jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pratik Dhakal | Wyatt Gassaway | Jindal K. Shah
Source: ChemRxiv (preprint, 2023-04-24)
Field: Materials Science — Materials Chemistry

Mapping the Frontier Orbital Energies of Imidazolium-based Cations Using Machine Learning The knowledge of frontier orbital, highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), energies is vital for studying chemical and electrochemical stability of compounds, their corrosion inhibition potential, reactivity, etc. Density functional theory (DFT) calculations provide a direct route to estimate these energies, either in the gas-phase or condensed phase. However, the application of DFT methods becomes computationally intensive when hundreds of thousands of compounds are to be screened. Such is the case when all the isomers for the 1-alkyl-3-alkyllimidazolium cation [C$_n$C$_m$im]$^+$ (n = 1-10, m=1-10) are considered. Enumerating the isomer space of [C$_n$C$_m$im]$^+$ yields close to 316,000 cation structures. Calculating frontier orbital energies for each would be computationally very expensive and time-consuming using DFT. In this article, we develop a machine learning model based on extreme gradient boosting (XGBoost) method using the a small subset of the isomer space and predict the HOMO and LUMO. Using the model, the HOMO energies are predicted with a mean-absolute error (MAE) of 0.4 eV and the LUMO energies with MAE of 0.2 eV. Inferences are also drawn on type of the descriptors deemed important for the HOMO and LUMO energy estimates. Application of the machine learning model results in a drastic reduction in computational time required for such calculations. HOMO/LUMO ; Density functional theory ; Molecular orbital ; Phase space ; Chemistry ; Computational chemistry ; Chemical space ; Physics ; Molecule ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Z.Y. Ma | Zhiliang Pan
Source: Research Square (preprint, 2023-05-03)
Field: Materials Science — Materials Chemistry

Efficient Machine Learning of Solute Segregation Energy Based on Physics-informed Features Abstract Machine learning models solute segregation energy based on appropriate features of segregation sites. Lumping many features together can give a decent accuracy but may suffer the curse of dimensionality. Here, we modeled the segregation energy with efficient machine learning using physics-informed features identified based on solid physical understanding. The features outperform the many features used in the literature work and the spectral neighbor analysis potential features by giving the best balance between accuracy and feature dimension, with the extent depending on machine learning algorithms and alloy systems. The excellence is attributed to the strong relevance to segregation energies and the mutual independence ensured by physics. In addition, the physics-informed features contain much less redundant information originating from the energy-only-concerned calculations in equilibrium states. This work showcases the merit of integrating physics in machine learning from the perspective of feature identification other than that of physics-informed machine learning algorithms. Curse of dimensionality ; Machine learning ; Artificial intelligence ; Feature (linguistics) ; Energy (signal processing) ; Dimension (graph theory) ; Computer science ; Relevance (law) ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electronic and Structural Properties of Oxides
Authors: Yu Kitamura | Daisuke Tanaka
Source: Nihon Kessho Gakkaishi (article, 2023-05-31)
Field: Chemistry — Inorganic Chemistry

Machine-Learning-Assisted Synthetic Exploration of Metal-Organic Frameworks In this review article, the authors present their recent work on efficiently synthesizing new metal-organic frameworks （MOFs） and optimizing their functionality by utilizing machine learning techniques. We successfully synthesized a series of new anionic Ln-BDC-MOFs by focusing on the unexplored chemical reaction space. We also adopted the Bayesian optimization technique to optimize the stoichiometric ratio of metal-salts in Ln-MOF, and successfully synthesized MIL-103 （Ln（BTB）（H2O）, H3BTB＝1,3,5-tris（4-carboxyphenyl）benzene）, which emits white light. These synthetic exploration approach can significantly reduce the experimental effort required to discover new materials. Bayesian optimization ; Metal-organic framework ; Materials science ; Stoichiometry ; Benzene ; Metal ; Nanotechnology ; Computer science ; Artificial intelligence ; Organic chemistry ; Chemistry ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Dong Zhu | Zhikuang xin | Siming Zheng | Yangang Wang | Xiaoyu Yang
Source: arXiv (Cornell University) (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Addressing the Accuracy-Cost Tradeoff in Material Property Prediction: A Teacher-Student Strategy Deep learning has revolutionized the process of new material discovery, with state-of-the-art models now able to predict material properties based solely on chemical compositions, thus eliminating the necessity for material structures. However, this cost-effective method has led to a trade-off in model accuracy. Specifically, the accuracy of Chemical Composition-based Property Prediction Models (CPMs) significantly lags behind that of Structure-based Property Prediction Models (SPMs). To tackle this challenge, we propose an innovative Teacher-Student (T-S) strategy, where a pre-trained SPM serves as the 'teacher' to enhance the accuracy of the CPM. Leveraging the T-S strategy, T-S CrabNet has risen to become the most accurate model among current CPMs. Initially, we demonstrated the universality of this strategy. On the Materials Project (MP) and Jarvis datasets, we validated the effectiveness of the T-S strategy in boosting the accuracy of CPMs with two distinct network structures, namely CrabNet and Roost. This led to CrabNet, under the guidance of the T-S strategy, emerging as the most accurate model among the current CPMs. Moreover, this strategy shows remarkable efficacy in small datasets. When predicting the formation energy on a small MP dataset comprising merely 5% of the samples, the T-S strategy boosted CrabNet's accuracy by 37.1%, exceeding the enhancement effect of the T-S strategy on the whole dataset. Boosting (machine learning) ; Computer science ; Property (philosophy) ; Universality (dynamical systems) ; Process (computing) ; Machine learning ; Artificial intelligence ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jodie A. Yuwono | Xinyu Li | Tyler D. Doležal | Adib J. Samin | Qinfeng Shi | Zhipeng Li | N. Birbilis
Source: arXiv (Cornell University) (preprint, 2023-05-20)
Field: Materials Science — Materials Chemistry

