jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sumner B. Harris | Christopher M. Rouleau | Alexander A. Puretzky | Seok Joon Yun | Liangbo Liang | Ondrej Dyck | Tom Berlijn | Gyula Eres | Gerd Duscher | Austin Houston | Yu‐Chuan Lin | Mina Yoon | Kai Xiao | David B. Geohegan
Source:  (article, 2023-03-15)
Field: Materials Science — Materials Chemistry

Combining gas-phase, structural, and optical diagnostics for in situ feedback and control during pulsed laser deposition Automated platforms for synthesis are a necessity to increase the rate of discovery, synthesis, and optimization of materials to match the accelerating pace of theoretical predictions. Here, we introduce a novel pulsed laser deposition (PLD) platform that combines in situ Raman, photoluminescence, and white light reflectance spectroscopies as a material probe with intensified CCD (ICCD) imaging/spectroscopy and ion probe gas-phase PLD plume diagnostics. These diagnostics together with full automation and high-throughput synthesis schemes enable rapid synthesis and characterization with a Python-based dataflow for seamless integration with machine learning algorithms. Examples of in situ spectroscopy of 2D materials during growth and modification will be discussed. Photoluminescence ; Dataflow ; Python (programming language) ; Materials science ; In situ ; Raman spectroscopy ; Laser ; Characterization (materials science) ; Pulsed laser deposition ; Spectroscopy ; Computer science ; Nanotechnology ; Deposition (geology) ; Optoelectronics ; Optics ; Thin film ; Physics Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Laser-induced spectroscopy and plasma
Authors: Michelle Kleinhaus | Viktoria H. Gessner
Source: ChemRxiv (preprint, 2023-06-25)
Field: Chemistry — Inorganic Chemistry

Predicting the Reactivity of Acyclic Silylenes and Germylenes in Hydrogen Activation Divalent group 14 element compounds (heavier carbenes) have been shown to serve as transition metal mimics and thus can activate small molecules and strong bonds at mild reaction conditions. Incorporating this special ability into novel transition metal-free catalysts requires careful tuning of the molecular properties. Finding the optimal combination of substituents is challenging and an experimentally highly deman¬ding process. In this work, we combine DFT and machine learning methods to predict the energy and activation barrier for the activation of dihydrogen by silylenes and germylenes, to thus facilitate the selection of candidates for small molecule activation in the future. We demonstrate that - based on the analysis of approx. 600 acyclic silylenes generated from 40 different substituents - the energy profiles can be reliably predicted by a simple model using the sum of energy increments of the substituents of the silylene. Furthermore, quantitative structure-activity relationships between the energies and the electronic and steric properties of the silylenes could be established, which enabled the prediction of the activation barrier with a mean average error of less than 9 kJ/mol. The model even enabled an extrapolation to silylenes with substituents not included in the original data set, and thus could predict new silylene structures for small molecule activation. Moreover, the reported procedure can also be applied to germylenes, and their energy profile quantitatively expressed as a function of the energies values of their silicon analogues. Overall, the presented protocol allows for a fast screening and prediction of possible candidates for H2 activation, which will accelerate the development of main group element catalysts in the future. Silylene ; Chemistry ; Steric effects ; Reactivity (psychology) ; Activation energy ; Extrapolation ; Molecule ; Computational chemistry ; Yield (engineering) ; Small molecule ; Chemical physics ; Silicon ; Thermodynamics ; Stereochemistry ; Physical chemistry ; Physics ; Organic chemistry Inorganic Chemistry and Materials ; Synthesis and characterization of novel inorganic/organometallic compounds ; Asymmetric Hydrogenation and Catalysis
Authors: Shoukun Sun | Fei Xu | Lu Cai | Daniele Salvato | Fidelma Dilemma | Luca Capriotti | Min Xian | Tiankai Yao
Source: Research Square (preprint, 2023-05-16)
Field: Materials Science — Materials Chemistry

An Efficient Instance Segmentation Approach for Studying Fission Gas Bubbles in Irradiated Metallic Nuclear Fuel Abstract Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. A mechanical understanding of fission gas bubble evolution behavior is a prerequisite for fuel development and qualification. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented detail and an extensive amount of morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multi-task deep learning network that integrates both U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to other similar material micro-structure segmentation tasks. Fission ; Nuclear fuel ; Nuclear fission ; Nuclear material ; Bubble ; Fuel element failure ; Nuclear engineering ; Fission products ; Segmentation ; Nuclear physics ; Materials science ; Characterization (materials science) ; Nuclear reactor ; Artificial intelligence ; Computer science ; Nanotechnology ; Physics ; Neutron ; Mechanics ; Engineering Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering
Authors: Rongzhi Dong | Yuqi Song | Edirisuriya M. Dilanga Siriwardane | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2023-01-14)
Field: Materials Science — Materials Chemistry

