jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alex Poppe | Jack Griffiths | Shu Hu | Jeremy J. Baumberg | Margarita Osadchy | Stuart Gibson | Bart de Nijs
Source: The Journal of Physical Chemistry Letters (article, 2023-08-18)
Field: Materials Science — Materials Chemistry

Mapping Atomic-Scale Metal–Molecule Interactions: Salient Feature Extraction through Autoencoding of Vibrational Spectroscopy Data Atomic-scale features, such as step edges and adatoms, play key roles in metal-molecule interactions and are critically important in heterogeneous catalysis, molecular electronics, and sensing applications. However, the small size and often transient nature of atomic-scale structures make studying such interactions challenging. Here, by combining single-molecule surface-enhanced Raman spectroscopy with machine learning, spectra are extracted of perturbed molecules, revealing the formation dynamics of adatoms in gold and palladium metal surfaces. This provides unique insight into atomic-scale processes, allowing us to resolve where such metallic protrusions form and how they interact with nearby molecules. Our technique paves the way to tailor metal-molecule interactions on an atomic level and assists in rational heterogeneous catalyst design. Atomic units ; Molecule ; Raman spectroscopy ; Chemical physics ; Nanotechnology ; Metal ; Molecular electronics ; Catalysis ; Palladium ; Materials science ; Spectroscopy ; Small molecule ; Chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Guanjie Wang | Zhimei Sun
Source: Science Bulletin (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

Atomic insights into device-scale phase-change memory materials using machine learning potential Atomic units ; Scale (ratio) ; Phase-change memory ; Computer science ; Phase (matter) ; Materials science ; Phase change ; Artificial intelligence ; Engineering physics ; Chemistry ; Engineering ; Physics Machine Learning in Materials Science ; Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing
Authors: Haozhe Hu | Yongquan Jiang | Yan Yang | Jim X. Chen
Source: Applied Intelligence (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

BiG2S: A dual task graph-to-sequence model for the end-to-end template-free reaction prediction Computer science ; Retrosynthetic analysis ; Graph ; Artificial intelligence ; Machine translation ; Task (project management) ; Representation (politics) ; Machine learning ; Outcome (game theory) ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Sebastian Bichelmaier | Jesús Carrete | Ralf Wanzenböck | Florian Buchner | Georg K. H. Madsen
Source: Physical review. B./Physical review. B (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

Neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia Machine learning methods, and in particular neural-network force fields (NNFF), are bridging the gap between macroscopically relevant parameters and quantum mechanical simulations. This work describes an NNFF training strategy and uses it as the back end for an effective harmonic potential study of phase transitions in hafnia. While good agreement with experiment is found regarding the monoclinic-tetragonal transition, the commonly assumed transition to a Fm$\overline{3}$m cubic phase is found to be unlikely for the stoichiometric material Hafnia ; Bridging (networking) ; Artificial neural network ; Tetragonal crystal system ; Phase transition ; Monoclinic crystal system ; Condensed matter physics ; Harmonic ; Materials science ; Quantum phase transition ; Statistical physics ; Phase (matter) ; Physics ; Quantum mechanics ; Computer science ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Semiconductor materials and devices
Authors: Xiao Fei | Muhammad Saqib | Soha Razzaq | Tayyaba Mubashir | Mudassir Hussain Tahir | Ihab Mohamed Moussa | Hosam O. Elansary
Source: Journal of Molecular Modeling (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Performance prediction of polymer-fullerene organic solar cells and data mining-assisted designing of new polymers Cheminformatics ; Computer science ; Organic solar cell ; Similarity (geometry) ; Random forest ; Machine learning ; Artificial intelligence ; Molecular descriptor ; Quantitative structure–activity relationship ; Set (abstract data type) ; Deep learning ; Data mining ; Polymer ; Biochemical engineering ; Materials science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: Tim Kircher | Felix Döppel | Martin Votsmeier
Source: ChemRxiv (preprint, 2023-09-14)
Field: Materials Science — Materials Chemistry

A neural network with embedded stoichiometry and thermodynamics for learningkinetics from reactor data The digitalization of chemical research and industry is vastly increasing the available data for developing and parametrizing kinetic models. To exploit this data, machine learning approaches are needed that autonomously learn kinetic models from large amounts of reactor data. In this paper we develop such a tool. We present a neural network architecture that embeds thermodynamic and stoichiometric prior knowledge (STeNN) for the accurate, robust and data-efficient modelling of chemical kinetics. This network architecture is used in conjunction with neural ODEs to autonomously learn kinetic models directly from reactor data. Using the example of an adiabatic steam reformer, we demonstrate that our approach accurately recovers the true kinetics from reactor data, where conventional neural networks fail. It is further shown that the proposed framework can handle large datasets and learns kinetic models from up to 1000 reactor experiments in around ten minutes. Furthermore, due to the embedded physico-chemical knowledge, our model is robust to significant noise in the data, even in the low-data regime. We anticipate that our approach, in combination with emerging big data frameworks, will greatly increase the availability of accurate kinetic models, providing a boost to model-based reactor design and control. Artificial neural network ; Computer science ; Chemical reactor ; Adiabatic process ; Machine learning ; Thermodynamics ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Process Optimization and Integration
Authors: Jingyi Hu | Xiang Xu | Yongcheng Cui | Mingxian Xu | Xiaojin Gao | Xiaomei Ji
Source: Journal of Non-Crystalline Solids (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

