jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sakhinana Sagar Srinivas | Rajat Kumar Sarkar | Sreeja Gangasani | Venkataramana Runkana
Source: arXiv (Cornell University) (preprint, 2024-08-21)
Field: Materials Science — Materials Chemistry

Vision HgNN: An Electron-Micrograph is Worth Hypergraph of Hypernodes Material characterization using electron micrographs is a crucial but challenging task with applications in various fields, such as semiconductors, quantum materials, batteries, etc. The challenges in categorizing electron micrographs include but are not limited to the complexity of patterns, high level of detail, and imbalanced data distribution(long-tail distribution). Existing methods have difficulty in modeling the complex relational structure in electron micrographs, hindering their ability to effectively capture the complex relationships between different spatial regions of micrographs. We propose a hypergraph neural network(HgNN) backbone architecture, a conceptually alternative approach, to better model the complex relationships in electron micrographs and improve material characterization accuracy. By utilizing cost-effective GPU hardware, our proposed framework outperforms popular baselines. The results of the ablation studies demonstrate that the proposed framework is effective in achieving state-of-the-art performance on benchmark datasets and efficient in terms of computational and memory requirements for handling large-scale electron micrograph-based datasets. Micrograph ; Hypergraph ; Electron micrographs ; Artificial intelligence ; Computer science ; Mathematics ; Computer vision ; Electron microscope ; Optics ; Physics ; Combinatorics ; Scanning electron microscope Machine Learning in Materials Science
Authors: Yoshiki Hasukawa | Fernando Garcia‐Escobar | Keisuke Takahashi | Lauren Takahashi
Source: The Journal of Physical Chemistry C (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts The oxidative coupling of methane reaction is important in the chemical industry from the perspective of an energy source. Although it is difficult to increase C2 yield due to the stability of methane and formation of COx byproducts at high temperatures, informatics has been proven to be a useful approach for effective OCM catalyst design. However, informatics using machine learning in complex environments, such as heterogeneous catalysts, has a black box problem. Therefore, graph theory is applied to the construction of complex networks in order to clarify the relationships between multiple variables and design catalysts based on the analysis of constructed networks. All proposed catalysts are demonstrated to be active in experiments, indicating that catalyst design based on the constructed network is useful and applicable to complex reaction systems. Oxidative coupling of methane ; Catalysis ; Methane ; Representation (politics) ; Coupling (piping) ; Oxidative phosphorylation ; Chemistry ; Environmental science ; Materials science ; Organic chemistry ; Metallurgy ; Biochemistry ; Political science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Hongjun Choi | Sinjini Mitra | Brodksy, Jason | Ruben Glatt | Holmbeck, Erika | Shusen Liu | N. Schunck | Sieverding, Andre | Kyle Wendt
Source: arXiv (Cornell University) (preprint, 2024-04-02)
Field: Materials Science — Materials Chemistry

Learning nuclear cross sections across the chart of nuclides with graph neural networks In this work, we explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime. Our approach follows a two-stage learning framework. First, we apply representation learning to encode cross section data into a latent space using either variational autoencoders (VAEs) or implicit neural representations (INRs). Then, we train graph neural networks (GNNs) on the resulting embeddings to predict missing values across the nuclear chart by leveraging the topological structure of neighboring isotopes. We demonstrate accurate cross section predictions within a 9x9 block of missing nuclei. We also find that the optimal GNN training strategy depends on the type of latent representation used, with VAE embeddings performing best under end-to-end optimization in the original space, while INR embeddings achieve better results when the GNN is trained only in the latent space. Furthermore, using clustering algorithms, we map groups of latent vectors into regions of the nuclear chart and show that VAEs and INRs can discover some of the neutron magic numbers. These findings suggest that deep-learning models based on the representation encoding of cross sections combined with graph neural networks holds significant potential in augmenting nuclear theory models, e.g., by providing reliable estimates of covariances of cross sections, including cross-material covariances. Graph ; Computer science ; Theoretical computer science Graphite, nuclear technology, radiation studies ; Radioactive element chemistry and processing
Authors: Bowen Hou | Jinyuan Wu | Diana Y. Qiu
Source: arXiv (Cornell University) (preprint, 2024-04-22)
Field: Materials Science — Materials Chemistry

