics ; Chemistry ; Statistics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Nuclear reactor physics and engineering
Authors: Taoyuze Lv | Zhicheng Zhong | Yuhang Liang | Feng Li | Jun Huang | Rongkun Zheng
Source: arXiv (Cornell University) (preprint, 2023-09-26)
Field: Materials Science — Materials Chemistry

Deep Charge: A Deep Learning Model of Electron Density from One-Shot Density Functional Theory Calculation Electron charge density is a fundamental physical quantity, determining various properties of matter. In this study, we have proposed a deep-learning model for accurate charge density prediction. Our model naturally preserves physical symmetries and can be effectively trained from one-shot density functional theory calculation toward high accuracy. It captures detailed atomic environment information, ensuring accurate predictions of charge density across bulk, surface, molecules, and amorphous structures. This implementation exhibits excellent scalability and provides efficient analyses of material properties in large-scale condensed matter systems. Density functional theory ; Scalability ; Charge (physics) ; Charge density ; Electron ; Statistical physics ; Electron density ; Physics ; Warm dense matter ; Computer science ; Computational physics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Joel M. Bowman | Qi Yu | Chen Qu | Paul L. Houston | Riccardo Conte | Apurba Nandi
Source: ChemRxiv (preprint, 2023-03-28)
Field: Materials Science — Materials Chemistry

Interfacing q-AQUA with a Polarizable ForceField: The Best of Both Worlds Polarizable force fields are pervasive in the fields of computational chemistry and biochemistry; however, their empirical or semi-empirical nature gives them both weaknesses and strengths. Here, we have developed a hybrid water potential, named q-AQUA-pol, by combining our recent ab initio q-AQUA potential with the TTM3-F water potential. The new potential demonstrates unprecedented accuracy ranging from gas-phase clusters, e.g., the eight low-lying isomers of the water hexamer, to the condensed phase, e.g., radial distribution functions, the self-diffusion coefficient, triplet OOO distribution, and the temperature dependence of the density. This represents a significant advancement in the field of polarizable machine learning potential and computational modeling. Polarizability ; Interfacing ; Random hexamer ; Force field (fiction) ; Chemical physics ; Ab initio ; Field (mathematics) ; Chemistry ; Computational chemistry ; Physics ; Computer science ; Molecule ; Quantum mechanics ; Crystallography ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced NMR Techniques and Applications
Authors: Diomande Siaho | Pandry Koffi Ghislain | Kadjo Tanon Lambert | Kakou Kouassi Ernest | Souleymane Oumtanaga | Assidjo Nogbou Emmanuel
Source: Ingénierie des systèmes d information (article, 2023-04-30)
Field: Chemistry — Analytical Chemistry

Modeling Artificial Neural Network of Insect’s Proliferation During Cocoa Beans Storage The cocoa bean is a grain which is the raw material for the economy of Côte d'Ivoire.Thus, throughout the bean value chain, particular attention is paid to quality.In this chain, storage remains an imported step.Indeed, insects are one of the pests causing enormous damage and losses in the conservation of stored grains.These insects are also present during the storage of cocoa beans.The proliferation of insects is due to several physico-chemical and environmental factors such as water content (Te), sugar content (TSu) and temperature (T°) which interact in the bean ecosystem.This proliferation remains difficult to control and estimate.In this work, we invented a method based on neural networks to determine the evolution of insect density.Indeed, an Insect Dynamics Model (MDI) has been established.To validate the effectiveness of the proposed method, we have chosen as performance criteria the coefficient of determination R² =0.9982.This shows a good correlation of the experimental values and those predicted.This result was obtained, with an optimal 4-5-1 architecture selected by Akaike's information criterion. Insect ; Artificial neural network ; Biology ; Computer science ; Horticulture ; Biological system ; Artificial intelligence ; Botany Spectroscopy and Chemometric Analyses
Authors: Robin Hilgers | Daniel Wortmann | Stefan Blügel
Source: arXiv (Cornell University) (preprint, 2023-11-26)
Field: Materials Science — Materials Chemistry