An ensemble learning based amorphous state predictor for multicomponent alloys Overfitting ; Machine learning ; Ensemble learning ; Computer science ; Alloy ; Amorphous metal ; Artificial neural network ; Random forest ; Artificial intelligence ; Materials science ; Amorphous solid ; Metallurgy Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys ; Phase-change materials and chalcogenides
Authors: Fang Wang | Zhi Yang | Fenglian Li | Jian-Li Shao | Li-Chun Xu
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Strategic sampling with stochastic surface walking for machine learning force fields in iron's bcc–hcp phase transitions This study developed a machine learning-based force field for simulating the bcc-hcp phase transitions of iron. By employing traditional molecular dynamics sampling methods and stochastic surface walking sampling methods, combined with Bayesian inference, we construct an efficient machine learning potential for iron. By using SOAP descriptors to map structural data, we find that the machine learning force field exhibits good coverage in the phase transition space. Accuracy evaluation shows that the machine learning force field has small errors compared to DFT calculations in terms of energy, force, and stress evaluations, indicating excellent reproducibility. Additionally, the machine learning force field accurately predicts the stable crystal structure parameters, elastic constants, and bulk modulus of bcc and hcp phases of iron, and demonstrates good performance in predicting higher-order derivatives and phase transition processes, as evidenced by comparisons with DFT calculations and existing experimental data. Therefore, our study provides an effective tool for investigating the phase transitions of iron using machine learning methods, offering new insights and approaches for materials science and solid-state physics research. Sampling (signal processing) ; Surface (topology) ; Phase (matter) ; Phase transition ; Materials science ; Computer science ; Physics ; Mathematics ; Condensed matter physics ; Computer vision ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Carlos Manuel de Armas-Morejón | Luís A. Montero | Ángel Rubio | Joaquim Jornet-Somoza
Source: Journal of Chemical Theory and Computation (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Electronic Descriptors for Supervised Spectroscopic Predictions Spectroscopic properties of molecules hold great importance for the description of the molecular response under the effect of UV/vis electromagnetic radiation. Computationally expensive <i>ab initio</i> (e.g., MultiConfigurational SCF, Coupled Cluster) or TDDFT methods are commonly used by the quantum chemistry community to compute these properties. In this work, we propose a (supervised) Machine Learning approach to model the absorption spectra of organic molecules. Several supervised ML methods have been tested such as Kernel Ridge Regression (KRR), Multiperceptron Neural Networs (MLP), and Convolutional Neural Networks. [Ramakrishnan et al. <i>J. Chem. Phys.</i> 2015, 143, 084111. Ghosh et al. <i>Adv. Sci.</i> 2019, 6, 1801367.] The use of only geometrical-atomic number descriptors (e.g., Coulomb Matrix) proved to be insufficient for an accurate training. [Ramakrishnan et al. <i>J. Chem. Phys.</i> 2015, 143, 084111.] Inspired by the TDDFT theory, we propose to use a set of electronic descriptors obtained from low-cost DFT methods: orbital energy differences (Δϵ<sub><i>ia</i></sub> = ϵ<sub><i>a</i></sub> - ϵ<sub><i>i</i></sub>), transition dipole moment between occupied and unoccupied Kohn-Sham orbitals (⟨ϕ<sub><i>i</i></sub>|<i>r</i>|ϕ<sub><i>a</i></sub>⟩), and when relevant, charge-transfer character of monoexcitations (<i>R</i><sub><i>ia</i></sub>). We demonstrate that with these electronic descriptors and the use of Neural Networks we can predict not only a density of excited states but also get a very good estimation of the absorption spectrum and charge-transfer character of the electronic excited states, reaching results close to chemical accuracy (∼2 kcal/mol or ∼0.1 eV). Coupled cluster ; Time-dependent density functional theory ; Excited state ; Ab initio ; Electronic structure ; Density functional theory ; Atomic orbital ; Chemistry ; Dipole ; Physics ; Molecular physics ; Artificial intelligence ; Computational chemistry ; Computer science ; Atomic physics ; Molecule ; Quantum mechanics ; Electron Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yudai Yamaguchi | Taruto Atsumi | Kenta Kanamori | Naoto Tanibata | Hayami Takeda | Masanobu Nakayama | Masayuki Karasuyama | Ichiro Takeuchi
Source: Scientific Reports (article, 2023-10-05)
Field: Materials Science — Materials Chemistry

Drawing a materials map with an autoencoder for lithium ionic conductors Efforts to optimize known materials and enhance their performance are ongoing, driven by the advancements resulting from the discovery of novel functional materials. Traditionally, the search for and optimization of functional materials has relied on the experience and intuition of specialized researchers. However, materials informatics (MI), which integrates materials data and machine learning, has frequently been used to realize systematic and efficient materials exploration without depending on manual tasks. Nonetheless, the discovery of new materials using MI remains challenging. In this study, we propose a method for the discovery of materials outside the scope of existing databases by combining MI with the experience and intuition of researchers. Specifically, we designed a two-dimensional map that plots known materials data based on their composition and structure, facilitating researchers' intuitive search for new materials. The materials map was implemented using an autoencoder-based neural network. We focused on the conductivity of 708 lithium oxide materials and considered the correlation with migration energy (ME), an index of lithium-ion conductivity. The distribution of existing data reflected in the materials map can contribute to the development of new lithium-ion conductive materials by enhancing the experience and intuition of material researchers. Lithium (medication) ; Autoencoder ; Ionic bonding ; Electrical conductor ; Computer science ; Materials science ; Ion ; Artificial intelligence ; Medicine ; Chemistry ; Internal medicine ; Composite material ; Deep learning Machine Learning in Materials Science ; Recycling and Waste Management Techniques ; VLSI and FPGA Design Techniques
Authors: Isak Falk | Leo H. Bonati | Pietro Novelli | M. Parinello | Massimiliano Pontil
Source: arXiv (Cornell University) (preprint, 2023-06-02)
Field: Materials Science — Materials Chemistry

Transfer learning for atomistic simulations using GNNs and kernel mean embeddings Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, accurate models require large training datasets, while generating reference calculations is computationally demanding. To bypass this difficulty, we propose a transfer learning algorithm that leverages the ability of graph neural networks (GNNs) to represent chemical environments together with kernel mean embeddings. We extract a feature map from GNNs pre-trained on the OC20 dataset and use it to learn the potential energy surface from system-specific datasets of catalytic processes. Our method is further enhanced by incorporating into the kernel the chemical species information, resulting in improved performance and interpretability. We test our approach on a series of realistic datasets of increasing complexity, showing excellent generalization and transferability performance, and improving on methods that rely on GNNs or ridge regression alone, as well as similar fine-tuning approaches. Interpretability ; Computer science ; Machine learning ; Artificial intelligence ; Kernel (algebra) ; Generalization ; Transferability ; Feature (linguistics) ; Graph ; Transfer of learning ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Aleksandr Zaichenko | Andreas J. Achazi | Simon Kunz | Hermann A. Wegner | Jürgen Janek | Doreen Mollenhauer
Source: Progress in Energy (article, 2023-11-02)
Field: Chemistry — Organic Chemistry

Static theoretical investigations of organic redox active materials for redox flow batteries Abstract New efficient redox flow batteries (RFBs) are currently of great interest for large-scale storage of renewable energy. Further development requires the improvement of the redox active materials. Quantum chemical calculations allow the screening of large numbers of redox active molecules for required static molecular properties. In particular, redox potentials are calculated in high-throughput studies. In addition, calculations of solubility and reactivity and in-depth electronic structure analysis are performed for smaller numbers of molecules. In this review, we provide an overview of the static theoretical investigations carried out on the known classes of molecules that are considered as redox active materials in RFBs. We will focus on electronic structure methods such as density functional theory and wave function-based methods. Furthermore, investigations using the increasingly important machine learning techniques are presented. For each class of redox active molecules considered, significant theoretical results are presented and discussed. In addition, the different quantum chemical approaches used are examined, in particular with regard to their advantages and limitations. Another focus of this review is the comparison of theoretically predicted results with available experimental studies. Finally, future challenges and trends in the theoretical studies of redox active materials are highlighted. Redox ; Molecule ; Density functional theory ; Chemistry ; Chemical physics ; Quantum ; Nanotechnology ; Electronic structure ; Materials science ; Computational chemistry ; Inorganic chemistry ; Organic chemistry ; Physics Free Radicals and Antioxidants ; Advanced battery technologies research ; Photochemistry and Electron Transfer Studies
Authors: Mila AI Science | : | Alex Hernandez-Garcia | Alexandre Duval | Alexandra Volokhova | Yoshua Bengio | Divya Sharma | Pierre Carrier | Benabed, Yasmine | Michał Koziarski | Victor Schmidt | Rignanese, Gian-Marco | De Breuck, Pierre-Paul | Clancy, Paulette
Source: ArXiv.org (preprint, 2023-10-07)
Field: Materials Science — Materials Chemistry