A Computational Approach for Mapping Electrochemical Activity of Multi-Principal Element Alloys Multi principal element alloys (MPEAs) comprise a unique class of metal alloys. MPEAs have been demonstrated to possess several exceptional properties, including, as most relevant to the present study, a high corrosion resistance. In the context of MPEA design, the vast number of potential alloying elements and the staggering number of elemental combinations favours a computational alloy design approach. In order to computationally assess the prospective corrosion performance of MPEA, an approach was developed in this study. A density functional theory (DFT) based Monte Carlo method was used for the development of MPEA structure, with the AlCrTiV alloy used as a model. High-throughput DFT calculations were performed to create training datasets for surface activity towards different adsorbate species: O2-, Cl- and H+. Machine learning (ML) with combined representation was then utilised to predict the adsorption and vacancy energies as descriptors for surface activity. The capability of the combined computational methods of MC, DFT and ML, as a virtual electrochemical performance simulator for MPEAs was established and may be useful in exploring other MPEAs. Density functional theory ; Context (archaeology) ; Corrosion ; Materials science ; Electrochemistry ; Alloy ; Representation (politics) ; Vacancy defect ; Adsorption ; Monte Carlo method ; Computer science ; Computational science ; Computational chemistry ; Metallurgy ; Chemistry ; Mathematics ; Physical chemistry ; Crystallography Corrosion Behavior and Inhibition ; Electrocatalysts for Energy Conversion ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Isaiah A. Moses | Wesley F. Reinhart
Source: arXiv (Cornell University) (preprint, 2023-10-11)
Field: Materials Science — Materials Chemistry

Codes: Quantitative Analysis of MoS$_2$ Thin Film Micrographs with Machine Learning Isolating the features associated with different materials growth conditions is important to facilitate the tuning of these conditions for effective materials growth and characterization. This study presents machine learning models for classifying atomic force microscopy (AFM) images of thin film MoS$_2$ based on their growth temperatures. By employing nine different algorithms and leveraging transfer learning through a pretrained ResNet model, we identify an effective approach for accurately discerning the characteristics related to growth temperature within the AFM micrographs. Robust models with up to 70% test accuracies were obtained, with the best performing algorithm being an end-to-end ResNet fine-tuned on our image domain. Class activation maps and occlusion attribution reveal that crystal quality and domain boundaries play crucial roles in classification, with models exhibiting the ability to identify latent features beyond human visual perception. Overall, the models demonstrated high accuracy in identifying thin films grown at different temperatures despite limited and imbalanced training data as well as variation in growth parameters besides temperature, showing that our models and training protocols are suitable for this and similar predictive tasks for accelerated 2D materials characterization. Characterization (materials science) ; Artificial intelligence ; Atomic force microscopy ; Computer science ; Domain (mathematical analysis) ; Materials science ; Machine learning ; Pattern recognition (psychology) ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Hydrogen embrittlement and corrosion behaviors in metals
Source: Angewandte Chemie International Edition (article, 2023-03-24)
Field: Chemistry — Organic Chemistry

