jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mostafa Abedi | Jörg Behler | C. Franklin Goldsmith
Source: ChemRxiv (preprint, 2023-05-17)
Field: Materials Science — Materials Chemistry

High-Dimensional Neural Network Potentials for Accurate Prediction of Equation of State: A Case Study of Methane Machine learning-based interatomic potentials, such as those provided by neural networks, are increasingly important in molecular dynamics simulations. In the present work, we consider the applicability and robustness of machine learning molecular dynamics to predict the equation of state properties of methane using high-dimensional neural network potentials (HDNNPs). We investigate two different strategies for generating training data: one based upon bulk representations using periodic cells, and another based upon clusters of molecules. We assess the accuracy of the trained potentials by predicting the equilibrium mass density for a wide range of thermodynamic conditions to characterize the liquid phase, supercritical fluid and gas phase, as well as the liquid-vapor coexistence curve. Our results show an excellent agreement with reference phase diagrams, with an average error below ∼ 2% for all studied phases. Moreover, we confirm the applicability of models trained on cluster data set for producing accurate and reliable results. Artificial neural network ; Equation of state ; Statistical physics ; Supercritical fluid ; Robustness (evolution) ; Computer science ; Molecular dynamics ; Work (physics) ; Methane ; Thermodynamics ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Catalysis and Oxidation Reactions
Authors: Dibakar Datta | Eon Soo Lee
Source: arXiv (Cornell University) (preprint, 2023-09-05)
Field: Materials Science — Materials Chemistry

Exploring Thermal Transport in Electrochemical Energy Storage Systems Utilizing Two-Dimensional Materials: Prospects and Hurdles Two-dimensional materials and their heterostructures have enormous applications in Electrochemical Energy Storage Systems (EESS) such as batteries. A comprehensive and solid understanding of these materials' thermal transport and mechanism is essential for the practical design of EESS. Experiments have challenges in providing improved control and characterization of complex structures, especially for low dimensional materials. Theoretical and simulation tools such as first-principles calculations, boltzmann transport equations, molecular dynamics simulations, lattice dynamics simulation, and non-equilibrium Green's function provide reliable predictions of thermal conductivity and physical insights to understand the underlying thermal transport mechanism in materials. However, doing these calculations require high computational resources. The development of new materials synthesis technology and fast-growing demand for rapid and accurate prediction of physical properties require novel computational approaches. The machine learning (ML) method provides a promising solution to address such needs. This review details the recent development in atomistic/molecular studies and ML of thermal transport in EESS. The paper also addresses the latest significant experimental advances. However, designing the best low-dimensional materials-based heterostructures is like a multivariate optimization problem. For example, a particular heterostructure may be suitable for thermal transport but can have lower mechanical strength/stability. For bi/multilayer structures, the interlayer distance may influence the thermal transport properties and interlayer strength. Therefore, the last part addresses the future research direction in low-dimensional materials-based heterostructure design for thermal transport in EESS. Heterojunction ; Computer science ; Materials science ; Thermal ; Thermal conductivity ; Electrochemical energy storage ; Nanotechnology ; Thermal energy storage ; Molecular dynamics ; Lattice Boltzmann methods ; Boltzmann equation ; Mechanical engineering ; Electrochemistry ; Chemistry ; Physics ; Engineering ; Thermodynamics ; Computational chemistry ; Supercapacitor ; Physical chemistry Thermal properties of materials ; Fuel Cells and Related Materials ; Advanced Thermoelectric Materials and Devices
Authors: Bruce Dunham | E.R. Scott
Source:  (article, 2023-04-13)
Field: Materials Science — Surfaces, Coatings and Films

Methods to Discover New Photocathode Materials using Machine Learning and Data-Driven Screening This poster poster is accepted to be presented at the upcoming International Particle Accelerator Conference (www.ipac23.org) Photocathode ; Brightness ; Optoelectronics ; Materials science ; Electron ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Romain Lacombe | Lucas Hendren | Khalid El-Awady
Source: arXiv (Cornell University) (preprint, 2023-12-04)
Field: Materials Science — Materials Chemistry