Application of batch learning for boosting high-throughput ab initio success rates and reducing computational effort required using data-driven processes The increased availability of computing time, in recent years, allows for systematic high-throughput studies of material classes with the purpose of both screening for materials with remarkable properties and understanding how structural configuration and material composition affect macroscopic attributes manifestation. However, when conducting systematic high-throughput studies, the individual ab initio calculations' success depends on the quality of the chosen input quantities. On a large scale, improving input parameters by trial and error is neither efficient nor systematic. We present a systematic, high-throughput compatible, and machine learning-based approach to improve the input parameters optimized during a DFT computation or workflow. This approach of integrating machine learning into a typical high-throughput workflow demonstrates the advantages and necessary considerations for a systematic study of magnetic multilayers of 3$d$ transition metal layers on FCC noble metal substrates. For 6660 film systems, we were able to improve the overall success rate of our high-throughput FLAPW-based structural relaxations from $64.8 \%$ to $94.3\ \%$ while at the same time requiring $17\ \%$ less computational time for each successful relaxation. Throughput ; Workflow ; Computer science ; Computation ; Boosting (machine learning) ; Ab initio ; Relaxation (psychology) ; Computational science ; Machine learning ; Artificial intelligence ; Materials science ; Chemistry ; Algorithm ; Database Machine Learning in Materials Science ; Theoretical and Computational Physics ; Ferroelectric and Negative Capacitance Devices
Authors: Sanghoon Chin | Jérôme Van Zaen | Séverine Denis | Enric Muntané | Stephan Schröder | H. Martin | Laurent Balet | Steve Lecomte
Source: Sensors (article, 2023-10-03)
Field: Chemistry — Spectroscopy

An Artificial Neural Network to Eliminate the Detrimental Spectral Shift on Mid-Infrared Gas Spectroscopy We demonstrate the successful implementation of an artificial neural network (ANN) to eliminate detrimental spectral shifts imposed in the measurement of laser absorption spectrometers (LASs). Since LASs rely on the analysis of the spectral characteristics of biological and chemical molecules, their accuracy and precision is especially prone to the presence of unwanted spectral shift in the measured molecular absorption spectrum over the reference spectrum. In this paper, an ANN was applied to a scanning grating-based mid-infrared trace gas sensing system, which suffers from temperature-induced spectral shifts. Using the HITRAN database, we generated synthetic gas absorbance spectra with random spectral shifts for training and validation. The ANN was trained with these synthetic spectra to identify the occurrence of spectral shifts. Our experimental verification unambiguously proves that such an ANN can be an excellent tool to accurately retrieve the gas concentration from imprecise or distorted spectra of gas absorption. Due to the global shift of the measured gas absorption spectrum, the accuracy of the retrieved gas concentration using a typical least-mean-squares fitting algorithm was considerably degraded by 40.3%. However, when the gas concentration of the same measurement dataset was predicted by the proposed multilayer perceptron network, the sensing accuracy significantly improved by reducing the error to less than ±1% while preserving the sensing sensitivity. HITRAN ; Spectral line ; Multilayer perceptron ; Spectroscopy ; Absorbance ; Artificial neural network ; Absorption spectroscopy ; Absorption (acoustics) ; Trace gas ; Spectrometer ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Biological system ; Computer science ; Optics ; Artificial intelligence ; Physics Spectroscopy and Laser Applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Nilin Abrahamsen | Lin Lin
Source: arXiv (Cornell University) (preprint, 2023-03-22)
Field: Materials Science — Materials Chemistry

Anti-symmetric Barron functions and their approximation with sums of determinants A fundamental problem in quantum physics is to encode functions that are completely anti-symmetric under permutations of identical particles. The Barron space consists of high-dimensional functions that can be parameterized by infinite neural networks with one hidden layer. By explicitly encoding the anti-symmetric structure, we prove that the anti-symmetric functions which belong to the Barron space can be efficiently approximated with sums of determinants. This yields a factorial improvement in complexity compared to the standard representation in the Barron space and provides a theoretical explanation for the effectiveness of determinant-based architectures in ab-initio quantum chemistry. Parameterized complexity ; Representation (politics) ; Factorial ; Space (punctuation) ; ENCODE ; Symmetric function ; Encoding (memory) ; Symmetric space ; Representation theory ; Mathematics ; Quantum ; Pure mathematics ; Artificial neural network ; Algebra over a field ; Computer science ; Combinatorics ; Physics ; Quantum mechanics ; Mathematical analysis ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Neural Networks and Applications