Discovery of 2D materials using Transformer Network based Generative Design Two-dimensional (2D) materials have wide applications in superconductors, quantum, and topological materials. However, their rational design is not well established, and currently less than 6,000 experimentally synthesized 2D materials have been reported. Recently, deep learning, data-mining, and density functional theory (DFT)-based high-throughput calculations are widely performed to discover potential new materials for diverse applications. Here we propose a generative material design pipeline, namely material transformer generator(MTG), for large-scale discovery of hypothetical 2D materials. We train two 2D materials composition generators using self-learning neural language models based on Transformers with and without transfer learning. The models are then used to generate a large number of candidate 2D compositions, which are fed to known 2D materials templates for crystal structure prediction. Next, we performed DFT computations to study their thermodynamic stability based on energy-above-hull and formation energy. We report four new DFT-verified stable 2D materials with zero e-above-hull energies, including NiCl$_4$, IrSBr, CuBr$_3$, and CoBrCl. Our work thus demonstrates the potential of our MTG generative materials design pipeline in the discovery of novel 2D materials and other functional materials. Transformer ; Computer science ; Generative grammar ; Pipeline (software) ; Computation ; Density functional theory ; Template ; Generative Design ; Crystal structure prediction ; Artificial neural network ; Materials science ; Artificial intelligence ; Topology (electrical circuits) ; Nanotechnology ; Algorithm ; Electrical engineering ; Crystal structure ; Chemistry ; Engineering ; Computational chemistry Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electronic and Structural Properties of Oxides
Authors: Huan Wang | Yan‐Fu Li | Ying Zhang
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Electrochemistry

Bioinspired Spiking Spatiotemporal Attention Framework for Lithium-Ion Batteries Capacity and RUL Prediction by Electrochemical Impedance Spectroscopy Spiking neural network ; Computer science ; Battery (electricity) ; Reliability (semiconductor) ; Artificial neural network ; Artificial intelligence ; Power (physics) Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques ; Advanced Battery Technologies Research
Authors: Yanxing Wang | Théo Jaffrelot Inizan | Chengwen Liu | Jean‐Philip Piquemal | Pengyu Ren
Source: ChemRxiv (preprint, 2023-12-13)
Field: Materials Science — Materials Chemistry

Incorporating Neural Networks into the AMOEBA Polarizable Force Field Neural network potentials (NNPs) offer significant promise to bridge the gap be- tween the accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular simulation. Most NNPs rely on the locality assumption that ensures the model’s transferability and scalability and thus lack the treatment of long-range inter- actions, which are essential for molecular systems in condensed phase. Here we present an integrated hybrid model, AMOEBA+NN, which combines the AMOEBA potential for the short- and long-range non-covalent atomic interactions and an NNP to capture the remaining local covalent contributions. The AMOEBA+NN model was trained on the conformational energy of ANI-1x dataset and tested on several external datasets ranging from small molecules to tetrapeptides.The hybrid model demonstrated sub- stantial improvements over the baseline models in term of accuracy as the molecule size increased, suggesting its potential as a next-generation approach for chemically accurate molecular simulations. Locality ; Transferability ; Ranging ; Scalability ; Statistical physics ; Computer science ; Amoeba (genus) ; Artificial neural network ; Force field (fiction) ; Molecular dynamics ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry ; Machine learning ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Physical and Chemical Molecular Interactions
Authors: Ping Hu | Qingyu Chang | Wei Zhang | Minglei Yang | Ming Lei | De Chen | Xinggui Zhou | Zhi‐Jun Sui | Yi‐An Zhu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Dual Pt Atoms Stabilized by an Optimized Coordination Environment for Propane Dehydrogenation Dehydrogenation ; Propane ; Dual (grammatical number) ; Chemistry ; Materials science ; Catalysis ; Organic chemistry ; Philosophy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Anufrieva DA
Source: Modern applications in Pharmacy & Pharmacology (article, 2023-05-03)
Field: Materials Science — Materials Chemistry

From the Energetic Materials Genome to the Drugs Genome The capabilities of multifactor computational models of experimental data enriched with metadata in the field of combustion and detonation of energetic materials with complex chemical composition are described.Models created using neural networks allow for solving direct and inverse problems, conducting virtual experiments, and solving prediction tasks.The collection of such models implements the concept of the "Genome of Energetic Materials".The prospects for creating similar models in pharmacology and setting tasks for creating a Drugs Genome are demonstrated. Genome ; Genomics ; Human genome ; Genome project ; ENCODE Machine Learning in Materials Science ; Chemical Thermodynamics and Molecular Structure ; RNA and protein synthesis mechanisms
Authors: Rui Li | Aleksei Makogon | Tatiana Galochkina | Jean‐François Lemineur | Frédéric Kanoufi | Viacheslav Shkirskiy
Source: ChemRxiv (preprint, 2023-06-08)
Field: Materials Science — Materials Chemistry

Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy Operando wide-field optical microscopy imaging yields a wealth of information about the reactivity of metal interfaces, yet the data are often unstructured and challenging to process. In this study, we harness the power of unsupervised machine learning (ML) algorithms to analyze chemical reactivity images obtained dynamically by reflectivity microscopy in combination with ex situ scanning electron microscopy to identify and cluster the chemical reactivity of particles in Al alloy. The ML analysis uncovers three distinct clusters of reactivity from unlabeled datasets. A detailed examination of representative reactivity patterns confirms the chemical communication of generated OH- fluxes within particles, as supported by statistical analysis of size distribution and finite element modelling (FEM). The ML procedures also reveal statistically significant patterns of reactivity under dynamic conditions, such as pH acidification. The results align well with a numerical model of chemical communication, underscoring the synergy between data-driven ML and physics-driven FEM approaches. Reactivity (psychology) ; Cluster (spacecraft) ; Finite element method ; Microscopy ; Computer science ; Biological system ; Materials science ; Optical microscope ; Nanotechnology ; Scanning electron microscope ; Optics ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Alexander Mathiasen | Hatem Helal | Kerstin Kläser | Paul Balanca | Josef Dean | Carlo Luschi | Dominique Beaini | Andrew Fitzgibbon | Dominic Masters
Source: arXiv (Cornell University) (preprint, 2023-11-02)
Field: Materials Science — Materials Chemistry

Generating QM1B with PySCF$_{\text{IPU}}$ The emergence of foundation models in Computer Vision and Natural Language Processing have resulted in immense progress on downstream tasks. This progress was enabled by datasets with billions of training examples. Similar benefits are yet to be unlocked for quantum chemistry, where the potential of deep learning is constrained by comparatively small datasets with 100k to 20M training examples. These datasets are limited in size because the labels are computed using the accurate (but computationally demanding) predictions of Density Functional Theory (DFT). Notably, prior DFT datasets were created using CPU supercomputers without leveraging hardware acceleration. In this paper, we take a first step towards utilising hardware accelerators by introducing the data generator PySCF$_{\text{IPU}}$ using Intelligence Processing Units (IPUs). This allowed us to create the dataset QM1B with one billion training examples containing 9-11 heavy atoms. We demonstrate that a simple baseline neural network (SchNet 9M) improves its performance by simply increasing the amount of training data without additional inductive biases. To encourage future researchers to use QM1B responsibly, we highlight several limitations of QM1B and emphasise the low-resolution of our DFT options, which also serves as motivation for even larger, more accurate datasets. Code and dataset are available on Github: http://github.com/graphcore-research/pyscf-ipu Computer science ; Code (set theory) ; Simple (philosophy) ; Generator (circuit theory) ; Source code ; Artificial intelligence ; Deep learning ; Artificial neural network ; Machine learning ; Resolution (logic) ; Data science ; Programming language Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Tom Pan | Dun Chen | Shikai Jin | Ria Stevens | Mitchell D. Miller | Anastasios Kyrillidis | G.N. Phillips
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Deep learning-based predictions of electron-density maps from input Patterson maps We have been testing the ability of deep learning to predict electron density maps given an input Patterson map.Hurwitz [1] reported success in determining the positions of 10 randomly positioned, identical atoms from their Patterson maps via a convolutional neural network( CNN).We have conducted trials using atom arrangements extracted from short peptides.After training U-Net based CNN machine learning models implemented in the Torch Python framework, we found that we can predict a low-resolution electron density estimate from the Patterson maps [2].The histogram of the phase error of the predicted maps are more accurate than a random distribution.From a recent trial, we estimate that roughly 85% of the output maps were accurate enough for building a startling model to start refinement of the peptide.After these encouraging results, we have tested variations in our training models.Adding prior information from standard amino acid rotamers and incorporating training recycling improved the accuracy of the predicted maps.We found that adding a transformer to our model, inspired by 3D vision transformers [3,4] and implemented using a one-way attention framework to the Patterson map and reference structures, gave more accurate predictions than the UNet only model with lower computational training cost.To see how far various training approaches can take us, we are exploring ways to expand the model to account for symmetry and are continuing to make our training and test model data more like actual crystal data. Artificial intelligence ; Computer science ; Statistical physics ; Pattern recognition (psychology) ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Vidya Ganapati | Daniel Tchoń | Aaron S. Brewster | Nicholas K. Sauter
Source: PubMed (preprint, 2023-07-04)
Field: Materials Science — Materials Chemistry

Self-Supervised Deep Learning for Model Correction in the Computational Crystallography Toolbox. The Computational Crystallography Toolbox (cctbx) is open-source software that allows for processing of crystallographic data, including from serial femtosecond crystallography (SFX), for macromolecular structure determination. We aim to use the modules in cctbx to determine the oxidation state of individual metal atoms in a macromolecule. Changes in oxidation state are reflected in small shifts of the atom's X-ray absorption edge. These energy shifts can be extracted from the diffraction images recorded in serial femtosecond crystallography, given knowledge of a forward physics model. However, as the diffraction changes only slightly due to the absorption edge shift, inaccuracies in the forward physics model make it extremely challenging to observe the oxidation state. In this work, we describe the potential impact of using self-supervised deep learning to correct the scientific model in cctbx and provide uncertainty quantification. We provide code for forward model simulation and data analysis, built from cctbx modules, at https://github.com/gigantocypris/SPREAD, which can be integrated with machine learning. We describe open questions in algorithm development to help spur advances through dialog between crystallographers and machine learning researchers. New methods could help elucidate charge transfer processes in many reactions, including key events in photosynthesis. Toolbox ; Computer science ; Femtosecond ; Diffraction ; Artificial intelligence ; File format ; Dialog box ; Software ; Algorithm ; Machine learning ; Computational science ; Physics ; Optics ; Programming language Enzyme Structure and Function ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Vidya Ganapati | Daniel Tchoń | Aaron S. Brewster | Nicholas K. Sauter
Source: arXiv (Cornell University) (preprint, 2023-07-04)
Field: Materials Science — Materials Chemistry

Self-Supervised Deep Learning for Model Correction in the Computational Crystallography Toolbox The Computational Crystallography Toolbox (CCTBX) is open-source software that allows for processing of crystallographic data, including from serial femtosecond crystallography (SFX), for macromolecular structure determination. We aim to use the modules in CCTBX to determine the oxidation state of individual metal atoms in a macromolecule. Changes in oxidation state are reflected in small shifts of the atom's X-ray absorption edge. These energy shifts can be extracted from the diffraction images recorded in serial femtosecond crystallography, given knowledge of a forward physics model. However, as the diffraction changes only slightly due to the absorption edge shift, inaccuracies in the forward physics model make it extremely challenging to observe the oxidation state. In this work, we describe the potential impact of using self-supervised deep learning to correct the scientific model in CCTBX and provide uncertainty quantification. We provide code for forward model simulation and data analysis, built from CCTBX modules, at https://github.com/gigantocypris/SPREAD , which can be integrated with machine learning. We describe open questions in algorithm development to help spur advances through dialog between crystallographers and machine learning researchers. New methods could help elucidate charge transfer processes in many reactions, including key events in photosynthesis. Toolbox ; Computer science ; Femtosecond ; Diffraction ; Artificial intelligence ; Machine learning ; Dialog box ; Algorithm ; Computational science ; Physics ; Optics ; Programming language Machine Learning in Materials Science ; Enzyme Structure and Function ; Electron and X-Ray Spectroscopy Techniques
Authors: Siva Krishna Karuturi | Doudou Zhang | Haobo Li | Haijiao Lu | Zongyou Yin | Zelio Fusco | Karsten Reuter | Kylie Catchpole
Source: Research Square (preprint, 2023-05-10)
Field: Materials Science — Materials Chemistry

WITHDRAWN: Unlocking the performance of ternary metal (hydro)oxide amorphous catalysts via data-driven active-site engineering Ternary operation ; Amorphous solid ; Oxide ; Catalysis ; Amorphous metal ; Metal ; Materials science ; Chemical engineering ; Computer science ; Metallurgy ; Chemistry ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Nanomaterials for catalytic reactions
Authors: Sarah Perez | Philippe Poncet
Source: SPIRE - Sciences Po Institutional REpository (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Chemical and morphological uncertainty quantification by auto-weighted Bayesian Physics-Informed Neural Networks for reactive two-scale porous media at the pore scale: MS14 Uncertainty Quantification in Porous Media International audience Scale (ratio) ; Porous medium ; Bayesian probability ; Artificial neural network ; Computer science ; Uncertainty quantification ; Artificial intelligence ; Materials science ; Porosity ; Machine learning ; Physics ; Composite material Machine Learning in Materials Science ; Fault Detection and Control Systems ; Enhanced Oil Recovery Techniques