AdsorbRL: Deep Multi-Objective Reinforcement Learning for Inverse Catalysts Design A central challenge of the clean energy transition is the development of catalysts for low-emissions technologies. Recent advances in Machine Learning for quantum chemistry drastically accelerate the computation of catalytic activity descriptors such as adsorption energies. Here we introduce AdsorbRL, a Deep Reinforcement Learning agent aiming to identify potential catalysts given a multi-objective binding energy target, trained using offline learning on the Open Catalyst 2020 and Materials Project data sets. We experiment with Deep Q-Network agents to traverse the space of all ~160,000 possible unary, binary and ternary compounds of 55 chemical elements, with very sparse rewards based on adsorption energy known for only between 2,000 and 3,000 catalysts per adsorbate. To constrain the actions space, we introduce Random Edge Traversal and train a single-objective DQN agent on the known states subgraph, which we find strengthens target binding energy by an average of 4.1 eV. We extend this approach to multi-objective, goal-conditioned learning, and train a DQN agent to identify materials with the highest (respectively lowest) adsorption energies for multiple simultaneous target adsorbates. We experiment with Objective Sub-Sampling, a novel training scheme aimed at encouraging exploration in the multi-objective setup, and demonstrate simultaneous adsorption energy improvement across all target adsorbates, by an average of 0.8 eV. Overall, our results suggest strong potential for Deep Reinforcement Learning applied to the inverse catalysts design problem. Reinforcement learning ; Ternary operation ; Traverse ; Adsorption ; Unary operation ; Catalysis ; Binary number ; Computer science ; Inverse ; Energy (signal processing) ; Artificial intelligence ; Chemistry ; Mathematics ; Physics ; Quantum mechanics ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling
Authors: Veerupaksh Singla | Qiyuan Zhao | Brett M. Savoie
Source: ChemRxiv (preprint, 2023-11-06)
Field: Materials Science — Materials Chemistry

Learning Stability Scores from Computationally Generated Kinetic Data The absence of computational methods to predict stressor-specific degradation susceptibilities represents a significant and costly challenge to the introduction of new materials into applications. Here, a machine-learning framework is developed that predicts stressor-specific stability scores from computationally generated reaction data. Thermal degradation of alkanes was studied as an exemplary system to demonstrate the approach. The half-lives of ~32k alkanes were simulated under pyrolysis conditions using 59 model reactions. Using a hinge-loss function, this half-life data was used to train machine learning models to predict a scalar representing the relative stability based only on the molecular graph. These models were successful in transferability case-studies using distinct train:test splits to recapitulate known stability trends with respect to the degree of branching and alkane size. Even the simplest models showed excellent performance in these case studies, demonstrating the relative ease with which thermal stability can be learned. The stability score is also shown to be useful in a design study, where it is used as part of the objective function of a genetic algorithm to guide the search towards more stable species. This work provides a framework for converting kinetic reaction data into stability scores that provide actionable design information and opens avenues for exploring more complex chemistries and stressors. Stability (learning theory) ; Computer science ; Transferability ; Stressor ; Graph ; Machine learning ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Computational Drug Discovery Methods
Authors: Albert Kireev | Sergey Osipenko | Е. Н. Николаев | Yury Kostyukevich
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-07-10)
Field: Chemistry — Physical and Theoretical Chemistry

In silico structures, mass-spectra and retention indices database development for purposes of Chemical Weapons Convention <strong>Abstract: </strong>The Chemical Weapons Convention (CWC) is a science-based international treaty for the disarmament and non-proliferation of chemical weapons. However, reference OPCW central analytical database (OCAD) of structures with mass-spectra (MS) and retention indices (RI) includes only minor part of all possible chemical species defined in the Schedules of CWC. In this work we employed OCAD <em>in silico</em> augmentation based on chemoinformatics approach for chemical structures enumeration, MS data generation based on message passing neural network and based on P-alkyl molecular pairs RI prediction to support the verification activities as provided for in the CWC. Enumerated 879 noncyclic and 5270 monocyclic alcohols became the basis for generating hundreds of thousands molecules of Schedule 1 toxic chemicals like Sarin, Tabun, VX and Novichok. Trained on ordinary and neutral loss electron ionization MS(EI-MS) a message-passing neural network (MPNN) outperformed other quantum chemistry and machine learning methods. Generated by this MPNN <em>in silico</em> EI-MS are very similar to the library’s spectra and allowed to reach desired match factor above 800 within a scale of 0-1000. Statistical data for molecular pairs based on P-alkyl fragments was collected and used to predict RIs within desired 20 RI window for some toxic chemicals of Schedule 1.A.01, for which in the current OCAD version RIs are absent. In silico ; Mass spectrum ; Convention ; Database ; Computer science ; Chemistry ; Mass spectrometry ; Chromatography ; Political science ; Law History and advancements in chemistry ; Mass Spectrometry Techniques and Applications
Authors: Jun Dai | Roman V. Krems
Source: arXiv (Cornell University) (preprint, 2023-04-11)
Field: Materials Science — Materials Chemistry

Neural network Gaussian processes as efficient models of potential energy surfaces for polyatomic molecules Kernel models of potential energy surfaces (PES) for polyatomic molecules are often restricted by a specific choice of the kernel function. This can be avoided by optimizing the complexity of the kernel function. For regression problems with very expensive data, the functional form of the model kernels can be optimized in the Gaussian process (GP) setting through compositional function search guided by the Bayesian information criterion. However, the compositional kernel search is computationally demanding and relies on greedy strategies, which may yield sub-optimal kernels. An alternative strategy of increasing complexity of GP kernels treats a GP as a Bayesian neural network (NN) with a variable number of hidden layers, which yields NNGP models. Here, we present a direct comparison of GP models with composite kernels and NNGP models for applications aiming at the construction of global PES for polyatomic molecules. We show that NNGP models of PES can be trained much more efficiently and yield better generalization accuracy without relying on any specific form of the kernel function. We illustrate that NNGP models trained by distributions of energy points at low energies produce accurate predictions of PES at high energies. We also illustrate that NNGP models can extrapolate in the input variable space by building the free energy surface of the Heisenberg model trained in the paramagnetic phase and validated in the ferromagnetic phase. By construction, composite kernels yield more accurate models than kernels with a fixed functional form. Therefore, by illustrating that NNGP models outperform GP models with composite kernels, our work suggests that NNGP models should be a preferred choice of kernel models for PES. Gaussian process ; Kernel (algebra) ; Polyatomic ion ; Computer science ; Gaussian ; Artificial neural network ; Function (biology) ; Gaussian function ; Kriging ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Mathematics ; Machine learning ; Applied mathematics ; Computational chemistry ; Physics ; Chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Gowri Sankar S | Mukesh K. Choudhary | Amal Raj V | P. Ravindran
Source: arXiv (Cornell University) (preprint, 2023-09-09)
Field: Materials Science — Materials Chemistry

Accelerating Discovery of Vacancy Ordered 18-Valence Electron Half-Heusler Compounds: A Synergistic Approach of Machine Learning and Density Functional Theory In this study, we attempted to model vacancy ordered half Heusler compounds with 18 valence electron count (VHH) derived from 19 VEC compounds such as TiNiSb such that the compositions will be Ti0.75NiSb, Zr0.75NiSb and Hf0.75NiSb with semiconducting behavior. The main motivation is that such a vacancy-ordered phase not only introduces semi conductivity but also it disrupts the phonon conducting path in HH alloys and thus reduces the thermal conductivity and as a consequence enhances the thermoelectric figure of merit. In order to predict the formation energy (ΔHf) from composition and crystal structure we have used 4684 compounds for their ΔHf values are available in the material project database and trained a machine learning model with R2 value of 0.943. Using this trained model, we have predicted the ΔHf of a list of VHH. From the predicted database of VHH we have selected Zr0.75NiSb and Hf0.75NiSb to validate the machine learning prediction using accurate DFT calculation. The calculated ΔHf for these two compounds from DFT calculation are found to be comparable with our ML prediction. The calculated electronic and lattice dynamics properties show that these materials are narrow band gap semiconductors and are dynamically stable as their all-phonon dispersion curves are having positive frequencies. The calculated Seebeck coefficient, electrical conductivity as well as thermal conductivity, power factor and thermoelectric figure of merit are analyzed. Thermoelectric effect ; Seebeck coefficient ; Condensed matter physics ; Vacancy defect ; Materials science ; Figure of merit ; Valence (chemistry) ; Thermal conductivity ; Thermoelectric materials ; Density functional theory ; Phonon ; Valence electron ; Band gap ; Semiconductor ; Electron ; Thermodynamics ; Computational chemistry ; Chemistry ; Physics ; Optoelectronics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Cristian David Camacho Parra
Source: Universidad Industrial de Santander (article, 2023-03-13)
Field: Materials Science — Materials Chemistry

Parameter Extraction for Photovoltaic Perovskite Synthesis Using Machine Learning Tools Este trabajo de investigación busca desarrollar un método de extracción de información mediante herramientas de minería de datos y aprendizaje automático a partir de artículos científicos relacionados con proceso de síntesis de Perovskitas. El desarrollo de este método se divide en cuatro etapas. La primera consiste en el procesamiento de oraciones que contienen información de artículos científicos pertinentes con el tema. En la segunda etapa, se realizan los procesos de etiquetado y clasificación de la información enfocada a la síntesis de Perovskitas para seguidamente en una tercera etapa refinar y enriquecer la información de parámetros de síntesis relevantes en los procesos de fabricación de las celdas de Perovskita. En la cuarta etapa la información finalmente se almacena en una base de datos que tenga la información de los parámetros más relevantes en la síntesis de películas delgadas de Perovskitas fotovoltaicas. Photovoltaic system ; Work (physics) ; Perovskite (structure) ; Extraction (chemistry) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chemical and Physical Properties of Materials
Authors: Guanjian Cheng | Xin-Gao Gong | Wan‐Jian Yin
Source: arXiv (Cornell University) (preprint, 2023-02-27)
Field: Materials Science — Materials Chemistry

Global optimization in the discrete and variable-dimension conformational space: The case of crystal with the strongest atomic cohesion We introduce a computational method to optimize target physical properties in the full configuration space regarding atomic composition, chemical stoichiometry, and crystal structure. The approach combines the universal potential of the crystal graph neural network and Bayesian optimization. The proposed approach effectively obtains the crystal structure with the strongest atomic cohesion from all possible crystals. Several new crystals with high atomic cohesion are identified and confirmed by density functional theory for thermodynamic and dynamic stability. Our method introduces a novel approach to inverse materials design with additional functional properties for practical applications. Cohesion (chemistry) ; Crystal (programming language) ; Inverse ; Density functional theory ; Stoichiometry ; Crystal structure ; Stability (learning theory) ; Statistical physics ; Computer science ; Mathematics ; Materials science ; Physics ; Computational chemistry ; Chemistry ; Crystallography ; Quantum mechanics ; Physical chemistry ; Geometry ; Machine learning Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; X-ray Diffraction in Crystallography
Authors: Thomas A. Teklemariam | Faith Chou | P. Kumaravel | Jeremy Van Buskrik
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Atr-Ftir Spectroscopy and Machine/Deep Learning Models for Detecting Substitutions in Coconut Water with Sugars, Sugar Alcohols, and Artificial Sweeteners Sugar ; Fourier transform infrared spectroscopy ; Artificial Sweetener ; Spectroscopy ; Chemistry ; Food science ; Organic chemistry ; Chemical engineering ; Engineering ; Physics Coconut Research and Applications ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Zhenyu Wu | Zhenyuan Zhang | Zhiyang Ding | Joel J. P. C. Rodrigues
Source:  (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

IMG2InChI: Extracting Molecular Big Data from Chemical Images Using Transformer Models Machine learning methods are extensively used to develop new drugs and materials, and molecular big data plays a vital role in this process. A large amount of chemical molecules in documents should be well utilized for the construction of molecular big data. Although optical character recognition technology has been widely applied to extract molecular data from scanned images, the recognition accuracy should be improved because of the complexity and sparsity of molecular structure and the fuzziness of scanned molecular images. In this paper, a novel Transformer-based model is used to automatically extract molecular features from images. Furthermore, the extracted molecular features are translated into InChI descriptors by another Transformer model. The experimental results suggest that the proposed method outperforms other deep learning based methods including ResNet and LSTM (Long Short Term Memory). Moreover, the data extraction process is visualized so that the interpretability of the model could be guaranteed. This is significant in understanding the mechanism of molecule representation. Meanwhile, the results also demonstrate that the proposed method could extract molecular features in a fine granularity, such as atoms and chemical bonds. Computer science ; Transformer ; Big data ; Data modeling ; Artificial intelligence ; Pattern recognition (psychology) ; Data mining ; Electrical engineering ; Engineering ; Database ; Voltage Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Genomics and Phylogenetic Studies
Authors: Dario Massa | Efthimios Kaxiras | Stefanos Papanikolaou
Source: arXiv (Cornell University) (preprint, 2023-09-24)
Field: Materials Science — Materials Chemistry

Alloy Informatics through Ab Initio Charge Density Profiles: Case Study of Hydrogen Effects in Face-Centered Cubic Crystals Materials design has traditionally evolved through trial-error approaches, mainly due to the non-local relationship between microstructures and properties such as strength and toughness. We propose 'alloy informatics' as a machine learning based prototype predictive approach for alloys and compounds, using electron charge density profiles derived from first-principle calculations. We demonstrate this framework in the case of hydrogen interstitials in face-centered cubic crystals, showing that their differential electron charge density profiles capture crystal properties and defect-crystal interaction properties. Radial Distribution Functions (RDFs) of defect-induced differential charge density perturbations highlight the resulting screening effect, and, together with hydrogen Bader charges, strongly correlate to a large set of atomic properties of the metal species forming the bulk crystal. We observe the spontaneous emergence of classes of charge responses while coarse-graining over crystal compositions. Nudge-Elastic-Band calculations show that RDFs and charge features also connect to hydrogen migration energy barriers between interstitial sites. Unsupervised machine-learning on RDFs supports classification, unveiling compositional and configurational non-localities in the similarities of the perturbed densities. Electron charge density perturbations may be considered as bias-free descriptors for a large variety of defects. Crystal (programming language) ; Charge density ; Ab initio ; Materials science ; Chemical physics ; Cubic crystal system ; Charge (physics) ; Molecular physics ; Condensed matter physics ; Chemistry ; Physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Hydrogen embrittlement and corrosion behaviors in metals