Crystal-GFN: sampling crystals with desirable properties and constraints The discovery of novel solid-state materials, such as electrocatalysts, super-ionic conductors, or photovoltaic materials, plays a critical role in addressing various global challenges. It has, for instance, the potential to significantly improve the efficiency of renewable energy production and storage, thereby making substantial contributions to climate crisis mitigation strategies. In this paper, we introduce Crystal-GFN, a generative model of crystal structures possessing desirable properties and constraints. Operating as a multi-environment, continuous-discrete GFlowNet, it sequentially samples structural attributes of crystalline materials, namely space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physicochemical and geometric hard constraints. We demonstrate the capabilities of Crystal-GFN to efficiently discover diverse and valid crystals with various properties: low predicted formation energy (median -3.2 eV/atom), band gap close to a target value and high density. Overall, Crystal-GFN is a crystal generation method that addresses several existing challenges in the literature and opens promising paths for accelerating materials discovery with machine learning. Crystal structure prediction ; Materials science ; Crystal structure ; Crystal (programming language) ; Renewable energy ; Ionic bonding ; Atom (system on chip) ; Nanotechnology ; Chemical physics ; Computer science ; Crystallography ; Ion ; Chemistry ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: Yong Ju Lee | Ji Eun Lee | Jaegyoung Gwon | Tai Ju Lee | Hyoung Jin Kim
Source: Journal of Korea Technical Association of The Pulp and Paper Industry (article, 2023-10-30)
Field: Chemistry — Analytical Chemistry

Predictive Modeling for Degree of Substitution of Cellulose Acetate using Infrared Spectroscopy and Machine Learning The objective of this study is to apply FTIR and machine learning models for the quantitative analysis of the degree of substitution of cellulose acetate. The models used for the degree of substitution analysis include PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), SVM (support vector machine), and KNN (k-nearest neighbor). The critical findings of this study indicated that it is possible to analyze the degree of substitution for cellulose acetate with a degree of substitution of 2.0 or less using IR spectrum data derived from acetylation, estimated through PCA. The decrease in explanatory power for degrees of substitution higher than 2.0 can be attributed to the chemical reaction rate. However, by applying SVM and utilizing the kernel trick to project the data into a high-dimensional feature space and perform non-linear classification, it was possible to create a degree of substitution discrimination model with excellent performance, regardless of the degree of substitution. As a result, the model for analyzing the degree of substitution of polymer monomers based on machine learning and IR spectrum data was proposed. It is believed that this model can efficiently replace existing analytical methods. Substitution (logic) ; Degree (music) ; Support vector machine ; Principal component analysis ; Linear discriminant analysis ; Substitution method ; Kernel (algebra) ; Artificial intelligence ; Partial least squares regression ; Computer science ; Infrared spectroscopy ; Chemistry ; Mathematics ; Biological system ; Machine learning ; Pattern recognition (psychology) ; Organic chemistry ; Physics ; Combinatorics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Lucas Almir Cavalcante Minho | Zenilda de Lourdes Cardeal | Helvécio Costa Menezes
Source: Journal of Separation Science (article, 2023-07-31)
Field: Chemistry — Spectroscopy

A deep learning‐based simulator for comprehensive two‐dimensional GC applications Among the main approaches for predicting the spatial positions of eluates in comprehensive two-dimensional gas chromatography, the still under-explored computational models based on deep learning algorithms emerge as robust and reliable options due to their high adaptability to the structure and complexity of the data. In this work, an open-source program based on deep neural networks was developed to optimize chromatographic methods and simulate operating conditions outside the laboratory. The deep neural networks models were fit to convenient experimental predictors, resulting in scaled losses (mean squared error) equivalent to 0.006 (relative average deviation = 8.56%, R<sup>2</sup> = 0.9202) and 0.014 (relative average deviation = 1.67%, R<sup>2</sup> = 0.8009) in the prediction of the first- and second-dimension retention times, respectively. Good compliance was observed for the main chemical classes, such as environmental contaminants: volatile, semivolatile organic compounds, and pesticides; biochemistry molecules: amino acids and lipids; pharmaceutical industry and personal care products and residues: drugs and metabolites; among others. On the other hand, there is a need for continuous database updates to predict retention times of less common compounds accurately. Thus, forming a collaborative database is proposed, gathering voluntary findings from other users. Adaptability ; Artificial neural network ; Deep learning ; Computer science ; Mean squared error ; Artificial intelligence ; Machine learning ; Chemistry ; Mathematics ; Statistics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies