jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shizhao Lu | Arthi Jayaraman
Source: JACS Au (article, 2023-07-21)
Field: Materials Science — Materials Chemistry

Pair-Variational Autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques In materials research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of a synthesized material. Depending on the availability and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other technique(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small-angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques (easy and difficult to interpret, fast and slow in data collection or sample preparation) so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, Pair-Variational Autoencoders (PairVAE), that works with data from small-angle X-ray scattering (SAXS) that present information about bulk morphology and images from scanning electron microscopy (SEM) that present two-dimensional local structural information on the sample. Using paired SAXS and SEM data of newly observed block copolymer assembled morphologies [open access data from Doerk G. S.; et al. Sci. Adv.2023, 9 ( (2), ), eadd3687], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample's corresponding SAXS 2D pattern and vice versa. This method can be extended to other soft material morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as an engine for generating ensembles of similar microscopy images to create a database for other downstream calculations of structure-property relationships. Characterization (materials science) ; Small-angle X-ray scattering ; Scattering ; Artificial intelligence ; Computer science ; Block (permutation group theory) ; Sample (material) ; Materials science ; Pattern recognition (psychology) ; Nanotechnology ; Optics ; Physics ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Rodrigo A. Vargas–Hernández | Kjell Jorner | Robert Pollice | Alán Aspuru‐Guzik
Source: The Journal of Chemical Physics (article, 2023-02-17)
Field: Materials Science — Materials Chemistry

Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation Semiempirical quantum chemistry has recently seen a renaissance with applications in high-throughput virtual screening and machine learning. The simplest semiempirical model still in widespread use in chemistry is Hückel's π-electron molecular orbital theory. In this work, we implemented a Hückel program using differentiable programming with the JAX framework based on limited modifications of a pre-existing NumPy version. The auto-differentiable Hückel code enabled efficient gradient-based optimization of model parameters tuned for excitation energies and molecular polarizabilities, respectively, based on as few as 100 data points from density functional theory simulations. In particular, the facile computation of the polarizability, a second-order derivative, via auto-differentiation shows the potential of differentiable programming to bypass the need for numeric differentiation or derivation of analytical expressions. Finally, we employ gradient-based optimization of atom identity for inverse design of organic electronic materials with targeted orbital energy gaps and polarizabilities. Optimized structures are obtained after as little as 15 iterations using standard gradient-based optimization algorithms. Automatic differentiation ; Differentiable function ; Polarizability ; Density functional theory ; Computer science ; Computation ; Computational chemistry ; Applied mathematics ; Chemistry ; Mathematical optimization ; Physics ; Algorithm ; Quantum mechanics ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Organic Chemistry Cycloaddition Reactions ; Advanced Chemical Physics Studies
Authors: Tong Xie | Yuwei Wan | Wei Huang | Yufei Zhou | Yixuan Liu | Qingyuan Linghu | Shaozhou Wang | Chunyu Kit | Clara Grazian | Zhang, Wenjie | Hoex, Bram
Source: arXiv (Cornell University) (preprint, 2023-04-05)
Field: Materials Science — Materials Chemistry

Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches. However, the materials science field struggles to effectively utilize the abundance of data, especially in applied disciplines where materials are evaluated based on device performance rather than their properties. This article presents a new natural language processing (NLP) task called structured information inference (SII) to address the complexities of information extraction at the device level in materials science. We accomplished this task by tuning GPT-3 on an existing perovskite solar cell FAIR (Findable, Accessible, Interoperable, Reusable) dataset with 91.8% F1-score and extended the dataset with data published since its release. The produced data is formatted and normalized, enabling its direct utilization as input in subsequent data analysis. This feature empowers materials scientists to develop models by selecting high-quality review articles within their domain. Additionally, we designed experiments to predict the electrical performance of solar cells and design materials or devices with targeted parameters using large language models (LLMs). Our results demonstrate comparable performance to traditional machine learning methods without feature selection, highlighting the potential of LLMs to acquire scientific knowledge and design new materials akin to materials scientists. Computer science ; Task (project management) ; Interoperability ; Inference ; Data science ; Field (mathematics) ; Key (lock) ; Artificial intelligence ; World Wide Web ; Systems engineering ; Engineering Machine Learning in Materials Science
Authors: Aleksey V. Dengaev | Aydar A. Kayumov | Andrey A. Getalov | Firdavs A. Aliev | Gadel F. Baimukhametov | Boris V. Sargin | А. Ф. Максименко | Аlexey V. Vakhin
Source: Fluids (article, 2023-04-21)
Field: Chemistry — Analytical Chemistry