A Team for Accelerating Catalyst and Reaction Developmen This invited Team Profile was created by the Gessner and Gooßen research groups at Ruhr-University Bochum (Germany). They recently published a series of articles in Angewandte Chemie on the application of ylide-substituted phosphines (YPhos) in palladium-catalyzed coupling reactions. Owing to their inherently strong sigma-donating ability and their modular synthesis, YPhos ligands can be tailored to fit the demands of various catalytic transformations. In the most recent article, machine learning methods were applied to identify a YPhos catalyst that enables the previously elusive Hiyama coupling of aryl chlorides. "Computer-Driven Development of Ylide Functionalized Phosphines for Palladium-Catalyzed Hiyama Couplings", J. F. Goebel, J. Löffler, Z. Zeng, J. Handelmann, A. Hermann, I. Rodstein, T. Gensch, V. H. Gessner, L. J. Gooßen, Angew. Chem. Int. Ed. 2023, e202216160; Angew. Chem. 2023, e202216160. Catalysis ; Ylide ; Aryl ; Chemistry ; Palladium ; Coupling reaction ; Combinatorial chemistry ; Medicinal chemistry ; Polymer chemistry ; Organic chemistry Catalytic Cross-Coupling Reactions
Authors: Raphaël Boichot | Elise Garel | Jean-Luc Parouty | H.P. Van Landeghem | M. Verdier | F. Robaut | Stéphane Coindeau
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Coupling Mixture Designs, High-Throughput Experiments and Machine Learning for Accelerated Exploration of Multinary Systems Coupling (piping) ; Throughput ; Computer science ; Artificial intelligence ; Materials science ; Machine learning ; Metallurgy ; Operating system Machine Learning in Materials Science
Authors: Enhu Diao | Yurong He | Xuhong Liu | Qiang Tong | Tao Yang | Xiaotong Liu | James P. Lewis
Source: Frontiers in Quantum Science and Technology (article, 2023-05-25)
Field: Materials Science — Materials Chemistry

First principles data-driven potentials for prediction of iron carbide clusters Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance to poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and the prediction of hydrogen adsorption sites on cluster surfaces. We achieve more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations. Adaptability ; Cluster (spacecraft) ; Throughput ; Density functional theory ; Computer science ; Stability (learning theory) ; Quantum ; Carbide ; Coupled cluster ; Quantum chemistry ; Materials science ; Chemistry ; Computational chemistry ; Machine learning ; Catalysis ; Molecule ; Physics ; Reaction mechanism ; Quantum mechanics Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; X-ray Diffraction in Crystallography
Authors: Artem Fediai | Patrick Reiser | Jorge Enrique Olivares Peña | Pascal Friederich | Wolfgang Wenzel
Source: arXiv (Cornell University) (preprint, 2023-03-15)
Field: Materials Science — Materials Chemistry

Accurate GW frontier orbital energies of 134 kilo molecules The QM9 dataset [Scientific Data, Vol. 1, 140022 (2014)] became a standard dataset to benchmark machine learning methods, especially on molecular graphs. It contains geometries as well as multiple computed molecular properties of 133,885 compounds at B3LYP/6-31G(2df,p) level of theory, including frontier orbitals (HOMO and LUMO) energies. However, the accuracy of HOMO/LUMO predictions from density functional theory, including hybrid methods such as B3LYP, is limited for many applications. In contrast, the GW method significantly improves HOMO/LUMO prediction accuracy, with mean unsigned errors in the GW100 benchmark dataset of 100 meV. In this work, we present a new dataset of HOMO/LUMO energies for the QM9 compounds, computed using the GW method. This database may serve as a benchmark of HOMO/LUMO prediction, delta-learning, and transfer learning, particularly for larger molecules where GW is the most accurate but still numerically feasible method. We expect this dataset to enable the development of more accurate machine learning models for predicting molecular properties HOMO/LUMO ; Benchmark (surveying) ; Atomic orbital ; Density functional theory ; Molecular orbital ; Molecule ; Physics ; Computer science ; Computational chemistry ; Artificial intelligence ; Machine learning ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Krystal Vasquez
Source: C&EN Global Enterprise (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

New tool detects AI-generated chemistry papers Chemists from the University of Kansas have developed a machine learning tool that can detect with at least 92% accuracy when the introductions of chemistry papers are written using ChatGPT ( Cell Rep. Phys. Sci. 2023, DOI: 10.1016/j.xcrp.2023.101672 ). To distinguish between human- and AI-generated text, the tool uses a machine learning algorithm to analyze 20 writing style features, such as the presence of common punctuation marks and the inclusion of some chemistry-specific terminology. The new detector was trained and tested on the introductions of papers published in 10 journals from the American Chemical Society and introductions generated by ChatGPT based on the ACS papers’ titles or abstracts. ACS publishes C&EN. The researchers say they focused on the introductory sections since that’s the part of a paper that authors are most likely to write with an AI text generator. “The reason why we developed [this tool] is because we wanted Terminology ; Punctuation ; Computer science ; Artificial intelligence ; Style (visual arts) ; Chemistry ; Natural language processing ; Library science ; Linguistics ; Art ; Literature ; Philosophy Machine Learning in Materials Science ; Academic Writing and Publishing ; Biomedical Text Mining and Ontologies
Authors: Kyohei Noda | Hisanao Takahashi | Koji Tsuda | M. Hiroshima
Source: Transactions of the Japanese Society for Artificial Intelligence (article, 2023-02-28)
Field: Materials Science — Materials Chemistry