Unsupervised Learning of Individual Kohn-Sham States: Interpretable Representations and Consequences for Downstream Predictions of Many-Body Effects Representation learning for the electronic structure problem is a major challenge of machine learning in computational condensed matter and materials physics. Within quantum mechanical first principles approaches, Kohn-Sham density functional theory (DFT) is the preeminent tool for understanding electronic structure, and the high-dimensional wavefunctions calculated in this approach serve as the building block for downstream calculations of correlated many-body excitations and related physical observables. Here, we use variational autoencoders (VAE) for the unsupervised learning of high-dimensional DFT wavefunctions and show that these wavefunctions lie in a low-dimensional manifold within the latent space. Our model autonomously determines the optimal representation of the electronic structure, avoiding limitations due to manual feature engineering and selection in prior work. To demonstrate the utility of the latent space representation of the DFT wavefunction, we use it for the supervised training of neural networks (NN) for downstream prediction of the quasiparticle bandstructures within the GW formalism, which includes many-electron correlations beyond DFT. The GW prediction achieves a low error of 0.11 eV for a combined test set of metals and semiconductors drawn from the Computational 2D Materials Database (C2DB), suggesting that latent space representation captures key physical information from the original data. Finally, we explore the interpretability of the VAE representation and show that the successful representation learning and downstream prediction by our model is derived from the smoothness of the VAE latent space, which also enables the generation of wavefunctions on arbitrary points in latent space. Our work provides a novel and general machine-learning framework for investigating electronic structure and many-body physics. Downstream (manufacturing) ; Kohn–Sham equations ; Psychology ; Physics ; Economics ; Quantum mechanics ; Operations management Machine Learning in Materials Science ; Quantum many-body systems
Authors: Ilia Kevlishvili | Roland St. Michel | Aaron Garrison | Jacob Toney | Husain Adamji | Haojun Jia | Heather J. Kulik
Source: ChemRxiv (preprint, 2024-05-05)
Field: Materials Science — Materials Chemistry

Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes The breadth of transition metal chemical space covered by databases such as the Cambridge Structural Database and the derived computational database tmQM is not conducive to application-specific modeling and the development of structure–property relationships. Here, we employ both supervised and unsupervised natural language processing (NLP) techniques to link experimentally synthesized compounds in the tmQM database to their respective applications. Leveraging NLP models, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism. Analyzing the chemical substructures within each dataset reveals common chemical motifs in each of the designated applications. We then use these common chemical structures to augment our initial datasets for each application, yielding a total of 21,631 compounds in tmCAT, 4,599 in tmPHOTO, 2,782 in tmBIO, and 983 in tmSCO. These datasets are expected to accelerate the more targeted computational screening and development of refined structure–property relationships with machine learning. Transition (genetics) ; Natural (archaeology) ; Computer science ; Transition metal ; Natural language processing ; Linguistics ; Artificial intelligence ; Chemistry ; Geography ; Philosophy ; Archaeology Machine Learning in Materials Science
Authors: Xinyi Fang | Elizabeth Murphy | Phillip Kohl | Youli Li | Craig J. Hawker | Christopher M. Bates | Mengyang Gu
Source: ChemRxiv (preprint, 2024-05-31)
Field: Materials Science — Materials Chemistry

Universal Phase Identification of Block Copolymers from Physics-informed Machine Learning Block copolymers play a vital role in materials science due to their widely studied self-assembly behavior. Traditionally, exploring the phase space of block copolymer self-assembly and associated structure–property relationships involves iterative synthesis, characterization, and theory, which is labor-intensive both experimentally and computationally. Here, we introduce a versatile, high-throughput workflow towards materials discovery that integrates controlled polymerization and automated chromatographic separation with a novel physics-informed machine learning (ML) algorithm for the rapid analysis of small-angle X-ray scattering (SAXS) data. Leveraging the expansive and high-quality experimental datasets generated by automated chromatography, this machine learning method effectively reduces data dimensionality by extracting chemical-independent features from SAXS data. This new approach allows for the rapid and accurate prediction of morphologies without repetitive manual analysis, achieving out-of-sample predictive accuracy of around 95% for both novel and existing materials in the training dataset. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy and achieve near-perfect predictions. In summary, the synergistic combination of controlled synthesis, automated chromatography, and data-driven analysis creates a powerful workflow that markedly expedites the discovery of structure–property relationships in advanced soft materials. Copolymer ; Identification (biology) ; Block (permutation group theory) ; Phase (matter) ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Mathematics ; Nuclear magnetic resonance ; Quantum mechanics ; Biology ; Combinatorics ; Polymer ; Botany Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Nanopore and Nanochannel Transport Studies
Authors: Tim Kircher | Felix Döppel | Martin Votsmeier
Source: ChemRxiv (preprint, 2024-01-09)
Field: Materials Science — Materials Chemistry

Embedding Physics into Neural ODEs to learn Kinetics from Integral Reactors While the digitalization of chemical research and industry is vastly increasing the amount of data for developing kinetic models, model parametrization is not keeping up. To take advantage of the full potential of this data, machine learning tools are required that autonomously learn kinetics from reactor data. Previously, we introduced a neural network with embedded stoichiometry and the thermodynamics for data efficient kinetic modelling. When trained as a neural ODE, this physics embedded neural network discovered kinetics from integral reactor measurements of an equilibrium limited steam reforming reactor whereas conventional neural ODEs failed. We now extend the application to another industrially relevant case of reactors operating at full conversion. Using the preferential oxidation of CO in H2 rich streams as an example, we show that the physics embedded neural network discovers kinetics from stiff systems containing cases of both full conversion and equilibrium limitation using integral reactor data. Ode ; Artificial neural network ; Kinetics ; Embedding ; Parametrization (atmospheric modeling) ; Computer science ; Physics ; Artificial intelligence ; Mathematics ; Applied mathematics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Somayeh Faraji | Mingjie Liu
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Transferable machine learning interatomic potential for carbon hydrogen systems In this study, we developed a machine learning interatomic potential based on artificial neural networks (ANN) to model carbon-hydrogen (C-H) systems. The ANN potential was trained on a dataset of C-H clusters obtained through density functional theory (DFT) calculations. Through comprehensive evaluations against DFT results, including predictions of geometries and formation energies across 0D-3D systems comprising C and C-H, as well as modeling various chemical processes, the ANN potential demonstrated exceptional accuracy and transferability. Its capability to accurately predict lattice dynamics, crucial for stability assessment in crystal structure prediction, was also verified through phonon dispersion analysis. Notably, its accuracy and computational efficiency in calculating force constants facilitated the exploration of complex energy landscapes, leading to the discovery of a novel C polymorph. These results underscore the robustness and versatility of the ANN potential, highlighting its efficacy in advancing computational materials science by conducting precise atomistic simulations on a wide range of C-H materials. Transferability ; Density functional theory ; Crystal structure prediction ; Interatomic potential ; Robustness (evolution) ; Molecular dynamics ; Artificial neural network ; Computer science ; Statistical physics ; Crystal structure ; Artificial intelligence ; Materials science ; Machine learning ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Somayeh Faraji | Mingjie Liu
Source: ChemRxiv (preprint, 2024-06-07)
Field: Materials Science — Materials Chemistry

Transferable Machine Learning Interatomic Potential for Carbon Hydrogen Systems In this study, we developed a machine learning interatomic potential based on artificial neural networks (ANN) to model carbon-hydrogen (C-H) systems. The ANN potential was trained on a dataset of C-H clusters obtained through density functional theory (DFT) calculations. Through comprehensive evaluations against DFT results, including predictions of geometries and formation energies across 0D-3D systems comprising C and C-H, as well as modeling various chemical processes, the ANN potential demonstrated exceptional accuracy and transferability. Its capability to accurately predict lattice dynamics, crucial for stability assessment in crystal structure prediction, was also verified through phonon dispersion analysis. Notably, its accuracy and computational efficiency in calculating force constants facilitated the exploration of complex energy landscapes, leading to the discovery of a novel C polymorph. These results underscore the robustness and versatility of the ANN potential, highlighting its efficacy in advancing computational materials science by conducting precise atomistic simulations on a wide range of C-H materials. Transferability ; Density functional theory ; Crystal structure prediction ; Interatomic potential ; Robustness (evolution) ; Artificial neural network ; Computer science ; Molecular dynamics ; Statistical physics ; Crystal structure ; Artificial intelligence ; Machine learning ; Materials science ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Ethan Berger | Juha Niemelä | Outi Lampela | André H. Juffer | Hannu‐Pekka Komsa
Source: arXiv (Cornell University) (preprint, 2024-01-26)
Field: Chemistry — Spectroscopy

Raman spectra of amino acids and peptides from machine learning polarizabilities Raman spectroscopy is an important tool in the study of vibrational properties and composition of molecules, peptides and even proteins. Raman spectra can be simulated based on the change of the electronic polarizability with vibrations, which can nowadays be efficiently obtained via machine learning models trained on first-principles data. However, the transferability of the models trained on small molecules to larger structures is unclear and direct training on large structures in prohibitively expensive. In this work, we first train two machine learning models to predict polarizabilities of all 20 amino acids. Both models are carefully benchmarked and compared to DFT calculations, with neural network method found to offer better transferability. By combining machine learning models with classical force field molecular dynamics, Raman spectra of all amino acids are also obtained and investigated, showing good agreement with experiments. The models are further extended to small peptides. We find that adding structures containing peptide bonds to the training set greatly improves predictions even for peptides not included in training sets. Raman spectroscopy ; Amino acid ; Chemistry ; Artificial intelligence ; Computer science ; Physics ; Biochemistry ; Optics Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Daniele Angioletti | Stefano Raniolo | Vittorio Limongelli
Source: arXiv (Cornell University) (preprint, 2024-04-25)
Field: Materials Science — Materials Chemistry

HEroBM: a deep equivariant graph neural network for universal backmapping from coarse-grained to all-atom representations Molecular simulations have assumed a paramount role in the fields of chemistry, biology, and material sciences, being able to capture the intricate dynamic properties of systems. Within this realm, coarse-grained (CG) techniques have emerged as invaluable tools to sample large-scale systems and reach extended timescales by simplifying system representation. However, CG approaches come with a trade-off: they sacrifice atomistic details that might hold significant relevance in deciphering the investigated process. Therefore, a recommended approach is to identify key CG conformations and process them using backmapping methods, which retrieve atomistic coordinates. Currently, rule-based methods yield subpar geometries and rely on energy relaxation, resulting in less-than-optimal outcomes. Conversely, machine learning techniques offer higher accuracy but are either limited in transferability between systems or tied to specific CG mappings. In this work, we introduce HEroBM, a dynamic and scalable method that employs deep equivariant graph neural networks and a hierarchical approach to achieve high-resolution backmapping. HEroBM handles any type of CG mapping, offering a versatile and efficient protocol for reconstructing atomistic structures with high accuracy. Focused on local principles, HEroBM spans the entire chemical space and is transferable to systems of varying sizes. We illustrate the versatility of our framework through diverse biological systems, including a complex real-case scenario. Here, our end-to-end backmapping approach accurately generates the atomistic coordinates of a G protein-coupled receptor bound to an organic small molecule within a cholesterol/phospholipid bilayer. Equivariant map ; Graph ; Atom (system on chip) ; Deep neural networks ; Computer science ; Mathematics ; Artificial neural network ; Artificial intelligence ; Theoretical computer science ; Pure mathematics ; Parallel computing Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Graph Neural Networks
Authors: Rebecca Birolo | Rıza Özçelik | Andrea Aramini | Roberto Gobetto | Michele R. Chierotti | Francesca Grisoni
Source: ChemRxiv (preprint, 2024-11-27)
Field: Materials Science — Materials Chemistry

Deep Supramolecular Language Processing for Co-crystal Prediction Approximately 40% of marketed drugs exhibit suboptimal pharmacokinetic profiles. Co-crystallization, where pairs of molecules form a multicomponent crystal, constitutes a promising strategy to enhance physicochemical properties without compromising the pharmacological activity. However, finding promising co-crystal pairs is resource-intensive, due to the vast number of possible combinations. We present DeepCocrystal, a novel deep learning approach designed to predict co-crystal formation by processing the 'chemical language' from a supramolecular vantage point. Rigorous validation of DeepCocrystal showed a balanced accuracy of 78% in realistic scenarios, outperforming existing models. By leveraging properties of molecular string representations, DeepCocrystal can also estimate the uncertainty of its predictions. We harness this capability in a challenging prospective study, and successfully discovered two novel co-crystal of diflunisal, an anti-inflammatory drug. This study underscores the potential of deep learning - and in particular of chemical language processing - to accelerate co-crystallization, and ultimately drug development, in both academic and industrial contexts. Computer science ; Supramolecular chemistry ; Artificial intelligence ; Crystal (programming language) ; Co-occurrence ; Natural language processing ; Crystallography ; Crystal structure ; Chemistry ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrocarbon exploration and reservoir analysis
Authors: Seong-Min Kim | Joshua Schrier | Yousung Jung
Source: ChemRxiv (preprint, 2024-12-09)
Field: Materials Science — Materials Chemistry

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs using Large Language Models We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine-tuned large language models (LLMs) trained on a human-readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive-unlabeled learning model on a text-embedding representation of the structure. An LLM-based workflow can then be used to generate human-readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non-synthesizable hypothetical structures to make them more feasible for materials design. Crystal (programming language) ; Computer science ; Programming language Machine Learning in Materials Science