jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Payam Kalhor | Nicole Jung | Stefan Bräse | Christof Wöll | Manuel Tsotsalas | Pascal Friederich
Source: Advanced Functional Materials (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Functional Material Systems Enabled by Automated Data Extraction and Machine Learning Abstract The development of new functional materials is crucial for addressing global challenges such as clean energy or the discovery of new drugs and antibiotics. Functional material systems are typically composed of functional molecular building blocks, organized across multiple length scales in a hierarchical order. The large design space allows for precise tuning of properties to specific applications, but also makes it time‐consuming and expensive to screen for optimal structures using traditional experimental methods. Machine learning (ML) models can potentially revolutionize the field of materials science by predicting chemical syntheses and materials properties with high accuracy. However, ML models require data to be trained and validated. Methods to automatically extract data from scientific literature make it possible to build large and diverse datasets for ML models. In this article, opportunities and challenges of data extraction and machine learning methods are discussed to accelerate the discovery of high‐performing functional material systems, while ensuring that the predicted materials are stable, synthesizable, scalable, and sustainable. The potential impact of large language models (LLMs) on the data extraction process are discussed. Additionally, the importance of research data management tools is discussed to overcome the intrinsic limitations of data extraction approaches. Computer science ; Scalability ; Machine learning ; Field (mathematics) ; Chemical space ; Data extraction ; Process (computing) ; Artificial intelligence ; Data science ; Knowledge extraction ; Experimental data ; Data mining ; Drug discovery ; Bioinformatics ; Database Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Zijian Liu | Wende Tian | Zhe Cui | Bin Liu
Source: Separation and Purification Technology (article, 2023-01-24)
Field: Materials Science — Materials Chemistry

A universal microkinetic-machine learning bimetallic catalyst screening method for steam methane reforming Bimetallic strip ; Catalysis ; Methane ; Steam reforming ; Chemistry ; Process engineering ; Computer science ; Biochemical engineering ; Engineering ; Organic chemistry ; Hydrogen production Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jakub Kubečka | Vitus Besel | Ivo Neefjes | Yosef Knattrup | Theo Kurtén | Hanna Vehkamäki | Jonas Elm
Source: ACS Omega (article, 2023-11-14)
Field: Materials Science — Materials Chemistry

Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning Computational modeling of atmospheric molecular clusters requires a comprehensive understanding of their complex configurational spaces, interaction patterns, stabilities against fragmentation, and even dynamic behaviors. To address these needs, we introduce the Jammy Key framework, a collection of automated scripts that facilitate and streamline molecular cluster modeling workflows. Jammy Key handles file manipulations between varieties of integrated third-party programs. The framework is divided into three main functionalities: (1) Jammy Key for configurational sampling (JKCS) to perform systematic configurational sampling of molecular clusters, (2) Jammy Key for quantum chemistry (JKQC) to analyze commonly used quantum chemistry output files and facilitate database construction, handling, and analysis, and (3) Jammy Key for machine learning (JKML) to manage machine learning methods in optimizing molecular cluster modeling. This automation and machine learning utilization significantly reduces manual labor, greatly speeds up the search for molecular cluster configurations, and thus increases the number of systems that can be studied. Following the example of the Atmospheric Cluster Database (ACDB) of Elm (ACS Omega, 4, 10965-10984, 2019), the molecular clusters modeled in our group using the Jammy Key framework have been stored in an improved online GitHub repository named ACDB 2.0. In this work, we present the Jammy Key package alongside its assorted applications, which underline its versatility. Using several illustrative examples, we discuss how to choose appropriate combinations of methodologies for treating particular cluster types, including reactive, multicomponent, charged, or radical clusters, as well as clusters containing flexible or multiconformer monomers or heavy atoms. Finally, we present a detailed example of using the tools for atmospheric acid-base clusters. Computer science ; Sampling (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Daniel Schletz | Morten Breidung | Andreas Fery
Source: The Journal of Physical Chemistry C (article, 2023-01-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Validating and Utilizing Machine Learning Methods to Investigate the Impacts of Synthesis Parameters in Gold Nanoparticle Synthesis The control over the synthesis of gold nanoparticles is crucial to ensure optimal optical and processing properties, but synthesis is complex and interdependent on many variables such as reducing agent, capping agent, and the amount of gold seeds and precursor. Machine learning offers the prospect of giving insight into this multidimensional problem, but the reason for selecting a certain model is often unclear. Here, we apply tree-based machine learning algorithms on the semi-batch, seed-mediated synthesis of gold nanoparticles in the size range of 20–120 nm to analyze the correlation between synthesis parameters, optical spectra, and size. After testing the validity of the machine learning models by nested cross-validation, the Random Forest model is selected as a simple model that can reproduce the outcome of the synthesis well. In a further analysis by SHAP (SHapley Additive exPlanations), chemical relationships that were not explicitly taught to the model but purely derived from the data analysis are revealed. Colloidal gold ; Computer science ; Machine learning ; Artificial intelligence ; Random forest ; Nanoparticle ; Interdependence ; Tree (set theory) ; Range (aeronautics) ; Algorithm ; Nanotechnology ; Materials science ; Mathematics Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Xingyi Guan | Joseph P. Heindel | Taehee Ko | Chao Yang | Teresa Head‐Gordon
Source: Nature Computational Science (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Using machine learning to go beyond potential energy surface benchmarking for chemical reactivity Metadynamics ; Potential energy surface ; Benchmarking ; Ab initio ; Workflow ; Computer science ; Intuition ; Retraining ; Potential energy ; Hydrogen ; Free energy principle ; Chemistry ; Molecular dynamics ; Artificial intelligence ; Computational chemistry ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Han Zhang | Haoyuan Li | Hanshen Xin | Jianhua Zhang
Source: Journal of Chemical Information and Modeling (article, 2023-08-24)
Field: Chemistry — Organic Chemistry

Property Prediction and Structural Feature Extraction of Polyimide Materials Based on Machine Learning The construction of material prediction models using machine learning algorithms can aid in the polyimide structural design and screening of materials as well as accelerate the development of new materials. There is a lack of research on predicting the optical properties of polyimide materials and the interpretation of the structural features. Here, we collected 652 polyimide molecular structures and used seven popular machine learning algorithms to predict the glass transition temperature and cut-off wavelength of polyimide materials and extract key feature information of repeating unit structures. The results showed that the root mean square error of the glass transition temperature prediction model was 33.92 °C, and the correlation coefficient was 0.861. The root mean square error of the cut-off wavelength prediction model was 17.18 nm, and the correlation coefficient was 0.837. The elasticity of the molecular structure was also found to be the key factor affecting glass transition temperature, and the presence and location of heterogeneous atoms had a significant effect on the cut-off wavelengths. Finally, eight polyimide materials were synthesized to test the accuracy of the prediction models, and the experimental characterization values agreed with the predicted values. The results would contribute to the development of polyimide structural design and materials preparation for flexible display. Polyimide ; Materials science ; Glass transition ; Mean squared error ; Wavelength ; Biological system ; Correlation coefficient ; Artificial intelligence ; Computer science ; Composite material ; Machine learning ; Mathematics ; Polymer ; Optoelectronics ; Statistics ; Layer (electronics) Photopolymerization techniques and applications ; Synthesis and properties of polymers
Authors: Chenyu Xing | Gaoyu Chen | Xia Zhu | Jiakun An | Jianchun Bao | Xuan Wang | Xiuqing Zhou | Xiuli Du | Xiangxing Xu
Source: Nano Research (article, 2023-07-14)
Field: Materials Science — Materials Chemistry

Synthesis of carbon dots with predictable photoluminescence by the aid of machine learning Carbon dots (CDs) have wide application potentials in optoelectronic devices, biology, medicine, chemical sensors, and quantum techniques due to their excellent fluorescent properties. However, synthesis of CDs with controllable spectrum is challenging because of the diversity of the CD components and structures. In this report, machine learning (ML) algorithms were applied to help the synthesis of CDs with predictable photoluminescence (PL) under the excitation wavelengths of 365 and 532 nm. The combination of precursors was used as the variable. The PL peaks of the strongest intensity (λs) and the longest wavelength (λl) were used as target functions. Among six investigated ML models, the random forest (RF) model showed outstanding performance in the prediction of the PL peaks. Photoluminescence ; Fluorescence ; Quantum dot ; Wavelength ; Materials science ; Carbon fibers ; Excitation wavelength ; Optoelectronics ; Luminescence ; Excitation ; Nanotechnology ; Computer science ; Physics ; Optics ; Quantum mechanics Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Scott Kennedy | Md Raf E Ul Shougat | Edmon Perkins
Source: Sensors and Actuators A Physical (article, 2023-02-21)
Field: Materials Science — Materials Chemistry

Robust self-sensing shape memory alloy actuator using a machine learning approach Shape-memory alloy ; Actuator ; Alloy ; Computer science ; Artificial intelligence ; Control engineering ; Mechanical engineering ; Materials science ; Engineering ; Composite material Shape Memory Alloy Transformations
Authors: Rémi Pétuya | Abhishek Punase | Emanuele Bosoni | Antonio Pedro de Oliveira Filho | Juan Sarria | Nirupam Purkayastha | Jonathan Wylde | Stephan Mohr
Source: ACS Omega (article, 2023-01-27)
Field: Chemistry — Analytical Chemistry

Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance Molecular dynamics simulations have been employed to investigate the effect of molecular polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine-learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, single asphaltene model simulations have shown a broad range of aggregation behaviors, driven by their structural features: size of the aromatic core, length of the aliphatic chains, and presence of heteroatoms. Then, the combination of these model molecules in a series of mixtures have highlighted the complex and diverse effects of molecular polydispersity on the aggregation process of asphaltene. Simulations yielded both antagonistic and synergistic effects mediated by the trigger or facilitator action of specific asphaltene model molecules. These findings illustrate the necessity of accounting for molecular polydispersity when studying the asphaltene aggregation process and have permitted establishing a robust protocol for the <i>in silico</i> evaluation of the performance of asphaltene inhibitors, as illustrated for the case of a nonylphenol resin. Asphaltene ; Dispersity ; Molecular dynamics ; Molecule ; Chemistry ; Chemical physics ; Computational chemistry ; Materials science ; Organic chemistry Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Computational Drug Discovery Methods
Authors: Felix Okello | Timothy Tizhe Fidelis | John Onyango Agumba | Timothy Manda | Livingstone Ochilo | Asif Mahmood | Anthony M.S. Pembere
Source: Materials Today Communications (article, 2023-07-03)
Field: Chemistry — Inorganic Chemistry

Towards estimation and mechanism of CO2 adsorption on zeolite adsorbents using molecular simulations and machine learning Adsorption ; Zeolite ; Materials science ; Gravimetric analysis ; Sorption ; Lithium (medication) ; Chemical engineering ; Carbon fibers ; Physical chemistry ; Organic chemistry ; Composite material ; Catalysis ; Chemistry Zeolite Catalysis and Synthesis ; Carbon Dioxide Capture Technologies ; Phase Equilibria and Thermodynamics
Authors: Arpit B. Mendhe | Himanshu Sekhar Panda
Source: ACS Sustainable Chemistry & Engineering (article, 2023-11-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Assisted Electrode Material Fabrication and Electrochemical Efficiency Prediction and Validation of PANI-Ni/Co Hydroxide Nanocomposites Electrode materials play a critical role in the charge storage mechanism in supercapacitor devices. Hence, predicting the performance, namely, the specific capacitance (Csp), rate capability, and cyclic stability of these electrodes, is crucial for supercapacitor applications. In this report, a data-centric machine-learning (ML) approach was implemented to study the effect of polyaniline (PANI) on the electrochemical behavior of NiCo(OH)2. ML models were used to predict the combinational Csp-grade and Csp-value of composite electrode materials. The lowest relative percentage error of 1.04% for 3 PNC is observed between predicted and experimental Csp-values. The ML-predicted rate capability of 3 PNC strongly aligned with the experimental data when the current density is increased 4-fold and revealed better rate capability. The experimental Csp retention of 82.4% after 10000 cycles closely matched the predicted retention of 88.56%, with a percentage error of 6.92%. Hence, the ML may be implemented to predict the performance of other electrode materials for energy storage applications. Supercapacitor ; Electrode ; Polyaniline ; Materials science ; Capacitance ; Horizontal scan rate ; Electrochemistry ; Fabrication ; Nanocomposite ; Composite number ; Energy storage ; Chemical engineering ; Composite material ; Nanotechnology ; Cyclic voltammetry ; Polymer ; Chemistry ; Thermodynamics Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: George Stephen Thoppil | Jian‐Feng Nie | Alankar Alankar
Source: Journal of Alloys and Compounds (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Bayesian approach for inferrable machine learning models of process–structure–property linkages in complex concentrated alloys The difference in the mechanical behaviors of dilute solid solutions , complex solid solutions and their corresponding strengthening mechanisms , is an evolving field of study. An understanding of the mechanisms and formulation of theories of strengthening in the complex atomic energy landscapes could eventually lead to a better understanding of the fundamental behavior of condensed matter itself. In this work we attempt to extract the effect of thermo–mechanical processing on the microstructure–mechanical property linkages of complex concentrated alloys (CCAs) by training machine learning (ML) models using processing information / parameters as features. The effect of processing on the phase morphology and the mechanical properties is studied. The stacking fault energy (SFE) predicted based on CCA composition is used as a benchmark to identify deformation mechanisms that are activated based on the arrangement of the component elements within the distorted CCA lattice. This work presents a novel method that attempts to establish ML based process–structure–property (PSP) linkages that could help capture higher order dependencies that may not be adequately captured by existing relations between mechanical properties, phase evolution, composition and processing information. An assortment of Bayesian–learning models are used to create a framework that captures the evolution of phases, their volume fractions, grain sizes and the corresponding change in mechanical properties of a diverse set of CCA compositions as they encounter various processing conditions. The evolution of the mechanical property with grain size is captured as Hall–Petch relations as an example of possible PSP linkage representations. • Effect of thermo-mechanical processing on the microstructure-mechanical properties. • A hierarchical model for determining properties over multiple length scale. • Bayesian learning models for predicting phases and their volume fractions. • Hall-Petch relation is verified as an example of PSP linkage representations. Stacking-fault energy ; Materials science ; Artificial intelligence ; Grain boundary ; Microstructure ; Machine learning ; Computer science ; Statistical physics ; Mechanical engineering ; Engineering ; Physics ; Metallurgy Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Jianchang Wu | Jiyun Zhang | Manman Hu | Patrick Reiser | Luca Torresi | Pascal Friederich | Leopold Lahn | Olga Kasian | Dirk M. Guldi | M. Eugenia Pérez‐Ojeda | Anastasia Barabash | Juan S. Rocha‐Ortiz | Yicheng Zhao | Zhiqiang Xie | Junsheng Luo | Yunuo Wang | Sang Il Seok | Jens Hauch | Christoph J. Brabec
Source: Journal of the American Chemical Society (article, 2023-07-19)
Field: Materials Science — Materials Chemistry

Integrated System Built for Small-Molecule Semiconductors via High-Throughput Approaches High-throughput synthesis of solution-processable structurally variable small-molecule semiconductors is both an opportunity and a challenge. A large number of diverse molecules provide a possibility for quick material discovery and machine learning based on experimental data. However, the diversity of the molecular structure leads to the complexity of molecular properties, such as solubility, polarity, and crystallinity, which poses great challenges to solution processing and purification. Here, we first report an integrated system for the high-throughput synthesis, purification, and characterization of molecules with a large variety. Based on the principle “Like dissolves like,” we combine theoretical calculations and a robotic platform to accelerate the purification of those molecules. With this platform, a material library containing 125 molecules and their optical-electronic properties was built within a timeframe of weeks. More importantly, the high repeatability of recrystallization we design is a reliable approach to further upgrading and industrial production. Chemistry ; Molecule ; Nanotechnology ; Throughput ; Crystallinity ; Small molecule ; Recrystallization (geology) ; Semiconductor ; Characterization (materials science) ; Solubility ; Combinatorial chemistry ; Organic chemistry ; Computer science ; Materials science ; Optoelectronics ; Crystallography Machine Learning in Materials Science ; Perovskite Materials and Applications ; Organic Electronics and Photovoltaics
Authors: Xiaorui Wang | Chang‐Yu Hsieh | Xiaodan Yin | Jike Wang | Yuquan Li | Yafeng Deng | Dejun Jiang | Zhenhua Wu | Hongyan Du | Hongming Chen | Yun Li | Huanxiang Liu | Yuwei Wang | Pei Luo | Tingjun Hou | Xiaojun Yao
Source: Research (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center Effective synthesis planning powered by deep learning (DL) can significantly accelerate the discovery of new drugs and materials. However, most DL-assisted synthesis planning methods offer either none or very limited capability to recommend suitable reaction conditions (RCs) for their reaction predictions. Currently, the prediction of RCs with a DL framework is hindered by several factors, including: (a) lack of a standardized dataset for benchmarking, (b) lack of a general prediction model with powerful representation, and (c) lack of interpretability. To address these issues, we first created 2 standardized RC datasets covering a broad range of reaction classes and then proposed a powerful and interpretable Transformer-based RC predictor named Parrot. Through careful design of the model architecture, pretraining method, and training strategy, Parrot improved the overall top-3 prediction accuracy on catalysis, solvents, and other reagents by as much as 13.44%, compared to the best previous model on a newly curated dataset. Additionally, the mean absolute error of the predicted temperatures was reduced by about 4 °C. Furthermore, Parrot manifests strong generalization capacity with superior cross-chemical-space prediction accuracy. Attention analysis indicates that Parrot effectively captures crucial chemical information and exhibits a high level of interpretability in the prediction of RCs. The proposed model Parrot exemplifies how modern neural network architecture when appropriately pretrained can be versatile in making reliable, generalizable, and interpretable recommendation for RCs even when the underlying training dataset may still be limited in diversity. Interpretability ; Benchmarking ; Computer science ; Machine learning ; Artificial intelligence ; Deep learning ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Emma Van de Reydt | Noam Marom | James Saunderson | Mario Boley | Tanja Junkers
Source: Polymer Chemistry (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A Predictive machine-learning model for propagation rate coefficients in radical polymerization Using ridge regression, the propagation rate coefficients for radical polymerization are correlated with basic molecular properties. Polymerization ; Computer science ; Chemistry ; Organic chemistry ; Polymer Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Polymer Synthesis and Characterization