Chemical Viscosity Reduction of Heavy Oil by Multi-Frequency Ultrasonic Waves with the Main Harmonics of 20–60 kHz Ultrasound technologies are well-known for their ability to intensify the heat and mass transfer processes. Hence, ultrasonic treatment processes are widely applied for the separation of oil–water emulsions, optimization of oil pumping processes, cleaning the bottomhole zone, etc. However, the main phenomenon under the positive influence of ultrasonic waves on such processes is the cavitation bubbles implosion on the water–oil boundary. It is well-known that ultrasound energy contributes to the reversible viscosity reduction in heavy oil systems. However, it is possible to exhibit chemical destruction of the weakest carbon–heteroatom bonds in the structure of the asphaltenes. This study investigates the influences of controllable ultrasound waves with frequency ranges of 20–60 kHz under the exposure time of 60 s on the rheology of a heavy crude oil sample produced from the Ashalcha reservoir (Tatarstan Republic, Russia). The specific feature of this study is the application of multi-frequency ultrasonic exposure with a wide spectrum of side harmonics with the frequency up to 400 kHz. The results of the Saturates, Aromatics, Resins and Asphaltenes (SARA) analysis method support the chemical consequences of ultrasonication of crude oil. The content of resins under the irradiation of ultrasound waves altered from 32.5 wt.% to 29.4 wt.%, while the number of aromatics hydrocarbons raised from 24.3 wt.% to 34.1 wt.%. The Gas Chromatography—Mass Spectroscopy (GC-MS) analytical analysis method was applied to qualitatively compare the composition of saturated and aromatics fractions between the initial and upgraded heavy crude oil in order to show the chemical destruction of asphaltene bonds after the ultrasonic treatment. It was demonstrated that ultrasound waves allowed chemical conversion of asphaltene fragments that led to irreversible viscosity reduction. The viscosity of the heavy oil sample under the favorable ultrasonic irradiation conditions reduced from 661.2 mPa·s to 178.8 mPa·s. This advantage can be used to develop enhanced oil recovery methods and partial upgrading processes in downstream conditions. Asphaltene ; Ultrasonic sensor ; Harmonics ; Materials science ; Ultrasound ; Viscosity ; Analytical Chemistry (journal) ; Chemistry ; Chemical engineering ; Chromatography ; Acoustics ; Composite material ; Organic chemistry Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Yuxin Guo | Yong‐Bin Zhuang | Jueli Shi | Jun Cheng
Source: The Journal of Chemical Physics (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

ChecMatE: A workflow package to automatically generate machine learning potentials and phase diagrams for semiconductor alloys Semiconductor alloy materials are highly versatile due to their adjustable properties; however, exploring their structural space is a challenging task that affects the control of their properties. Traditional methods rely on ad hoc design based on the understanding of known chemistry and crystallography, which have limitations in computational efficiency and search space. In this work, we present ChecMatE (Chemical Material Explorer), a software package that automatically generates machine learning potentials (MLPs) and uses global search algorithms to screen semiconductor alloy materials. Taking advantage of MLPs, ChecMatE enables a more efficient and cost-effective exploration of the structural space of materials and predicts their energy and relative stability with ab initio accuracy. We demonstrate the efficacy of ChecMatE through a case study of the InxGa1-xN system, where it accelerates structural exploration at reduced costs. Our automatic framework offers a promising solution to the challenging task of exploring the structural space of semiconductor alloy materials. Workflow ; Computer science ; Semiconductor ; Stability (learning theory) ; Task (project management) ; Space (punctuation) ; Phase space ; Ab initio ; Machine learning ; Computational science ; Artificial intelligence ; Materials science ; Systems engineering ; Engineering ; Chemistry ; Physics ; Optoelectronics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electron and X-Ray Spectroscopy Techniques
Authors: Jonathan D. Hirst | Samuel Boobier | Jennifer Coughlan | Jessica Streets | Philippa L. Jacob | O. Pugh | Ender Özcan | Simon Woodward
Source: Artificial Intelligence Chemistry (article, 2023-07-11)
Field: Materials Science — Materials Chemistry

ML meets MLn: Machine learning in ligand promoted homogeneous catalysis The benefits of using machine learning approaches in the design, optimisation and understanding of homogeneous catalytic processes are being increasingly realised. We focus on the understanding and implementation of key concepts, which serve as conduits to more advanced chemical machine learning literature, much of which is (presently) outside the area of homogeneous catalysis. Potential pitfalls in the ‘workflow’ procedures needed in the machine learning process are identified and all the examples provided are in a chemical sciences context, including several from ‘real world’ catalyst systems. Finally, potential areas of expansion and impact for machine learning in homogeneous catalysis in the future are considered. Homogeneous ; Workflow ; Context (archaeology) ; Catalysis ; Homogeneous catalysis ; Computer science ; Process (computing) ; Biochemical engineering ; Artificial intelligence ; Process management ; Chemistry ; Engineering ; Organic chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Óscar Barros | Píer Parpot | Elisabetta Rombi | Teresa Tavares | Isabel C. Neves
Source: Chemical Engineering Science (article, 2023-11-28)
Field: Chemistry — Organic Chemistry

Machine learning approach for classification of REE/Fe-zeolite catalysts for fenton-like reaction Various heterogeneous catalysts based on rare earth elements (REE) and iron supported on zeolites were selected and analyzed using machine learning approaches. REE were used in the preparation of multiple REE/Fe-zeolite catalysts with lanthanum, praseodymium or cerium obtained by ion exchange or impregnation methods, using FAU or MFI structures as supports. The efficiency of these REE/Fe-zeolite catalysts was examined in Fenton-like reaction, in the degradation of tartrazine (Tar) and indigo carmine (IC) as selected organic pollutants in the aqueous solution. The REE/Fe-zeolite catalysts demonstrated outstanding performance, with Tar being degraded by over 80% and IC 95%. Machine learning algorithms were employed for clustering and classification of the different catalysts, based on their performance. Unsupervised learning algorithms like Principal Component Analysis and K-Means were used for pattern recognition while supervised classifiers were employed to classify the heterogeneous catalysts, considering their ability to degrade dyes by Fenton reaction. Catalysis ; Zeolite ; Tartrazine ; Cerium ; Chemistry ; Ion exchange ; Inorganic chemistry ; Ion ; Chromatography ; Organic chemistry Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Catalytic Processes in Materials Science
Authors: Gbolade O. Kayode | M. M. Montemore
Source: JACS Au (article, 2023-12-19)
Field: Materials Science — Materials Chemistry

Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability Machine learning has been successfully applied in recent years to screen materials for a variety of applications. However, despite recent advances, most screening-based machine learning approaches are limited in generality and transferability, requiring new models to be created from scratch for each new application. This is particularly apparent in catalysis, where there are many possible intermediates and transition states of interest in addition to a large number of potential catalytic materials. In this work, we developed a new machine learning framework that is built on chemical principles and allows the creation of general, interpretable, reusable models. Our new architecture uses latent variables to create a set of submodels that each take on a relatively simple learning task, leading to higher data efficiency and promoting transfer learning. This architecture infuses fundamental chemical principles, such as the existence of elements as discrete entities. We show that this architecture allows for the creation of models that can be reused for many different applications, providing significant improvements in efficiency and convenience. For example, our architecture allows simultaneous prediction of adsorption energies for many adsorbates on a broad array of alloy surfaces with mean absolute errors (MAEs) around 0.20-0.25 eV. The integration of latent variables provides physical interpretability, as predictions can be explained in terms of the learned chemical environment as represented by the latent space. Further, these latent variables also serve as new feature representations, allowing efficient transfer learning. For example, new models with useful levels of accuracy can be created with less than 10 data points, including transfer learning to an experimental data set with an MAE less than 0.15 eV. Lastly, we show that our new machine learning architecture is general and robust enough to handle heterogeneous and multifidelity data sets, allowing researchers to leverage existing data sets to speed up screening using their own computational setup. Interpretability ; Latent variable ; Artificial intelligence ; Computer science ; Machine learning ; Variable (mathematics) ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Catalysis and Oxidation Reactions
Authors: Frederik Ørsted Kjeldal | Janus J. Eriksen
Source: Journal of Chemical Theory and Computation (article, 2023-03-16)
Field: Materials Science — Materials Chemistry