Prediction of Material Properties of Inorganic Compounds Using Self-Attention Network Due to the increase in material databases in recent years, there has been a lot of research regarding deep learning models which use large sizes of datasets and are aimed at the prediction of the material properties of inorganic compounds. Particularly, prediction models with Self-Attention structures, such as Roost and CrabNet, have garnered attention because of two reasons: (1) input variables are confined to the chemical composition of each formula and (2) Self-Attention enables models to learn individual element representations based on their chemical environment. However, the existing Self- Attention model yields low prediction accuracy when predicting structure-dependent material properties, such as the magnetic moment, for lack of structural information of compounds as input. In this research, based on the existing Self- Attention model, we set both elemental and structural information, especially the space group number and lattice constant, as input information and successfully construct a prediction model that is more versatile than existing methods. Furthermore, we visualized lists of promising materials by adopting Bayesian optimization. As a result, we have developed a system to propose desired materials for materials researchers. Computer science ; Bayesian network ; Construct (python library) ; Set (abstract data type) ; Machine learning ; Data mining ; Artificial intelligence ; Chemical space ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Batzner, Simon Lutz
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Equivariant Deep Learning Interatomic Potentials Simulating the quantum-mechanical behavior of matter is at the core of the computational design of novel molecules and molecules. First-principles methods provide high fidelity, but scale poorly with the number of electrons, thereby greatly limiting the accessible length-scales and time-scales. This effect is particularly pronounced in molecular dynamics where due to short required integration time steps, millions or even billions of steps are required for the computation of structural and kinetic observables. Machine learning interatomic potentials aim to move past this dilemma, by learning to regress the energies, forces, and stresses of accurate first-principles calculations and providing an energy model that scales linearly with the number of atoms. Representations of the atomistic geometry must obey the symmetries of 3D space, translation, rotation, and mirrors, which together comprise the Euclidean group E(3). For decades, interatomic potentials have represented the atomistic geometry as invariants of the geometry, typically distances and angles, thereby automatically satisfying the required symmetries. In this thesis, I present efforts to generalize these invariant representations to equivariant ones, that directly operate on the relative interatomic positions and more faithfully represent the atomistic geometry. I demonstrate that this leads to large improvements in generalization, robustness, transferability, as well as order-of-magnitude improvements in sample efficiency of the learned potentials. I then demonstrate how to move from atom-centered message passing interatomic potentials, the dominant approach in deep learning interatomic potentials, to strictly local deep learning interatomic potentials. These retain the accuracy and transferability of message-passing interatomic potentials, but due to their local nature can be scaled to large-scale simulations. I demonstrate the scalability of the strictly local potential on a molecular dynamics simulation of more than 100 million atoms. The work demonstrates how leveraging the symmetry of 3D space can lead to fundamental advances in machine learning interatomic potentials. Interatomic potential ; Computation ; Invariant (physics) ; Molecular dynamics ; Euclidean geometry ; Polyhedron ; Equivariant map ; Scale invariance Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum many-body systems
Authors: Jason R. Hall | Taylor D. Sparks
Source: Integrating materials and manufacturing innovation (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

A Case Study of Beta-Variational Auto-encoders, Disentanglement Impacts of Input Distribution and Beta-Variation Based Upon a Computational Multi-modal Particle Packing Simulation Modal ; Encoder ; BETA (programming language) ; Computer science ; Beta distribution ; Artificial neural network ; Variation (astronomy) ; Work (physics) ; Field (mathematics) ; Algorithm ; Distribution (mathematics) ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Statistics ; Engineering ; Mathematical analysis ; Physics ; Mechanical engineering Machine Learning in Materials Science ; Integrated Circuits and Semiconductor Failure Analysis ; Electron and X-Ray Spectroscopy Techniques
Authors: Stefan Hödl | William H. Robinson | Yoram Bachrach | Wilhelm T. S. Huck | Tal Kachman
Source: arXiv (Cornell University) (preprint, 2023-05-25)
Field: Materials Science — Materials Chemistry

Explainability Techniques for Chemical Language Models Explainability techniques are crucial in gaining insights into the reasons behind the predictions of deep learning models, which have not yet been applied to chemical language models. We propose an explainable AI technique that attributes the importance of individual atoms towards the predictions made by these models. Our method backpropagates the relevance information towards the chemical input string and visualizes the importance of individual atoms. We focus on self-attention Transformers operating on molecular string representations and leverage a pretrained encoder for finetuning. We showcase the method by predicting and visualizing solubility in water and organic solvents. We achieve competitive model performance while obtaining interpretable predictions, which we use to inspect the pretrained model. Leverage (statistics) ; Computer science ; Transformer ; Relevance (law) ; String (physics) ; Language model ; Artificial intelligence ; Encoder ; Deep learning ; Machine learning ; Natural language processing ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods