jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Astrid Klipfel | Yaël Frégier | Adlane Sayede | Zied Bouraoui
Source:  (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Optimized Crystallographic Graph Generation for Material Science Graph neural networks are widely used in machine learning applied to chemistry, and in particular for material science discovery. For crystalline materials, however, generating graph-based representation from geometrical information for neural networks is not a trivial task. The periodicity of crystalline needs efficient implementations to be processed in real-time under a massively parallel environment. With the aim of training graph-based generative models of new material discovery, we propose an efficient tool to generate cutoff graphs and k-nearest-neighbours graphs of periodic structures within GPU optimization. We provide pyMatGraph a Pytorch-compatible framework to generate graphs in real-time during the training of neural network architecture. Our tool can update a graph of a structure, making generative models able to update the geometry and process the updated graph during the forward propagation on the GPU side. Our code is publicly available at https://github.com/aklipf/mat-graph. Computer science ; Graph ; Theoretical computer science ; Implementation ; Artificial neural network ; Data structure ; Generative grammar ; Parallel computing ; Computational science ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ke Chen | Christian Künkel | Bingqing Cheng | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2023-02-20)
Field: Materials Science — Materials Chemistry

Physics-Inspired Machine Learning of Localized Intensive Properties Machine learning (ML) has been widely applied to chemical property prediction, most prominently for the energies and forces in molecules and materials. The strong interest in predicting energies in particular has led to a local energy-based paradigm for modern atomistic ML models, which ensures size-extensivity and a linear scaling of computational cost with system size. However, many electronic properties (such as excitation energies or ionization energies) do not necessarily scale linearly with system size and may even be spatially localized. Using size-extensive models in these cases can lead to large errors. In this work, we explore different strategies for learning intensive and localized properties, using HOMO energies in organic molecules as a representative test case. In particular, we analyze the pooling functions that atomistic neural networks use to predict molecular properties, and suggest an orbital weighted average (OWA) approach that enables the accurate prediction of orbital energies and locations. Pooling ; Scaling ; Statistical physics ; Excitation ; Linear scale ; Work (physics) ; Artificial neural network ; Atomic orbital ; Scale (ratio) ; Computer science ; Ionization ; Physics ; Artificial intelligence ; Machine learning ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Hanan A. Hosni Mahmoud
Source: Crystals (article, 2023-01-02)
Field: Materials Science — Materials Chemistry

Transfer Learning in Inorganic Compounds’ Crystal Structure Classification Deep learning consists of deep convolutional layers and an unsupervised feature selection phase. The feature selection of deep learning on a large size dataset can be employed in correlated prediction models with small size datasets. This methodology is titled deep transfer learning model and enhances prediction model generalization. In this research, we proposed a prediction model for the crystal structure classification of inorganic compounds. Deep learning models in structure classification are usually trained using a large size dataset of 300 K compounds from different quantum compounds dataset (DS1). The feature selection of the deep learning models is reused for selecting features in a small size dataset (with 30 K inorganic compounds and containing 150 different crystal structures) and three alloy classes. The selected features are then fed into a random decision forest prediction model as input. The proposed convolutional neural network (CNN) with transfer learning realizes an accuracy of 98.5%. The experiment results display the CPU time consumed by our model, comparing the time required by similar models. The CPU classification time of the proposed model is 21 s on average. Artificial intelligence ; Transfer of learning ; Convolutional neural network ; Deep learning ; Computer science ; Generalization ; Feature (linguistics) ; Feature selection ; Machine learning ; Pattern recognition (psychology) ; Random forest ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Rakotonirina, V. Diana | Marco Bragato | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2023-05-11)
Field: Materials Science — Materials Chemistry

Hammett-Inspired Product Baseline for Data-efficient $Δ$-ML in Chemical Space Data-hungry machine learning methods have become a new standard to efficiently navigate chemical compound space for molecular and materials design and discovery. Due to the severe scarcity and cost of high-quality experimental or synthetic simulated training data, however, data-acquisition costs can be considerable. Relying on reasonably accurate approximate legacy baseline labels with low computational complexity represents one of the most effective strategies to curb data-needs, e.g.~through $Δ$-, transfer-, or multi-fidelity learning. A surprisingly effective and data-efficient baseline model is presented in the form of a generic coarse-graining Hammett-Inspired Product (HIP) {\em Ansatz}, generalizing the empirical Hammett equation towards arbitrary systems and properties. Numerical evidence for the applicability of HIP includes solvation free energies of molecules, formation energies of quaternary elpasolite crystals, carbon adsorption energies on heterogeneous catalytic surfaces, HOMO-LUMO gaps of metallorganic complexes, activation energies for S$_\text{N}$2 reactions, and catalyst-substrate binding energies in cross-coupling reactions. After calibration on the same training sets, HIP yields an effective baseline for improved $Δ$-machine learning models with superior data-efficiency when compared to previously introduced specialised domain-specific models. Ansatz ; Chemical space ; Granularity ; Multilinear map ; Computer science ; Product (mathematics) ; Space (punctuation) ; Chemistry ; Statistical physics ; Computational chemistry ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Physics ; Quantum mechanics ; Drug discovery Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Evan Walter Clark Spotte‐Smith | Orion Cohen | Samuel M. Blau | Jason M. Munro | Ruoxi Yang | Rishabh D. Guha | Hetal D. Patel | Sudarshan Vijay | Patrick Huck | Ryan Kingsbury | Matthew K. Horton | Kristin A. Persson
Source: ChemRxiv (preprint, 2023-09-18)
Field: Materials Science — Materials Chemistry

A database of molecular properties integrated in the Materials Project Advanced chemical research is increasingly reliant on large computed datasets of molecules and reactions to discover new functional molecules, understand chemical trends, train machine learning models, and more. To be of greatest use to the scientific community, such datasets should follow FAIR principles (i.e. be findable, accessible, interoperable, and reusable). In this work, we present a FAIR expansion of the Materials Project database ("MPcules") that adds more than 170,000 molecules studied using density functional theory (DFT) to the existing data, which comprises crystalline solids. MPcules is a diverse collection of DFT-calculated molecular properties, with an emphasis on reactive, open-shell, and charged species - relevant for studying reaction pathways - and a wide array of structural, electronic, vibrational, and thermodynamic properties. This database can be queried through an OpenAPI-compliant application programming interface and a featureful web application. We continue to expand the data available on MPcules and encourage contributions from the community. Interoperability ; Interface (matter) ; Computer science ; Chemical database ; Database ; Application programming interface ; Density functional theory ; Work (physics) ; Data science ; World Wide Web ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: N. Işık | Ömer Can ESKİCİOĞLU
Source: Open Physics (article, 2023-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Predictability of machine learning framework in cross-section data Abstract Today, the use of artificial intelligence in electron optics, as in many other fields, has begun to increase. In this scope, we present a machine learning framework to predict experimental cross-section data. Our framework includes 8 deep learning models and 13 different machine learning algorithms that learn the fundamental structure of the data. This article aims to develop a machine learning framework to accurately predict double-differential cross-section values. This approach combines multiple models such as convolutional neural networks, machine learning algorithms, and autoencoders to create a more robust prediction system. The data for training the models are obtained from experimental data for different atomic and molecular targets. We developed a methodology for learning tasks, mainly using rigorous prediction error limits. Prediction results show that the machine learning framework can predict the scattering angle and energy of scattering electrons with high accuracy, with an R -squared score of up to 99% and a mean squared error of &lt;0.7. This performance result demonstrates that the proposed machine learning framework can be used to predict electron scattering events, which could be useful for applications such as medical physics. Machine learning ; Artificial intelligence ; Computer science ; Predictability ; Artificial neural network ; Convolutional neural network ; Online machine learning ; Deep learning ; Mean squared error ; Support vector machine ; Algorithm ; Physics ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Michael Alverson | Sterling G. Baird | Ryan Murdock | Sin‐Hang Ho | Jeremy A. Johnson | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-05-03)
Field: Materials Science — Materials Chemistry