Decomposing Chemical Space: Applications to the Machine Learning of Atomic Energies We apply a number of atomic decomposition schemes across the standard QM7 data set─a small model set of organic molecules at equilibrium geometry─to inspect the possible emergence of trends among contributions to atomization energies from distinct elements embedded within molecules. Specifically, a recent decomposition scheme of ours based on spatially localized molecular orbitals is compared to alternatives that instead partition molecular energies on account of which nuclei individual atomic orbitals are centered on. We find these partitioning schemes to expose the composition of chemical compound space in very dissimilar ways in terms of the grouping, binning, and heterogeneity of discrete atomic contributions, e.g., those associated with hydrogens bonded to different heavy atoms. Furthermore, unphysical dependencies on the one-electron basis set are found for some, but not all of these schemes. The relevance and importance of these compositional factors for training tailored neural network models based on atomic energies are next assessed. We identify both limitations and possible advantages with respect to contemporary machine learning models and discuss the design of potential counterparts based on atoms and the intrinsic energies of these as the principal decomposition units. Atomic orbital ; Chemical space ; Partition (number theory) ; Set (abstract data type) ; Space (punctuation) ; Computer science ; Decomposition ; Basis set ; Molecule ; Basis (linear algebra) ; Statistical physics ; Atomic nucleus ; Chemical physics ; Physics ; Chemistry ; Electron ; Atomic physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Edgar Iván Sánchez Medina | Sreekanth Kunchapu | Kai Sundmacher
Source: The Journal of Physical Chemistry A (article, 2023-11-09)
Field: Materials Science — Materials Chemistry

Gibbs–Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite Dilution Machine learning models have gained prominence for predicting pure-component properties, yet their application to mixture property prediction remains relatively limited. However, the significance of mixtures in our daily lives is undeniable, particularly in industries such as polymer processing. This study presents a modification of the Gibbs-Helmholtz graph neural network (GH-GNN) model for predicting weight-based activity coefficients at infinite dilution (Ω<sub><i>ij</i></sub><sup>∞</sup>) in polymer solutions. We evaluate various polymer representations ranging from monomer, repeating unit, periodic unit, and oligomer and observe that, in data-scarce scenarios of polymer-solvent mixtures, polymer representation specifics have a reduced impact compared to data-rich environments. Leveraging transfer learning, we harness richer activity coefficient data from small-size systems, enhancing model accuracy and reducing prediction variability. The modified GH-GNN model achieves remarkable prediction results in mixture interpolation and solvent extrapolation tasks having an overall mean absolute error of 0.15, showcasing the potential of graph-neural-network-based models for property prediction of polymer solutions. Comparative analysis with the established models UNIFAC-ZM and Entropic-FV suggests a promising avenue for future research on the use of data-driven models for the prediction of the thermodynamic properties of polymer solutions. Activity coefficient ; Extrapolation ; Dilution ; UNIFAC ; Polymer ; Artificial neural network ; Chemistry ; COSMO-RS ; Biological system ; Thermodynamics ; Graph ; Helmholtz free energy ; Representation (politics) ; Computer science ; Artificial intelligence ; Mathematics ; Organic chemistry ; Theoretical computer science ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Quentin Gromoff | Patrizio Benzo | Wissam A. Saidi | Christopher M. Andolina | Marie‐José Casanove | Teresa Hungrı́a | Sophie Barré | Magali Benoit | Julien Lam
Source: Nanoscale (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations While nanoalloys are of paramount scientific and practical interest, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including the crystal phase, chemical ordering, and morphology, are challenging to control at the nanoscale, making it difficult to extend their use to industrial applications. In this contribution, we focus on the gold/silver system that has two of the most prevalent noble metals and combine experiments with simulations to uncover the formation mechanisms at the atomic level. Nanoparticles were produced using a state-of-the-art inert-gas aggregation source and analyzed using transmission electron microscopy and energy-dispersive X-ray spectroscopy. Machine-learning-assisted molecular dynamics simulations were employed to model the crystallization process from liquid droplets to nanocrystals. Our study finds a preponderance of nanoparticles with five-fold symmetric morphology, including icosahedra and decahedra which is consistent with previous results on mono-metallic nanoparticles. However, we observed that gold atoms, rather than silver atoms, segregate at the surface of the obtained nanoparticles for all the considered alloy compositions. These segregation tendencies are in contrast to previous studies and have consequences on the crystallization dynamics and the subsequent crystal ordering. We finally showed that the underpinning of this surprising segregation dynamics is due to charge transfer and electrostatic interactions rather than surface energy considerations. Gas phase ; Phase (matter) ; Materials science ; Nanotechnology ; Chemical engineering ; Computer science ; Chemistry ; Physical chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; nanoparticles nucleation surface interactions