jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hongjie Chen | Xuanyu Zhang | Zengyuan Chen
Source:  (article, 2024-02-08)
Field: Materials Science — Materials Chemistry

Photovoltaic conversion of organic photovoltaic materials based on molecular fingerprint feature engineering In recent years, organic solar cells (OSCs) have gradually become the focus of renewable energy research. In order to predict the photovoltaic characteristics of OSCs more accurately and efficiently, researchers have incorporated numerous machine learning models into their studies. In this research, we designed and implemented a neural network model based on molecular fingerprints and applied it to the study of the power conversion efficiency (PCE) prediction of OSCs. We explored the impact of different organic photovoltaic material structures and feature extraction methods on the prediction of the PCE. Through experimental evaluations, the model not only achieved good experimental results in terms of material structure and PCE prediction but also compared the feature extraction methods of different molecular fingerprints. It was found that both Morgan and Circular fingerprints performed excellently in multiple scenarios. Photovoltaic system ; Organic solar cell ; Renewable energy ; Fingerprint (computing) ; Feature extraction ; Computer science ; Artificial neural network ; Artificial intelligence ; Feature (linguistics) ; Energy conversion efficiency ; Pattern recognition (psychology) ; Materials science ; Engineering ; Electrical engineering ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: G. Yu | Dong Hyeon Mok | Ho Yeon Jang | Hyun Dong Jung | Samira Siahrostami | Seoin Back
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Leveraging Machine Learning and Active Motifs-Based Catalyst Design for Discovery of Oxygen Reduction Electrocatalysts for Hydrogen Peroxide Production Hydrogen peroxide ; Catalysis ; Oxygen reduction ; Oxygen ; Chemistry ; Production (economics) ; Computer science ; Biochemical engineering ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Engineering ; Biochemistry ; Electrochemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Karun K. Rao | Michael Nikolaou | Yan Yao | Christopher Wolverton | Lars C. Grabow
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Chemistry — Physical and Theoretical Chemistry

Discovery of Lithium Solid State Electrolytes through Machine Learned Probability Density Analysis Solid state Li-ion conducting electrolytes are a class of materials with appreciable ionic conductivities, but which are still often several of orders of magnitude lower than liquids impeding their commercial adoption in commercial solid-state Li and Li-ion batteries. The major bottleneck to the design of solid electrolytes with high ionic conductivity is the lack of an efficient predictive model of Li-ion diffusivity from either the composition or the crystal structure. Traditional ab initio molecular dynamics (AIMD) simulations have very high accuracy, but are limited by both the number of atoms and timescales they can simulate due to the O(n 3 ) computational cost. Here, we propose using the Li-ion potential energy surface calculated from AIMD to predict ionic conductivity. We show direct correlations (R 2 = 0.72) between several topological descriptors and both the magnitude of the ionic conductivity and diffusion mechanism across many categories of ionic conductors including oxides, halides, and sulfides. Generating the Li-ion probability density typically requires a full AIMD simulation, however, to accelerate the calculation for new materials, we train a three-dimensional U-net convolutional neural network to predict the Li-ion probability density directly from the pseudo-electron density of the crystal (Figure 1). Using AIMD simulations of fifty known Li-ion conductors as training data, we train a model with 91% test accuracy. Our model is able to efficiently calculate the probability density for new materials including arbitrary compositions, space groups, and site occupations (i.e., disordered structures) which greatly expands the design space over which we can optimize and discover ionic conductors. By screening the ICSD we identify several for new candidate solid state electrolytes and re-identified Li 6 NBr 3 . This composition could not be directly simulated using AIMD due to the large unit cell volume, but its ionic conductivity has been experimentally measured as 0.0035 mS/cm at 150°C demonstrating both the accuracy and utility of our model for solid state electrolyte discovery. Figure 1 Lithium (medication) ; Solid-state ; Electrolyte ; Computer science ; Chemistry ; Physical chemistry ; Medicine ; Electrode ; Internal medicine Electrostatics and Colloid Interactions
Authors: Yuanbin Liu | Joe D. Morrow | Christina Ertural | Natascia L. Fragapane | John L. A. Gardner | Aakash Ashok Naik | Yuxing Zhou | Janine George | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2024-12-21)
Field: Materials Science — Materials Chemistry

An automated framework for exploring and learning potential-energy surfaces Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex (`automatic potential-landscape explorer'). We discuss design choices, particularly the interoperability with existing software architectures, and the ability for the end user to easily use the computational workflows provided. We show wide-ranging capability demonstrations: for the titanium-oxygen system, SiO2, crystalline and liquid water, as well as phase-change memory materials. More generally, our study illustrates how automation can speed up atomistic machine learning -- with a long-term vision of making it a genuine mainstream tool in physics, chemistry, and materials science. Computer science ; Energy (signal processing) ; Human–computer interaction ; Knowledge management ; Physics Machine Learning in Materials Science
Authors: Maliheh Shaban Tameh | Veaceslav Coropceanu | T. Purcell | Jean‐Luc Brédas
Source: arXiv (Cornell University) (preprint, 2024-09-26)
Field: Chemistry — Analytical Chemistry

Prediction of the Infrared Absorbance Intensities and Frequencies of Hydrocarbons:A Message Passing Neural Network Approach Accurately and efficiently predicting the infrared (IR) spectra of a molecule can provide insights into the structure-properties relationships of molecular species, which has led to a proliferation of machine learning tools designed for this purpose. However, earlier studies have focused primarily on obtaining normalized IR spectra, which limits their potential for a comprehensive analysis of molecular behavior in the IR range. For instance, to fully understand and predict the optical properties, such as the transparency characteristics, it is necessary to predict the molar absorptivity IR spectra instead. Here, we propose a graph-based communicative message passing neural network (CMPNN) algorithm that can predict both the peak positions and absolute intensities corresponding to density functional theory (DFT) calculated molar absorptivities in the IR domain. By modifying existing spectral loss functions, we show that our method is able to predict with DFT-accuracy level the IR molar absorptivities of a series of hydrocarbons containing up to ten carbon atoms and apply the model to a set of larger molecules. We also compare the predicted spectra with those generated by the direct message passing neural network (DMPNN). The results suggest that both algorithms demonstrate similar predictive capabilities for hydrocarbons, indicating that either model could be effectively used in future research on spectral prediction for such systems. Absorbance ; Infrared ; Artificial neural network ; Biological system ; Environmental science ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Computer science ; Artificial intelligence ; Environmental chemistry ; Optics ; Physics ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Chemical Thermodynamics and Molecular Structure
Authors: Ryosuke Jinnouchi | Saori Minami
Source: ChemRxiv (preprint, 2024-10-15)
Field: Materials Science — Materials Chemistry

Melamine-driven solvation effect promotes oxygen reduction on platinum catalyst: machine learning-aided free energy calculations The modification of Pt surfaces by organic compounds such as melamine is known to enhance the activity of the oxygen reduction reaction (ORR) and improve catalyst durability against potential cycling. Here, we present the mechanism of activity enhancement elucidated by first-principles (FP) free energy calculations using thermodynamic integration within finite-temperature molecular dynamics simulations. These calculations accelerate phase space sampling, including nanosecond-scale fluctuations of interfacial water, by employing machine-learned force fields. The errors introduced by the force fields are corrected via thermodynamic integration from the machine-learned potential to the FP potential, allowing us to obtain accurate FP free energies. The results reveal that melamine destabilizes the OH adsorbate, an intermediate in the ORR, thereby accelerating the OH removal step and pushing the catalytic activity to the top of the volcano plot. This behavior is similar to what is observed on Pt(1-x)Cux alloy surfaces evaluated using the same method. However, the mechanism of OH destabilization by melamine differs entirely from that on alloy surfaces. Unlike alloys, melamine does not shift the d-band center or shorten the metal-metal distance. Instead, melamine disrupts the hydrogen bonds between the OH adsorbate and the surrounding interfacial water. Structural and vibrational analyses have revealed that the solvation structure changes induced by melamine are manifested as alterations in the radial distribution function of water near the OH adsorbate and as a blue shift in the O-H stretching vibrations of interfacial water. These findings indicate that manipulating interfacial solvation with organic compounds could be a promising approach to enhance catalytic activity without compromising durability. Solvation ; Platinum ; Catalysis ; Oxygen reduction ; Reduction (mathematics) ; Melamine ; Oxygen ; Energy (signal processing) ; Materials science ; Chemical engineering ; Chemistry ; Computer science ; Physical chemistry ; Electrode ; Electrochemistry ; Engineering ; Physics ; Ion ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Marco Gibaldi | Anna Kapeliukha | Andrew J. P. White | Jun Luo | Rebeca Mayo | Jake Burner | Tom K. Woo
Source: ChemRxiv (preprint, 2024-11-05)
Field: Materials Science — Materials Chemistry

MOSAEC-DB: A comprehensive database of experimental metal-organic frameworks with verified chemical accuracy suitable for molecular simulations Ongoing developments in computational databases seek to improve the accessibility and breadth of high-throughput screening and materials discovery efforts. Their reliance on experimental crystal structures necessitates significant processing prior to computation in order to resolve any crystallographic disorder or partial occupancies and remove any residual solvent molecules in the case of activated porous materials. Contemporary investigations revealed that deficiencies in the experimental characterization and computational preprocessing methods generated considerable occurrence of structural errors in metal-organic framework (MOF) databases. The MOSAEC MOF database (MOSAEC-DB) tackles these structural reliability concerns through utilization of innovative preprocessing and error analysis protocols applying the concepts of oxidation states and formal charge to exclude erroneous MOF crystal structures. Comprising more than 124k crystal structures, this work maintains the largest and most accurate dataset of experimental MOFs ready for immediate deployment in molecular simulations. The databases’ comparative diversity is demonstrated through its enhanced coverage of the periodic table, expansive quantity of structures, and balance of chemical properties relative to existing MOF databases. Chemical and geometric descriptors, as well as DFT electrostatic potential-fitted charges, are included to facilitate subsequent atomistic simulation and machine-learning (ML) studies. Curated subsets—sampled according to their chemical properties and structural uniqueness—are also provided to further enable ML studies in recognition of the strict demand for duplicate elimination and dataset diversity in such applications. Computer science ; Metal ; Database ; Materials science ; Data mining ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Ruitao Lv | Lingxi Zhou | Menghao Yang | Yihong Liu | Feiyu Kang
Source: Research Square (preprint, 2024-11-27)
Field: Materials Science — Materials Chemistry

Intrinsic metal-support interactions break the activity-stability dilemma in electrocatalysis Dilemma ; Electrocatalyst ; Stability (learning theory) ; Chemistry ; Computer science ; Philosophy ; Physical chemistry ; Epistemology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Anthony Onwuli | Keith T. Butler | Aron Walsh
Source: ChemRxiv (preprint, 2024-08-05)
Field: Materials Science — Materials Chemistry

Ionic species representations for materials informatics High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials. However, the characteristics of elements change when they adopt a given oxidation state, with distinct structural preferences and physical properties. We explore several methods for developing embedding vectors of elements decorated with oxidation states. Graphs generated from 110,160 crystals are used to train representations of 84 elements that form 336 species. Clustering these learned representations of ionic species in low-dimensional space reproduces expected chemical heuristics, in particular the separation of cations from anions. We show that these representations have enhanced expressive power for property prediction tasks involving inorganic compounds. We expect that ionic representations, necessary for the description of mixed valence and complex magnetic systems, will support more powerful machine learning models for materials. Heuristics ; Valence (chemistry) ; Computer science ; Embedding ; Cluster analysis ; Ionic bonding ; Theoretical computer science ; Chemical physics ; Ion ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: T. P. M. Goumans | Nicolas Onofrio
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Accelerating Battery Research through Atomistic and Multi-Scale Computational Chemistry Computational screening and advanced insights from simulations will help you develop new and improved battery materials quicker and with less resources. We will discuss our efforts to help researchers to do that with the Amsterdam Modeling Suite (AMS). The AMS framework separates the atomistic engines (DFT, DFTB, ReaxFF, ML potentials) from the driver, which enables the exploration of potential energy surfaces (PESs), mechanical, and electronic properties at several levels of theory. The central AMS driver supports advanced PES explorations, molecular dynamics (MD) and Grand Canonical Monte Carlo (GCMC), based on energies and forces from the engines. With this driver-engine set up, you can calculate and predict relevant processes and properties for battery materials. We will discuss recent developments and examples using: DFT and GW with COSMO-RS for accurate electrode and redox potentials (e)ReaxFF for discharge processes including diffusion, discharge profiles, electrolyte degradation, and solid-electrolyte interface formation polarizable force fields for accurate viscosities and charge mobilities pre-parametrized and on-the-fly machine learning potentials for diffusion barriers, intercalation potentials, and reactive processes kinetic Monte Carlo to describe mesoscale processes such as dendrite formation We will also discuss where we envision future ML and multi-scale developments to accelerate materials discovery, particularly focusing on battery applications. Figure 1 Scale (ratio) ; Battery (electricity) ; Computer science ; Computational science ; Engineering physics ; Statistical physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Materials science ; Engineering ; Physics ; Thermodynamics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Radioactive element chemistry and processing
Authors: Nikolai Schapin | Maciej Majewski | Mariona Torrens‐Fontanals | Gianni De Fabritiis
Source: arXiv (Cornell University) (preprint, 2024-07-15)
Field: Chemistry — Physical and Theoretical Chemistry

PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks Small molecule protonation is an important part of the preparation of small molecules for many types of computational chemistry protocols. For this, a correct estimation of the pKa values of the protonation sites of molecules is required. In this work, we present pKAce, a new web application for the prediction of micro-pKa values of the molecules' protonation sites. We adapt the state-of-the-art, equivariant, TensorNet model originally developed for quantum mechanics energy and force predictions to the prediction of micro-pKa values. We show that an adapted version of this model can achieve state-of-the-art performance comparable with established models while trained on just a fraction of their training data. Protonation ; Equivariant map ; Molecule ; Artificial neural network ; Chemistry ; Computer science ; Computational chemistry ; Artificial intelligence ; Mathematics ; Pure mathematics ; Organic chemistry History and advancements in chemistry ; Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Jiadong Dan | Cheng Zhang | N. Duane Loh
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Surfaces, Coatings and Films

Can Conventional Classifiers Outperform Neural Networks in Identifying Structural Defects from Atomic Resolution Micrographs? The identification of structural defects in materials through atomic resolution scanning transmission electron microscopy (STEM) images is pivotal for understanding material properties [1] and facilitating advancements in nanotechnology.However, the efficacy of deep neural networks (DNNs), which are commonly employed for such tasks, is significantly hampered by out-of-distribution drift when applied to experimental data.Furthermore, these DNN models often overlook the incorporation of available prior knowledge, such as the chemical composition of samples, into their analytical frameworks.This study proposes a novel approach that challenges the supremacy of DNNs by leveraging an ensemble of simple, yet highly effective, classifiers trained on data simulated using the multislice algorithm for various two-dimensional materials.By categorizing these materials into groups based on differences in atomic number, our methodology not only utilizes prior knowledge regarding chemical composition but also enhances the specificity and accuracy of defect identification.In this work, we emphasize the strategic manipulation of input features through the adoption of Zernike moments [2], which allows us to standardize input data into uniform-length feature vectors regardless of the original image or patch size.This innovative approach mitigates the challenges posed by multiscale variations inherent in STEM images, ensuring consistent and reliable analysis.The ensemble model's architecture is designed to direct experimental data toward the most appropriate classifier based on the sample's chemical composition, thus maximizing the relevance and efficiency of the prediction process.Preliminary results suggest that our ensemble of conventional classifiers not only matches but potentially exceeds the performance of conventional DNNs in identifying structural defects, particularly by effectively leveraging the prior knowledge and addressing the issue of out-of-distribution drift.This study signifies a critical step forward in the application of machine learning to microscopy and microanalysis, highlighting the untapped potential of combining traditional classifiers with domain-specific knowledge to overcome the limitations of current deep learning paradigms [3]. Micrograph ; Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; Resolution (logic) ; Materials science ; Computer science ; Composite material Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Machine Learning in Materials Science
Authors: Md Rajib Khan Musa | Yichen Qian | Jie Peng | David Cereceda
Source: arXiv (Cornell University) (preprint, 2024-10-08)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of low-energy structure configurations: a computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications. Monte Carlo method ; Computer science ; Sampling (signal processing) ; Statistical physics ; Machine learning ; Physics ; Mathematics ; Statistics Machine Learning in Materials Science