Generative adversarial networks and diffusion models in material discovery The idea of materials discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structure to advanced machine learning methods for predicting entirely new crystal structures. In this work, we pursue three primary objectives. I) Introduce CrysTens, a crystal encoding that can be used in a wide variety of deep-learning generative models. II) Investigate and analyze the performance of Generative Adversarial Networks (GANs) and Diffusion Models to find an innovative and effective way of generating theoretical crystal structures that are synthesizable and stable. III) Show that the models that have a better “understanding” of the structure of CrysTens produce more symmetrical and realistic crystals and exhibit a better apprehension of the dataset as a whole. We accomplish these objectives using over fifty thousand Crystallographic Information Files (CIFs) from Pearson’s Crystal Database. Generative grammar ; Crystal (programming language) ; Computer science ; Variety (cybernetics) ; Artificial intelligence ; Adversarial system ; Generative model ; Apprehension ; Machine learning ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Franz M. Rohrhofer | Stefan Posch | Clemens Gößnitzer | José M García-Oliver | Bernhard C. Geiger
Source: arXiv (Cornell University) (preprint, 2023-08-03)
Field: Chemistry — Analytical Chemistry

Bringing Chemistry to Scale: Loss Weight Adjustment for Multivariate Regression in Deep Learning of Thermochemical Processes Flamelet models are widely used in computational fluid dynamics to simulate thermochemical processes in turbulent combustion. These models typically employ memory-expensive lookup tables that are predetermined and represent the combustion process to be simulated. Artificial neural networks (ANNs) offer a deep learning approach that can store this tabular data using a small number of network weights, potentially reducing the memory demands of complex simulations by orders of magnitude. However, ANNs with standard training losses often struggle with underrepresented targets in multivariate regression tasks, e.g., when learning minor species mass fractions as part of lookup tables. This paper seeks to improve the accuracy of an ANN when learning multiple species mass fractions of a hydrogen (\ce{H2}) combustion lookup table. We assess a simple, yet effective loss weight adjustment that outperforms the standard mean-squared error optimization and enables accurate learning of all species mass fractions, even of minor species where the standard optimization completely fails. Furthermore, we find that the loss weight adjustment leads to more balanced gradients in the network training, which explains its effectiveness. Multivariate statistics ; Artificial neural network ; Regression ; Lookup table ; Combustion ; Computer science ; Mean squared error ; Table (database) ; Artificial intelligence ; Minor (academic) ; Machine learning ; Chemistry ; Statistics ; Mathematics ; Data mining Spectroscopy and Chemometric Analyses ; Petroleum Processing and Analysis ; Chemical Thermodynamics and Molecular Structure
Authors: Sriram Devata | Bhuvanesh Sridharan | Sarvesh Mehta | Yashaswi Pathak | Siddhartha Laghuvarapu | Girish Varma | Deva Priyakumar
Source: ChemRxiv (preprint, 2023-11-22)
Field: Chemistry — Analytical Chemistry

DeepSPInN - multimodal Deep learning for molecular Structure Prediction from Infrared and NMR spectra Molecular spectroscopy studies the interaction of molecules with electromagnetic radiation, and interpreting the resultant spectra is invaluable for deducing the molecular structures. However, it is a strenuous task that requires highly specific domain knowledge. DeepSPInN predicts the molecular structure when given Infrared and 13C Nuclear magnetic resonance spectra without referring to any pre-existing spectral databases or molecular fragment knowledge bases. DeepSPInN does this by formulating the molecular structure prediction problem as a Markov decision process (MDP) and employs Monte-Carlo tree search to explore and choose the actions in the formulated MDP. On the QM9 dataset, DeepSPInN is able to predict the molecular structure for 95.98% of the input spectra in an average time of 160 seconds for molecules with less than 10 heavy atoms. This study is the first of its kind that uses multimodal data for molecular structure prediction, and is a leap forward in automated molecular spectral analysis. Spectral line ; Molecule ; Computer science ; Infrared spectroscopy ; Infrared ; Artificial intelligence ; Chemistry ; Nuclear magnetic resonance ; Physics ; Optics ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Giovanni Trezza | Eliodoro Chiavazzo
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Leveraging Composition-Based Energy Material Descriptors for Machine Learning Models Computer science ; Set (abstract data type) ; Bayesian probability ; Composition (language) ; Binary number ; Key (lock) ; Quality (philosophy) ; Property (philosophy) ; Artificial intelligence ; Focus (optics) ; Data mining ; Machine learning ; Database ; Mathematics ; Physics Machine Learning in Materials Science
Authors: Cheng-Han Li | Daniel P. Tabor
Source: ChemRxiv (preprint, 2023-07-02)
Field: Materials Science — Materials Chemistry

Generative Organic Electronic Molecular Design via Reinforcement Learning Integration with Quantum Chemistry: Tuning Singlet and Triplet Energy Levels Generative molecular design strategies have emerged as promising alternatives to trial-and-error approaches for exploring and optimizing within large chemical spaces. To date, generative models with reinforcement learning approaches have frequently used low-cost methods to evaluate the quality of the generated molecules, enabling many loops through the generative model. However, for functional molecular materials tasks, such low-cost methods are either not available or would require the generation of large amounts of training data to train surrogate machine learning models. In this work, we develop a framework that connects the REINVENT reinforcement learning framework with excited state quantum chemistry calculations to discover molecules with specified molecular excited state energy levels, specifically molecules with excited state landscapes that would serve as promising singlet fission or triplet-triplet annihilation materials. We employ a two-step curriculum strategy to find a set of diverse promising molecules, then exploit a more focused chemical space with anthracene derivatives. Under this protocol, we show that the framework can find desired molecules and improve Pareto fronts for targeted properties versus synthesizability. Moreover, from this framework, we are able to find several different design principles used by chemists for the design of singlet fission and triplet-triplet annihilation molecules. Reinforcement learning ; Singlet fission ; Computer science ; Excited state ; Chemical space ; Quantum chemistry ; Chemistry ; Singlet state ; Molecule ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Jun Huo | Yijian Zhang | Kai-yu Zhang | Peng Liu | Benkun Hong | Hao Dong | Shuhua Li
Source: Scientia Sinica Chimica (article, 2023-11-09)
Field: Materials Science — Materials Chemistry

Accurate modeling of aqueous chemistry at the nano-/micro-scale <p indent="0mm">At the nano-/micro-scale, the properties of molecules, material transfer, chemical reactions, and other processes exhibit distinct characteristics from those observed at the macroscopic scale. In recent years, the research on droplet and aqueous chemistry at the nano/micro scale in areas such as life sciences, materials science, and other fields has flourished. Accurate calculations and characterization of such systems help to reveal the molecular mechanisms that give rise to their unique properties at the microscopic level, and provide new insights for unraveling the mysteries of life and facilitating the research and development of new materials. This paper provides a review of recent scientific advances in studying such systems using simulation methods, including molecular force fields, quantum chemical methods, and the combination of machine learning algorithms, with a focus on aqueous systems at the gas–liquid interfaces at the nano-/micro-scales (including air/water, air/solution, and atmosphere/water), with bulk-phase liquids serving as a reference. In addition, we also provide an outlook on the future development of computational techniques for characterizing such systems. Aqueous solution ; Nano- ; Scale (ratio) ; Chemistry ; Nanoscopic scale ; Nanotechnology ; Chemical engineering ; Materials science ; Physical chemistry ; Physics ; Engineering Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Spectroscopy and Quantum Chemical Studies
Authors: Xiaoxiao Wang | Joseph Musielewicz | Richard Tran | Sudheesh Kumar Ethirajan | Xiaoyan Fu | H. Mera | John R. Kitchin | Rachel C. Kurchin | Zachary W. Ulissi
Source: arXiv (Cornell University) (preprint, 2023-11-03)
Field: Materials Science — Materials Chemistry

Generalization of Graph-Based Active Learning Relaxation Strategies Across Materials Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput efforts. This has prompted an explosion in exploration of machine learning assisted techniques to improve the computational efficiency of DFT. In this study, we present a comprehensive investigation of the broader application of Finetuna, an active learning framework to accelerate structural relaxation in DFT with prior information from Open Catalyst Project pretrained graph neural networks. We explore the challenges associated with out-of-domain systems: alcohol ($C_{&gt;2}$) on metal surfaces as larger adsorbates, metal-oxides with spin polarization, and three-dimensional (3D) structures like zeolites and metal-organic-frameworks. By pre-training machine learning models on large datasets and fine-tuning the model along the simulation, we demonstrate the framework's ability to conduct relaxations with fewer DFT calculations. Depending on the similarity of the test systems to the training systems, a more conservative querying strategy is applied. Our best-performing Finetuna strategy reduces the number of DFT single-point calculations by 80% for alcohols and 3D structures, and 42% for oxide systems. Density functional theory ; Computer science ; Artificial neural network ; Machine learning ; Relaxation (psychology) ; Artificial intelligence ; Materials science ; Theoretical computer science ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling