jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Albert Musaelian | Simon Batzner | Anders Johansson | Lixin Sun | Cameron J. Owen | Mordechai Kornbluth | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2022-04-11)
Field: Materials Science — Materials Chemistry

Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences. In pursuit of this goal, neural message passing has lead to a paradigm shift by describing many-body correlations of atoms through iteratively passing messages along an atomistic graph. This propagation of information, however, makes parallel computation difficult and limits the length scales that can be studied. Strictly local descriptor-based methods, on the other hand, can scale to large systems but do not currently match the high accuracy observed with message passing approaches. This work introduces Allegro, a strictly local equivariant deep learning interatomic potential that simultaneously exhibits excellent accuracy and scalability of parallel computation. Allegro learns many-body functions of atomic coordinates using a series of tensor products of learned equivariant representations, but without relying on message passing. Allegro obtains improvements over state-of-the-art methods on the QM9 and revised MD-17 data sets. A single tensor product layer is shown to outperform existing deep message passing neural networks and transformers on the QM9 benchmark. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular dynamics simulations based on Allegro recover structural and kinetic properties of an amorphous phosphate electrolyte in excellent agreement with first principles calculations. Finally, we demonstrate the parallel scaling of Allegro with a dynamics simulation of 100 million atoms. Message passing ; Computer science ; Computation ; Scalability ; Equivariant map ; Scaling ; Generalization ; Artificial neural network ; Theoretical computer science ; Statistical physics ; Algorithm ; Artificial intelligence ; Mathematics ; Physics ; Parallel computing Machine Learning in Materials Science ; Topic Modeling ; Protein Structure and Dynamics
Authors: Hao Gao | Valentina Belova | Francesco La Porta | Juan Santiago Cingolani | Mie Andersen | Mehdi Saedi | Oleg Konovalov | Maciej Jankowski | Hendrik H. Heenen | Irene M. N. Groot | G. Renaud | Karsten Reuter
Source: Advanced Science (article, 2022-11-09)
Field: Materials Science — Materials Chemistry

Graphene at Liquid Copper Catalysts: Atomic‐Scale Agreement of Experimental and First‐Principles Adsorption Height Liquid metal catalysts have recently attracted attention for synthesizing high-quality 2D materials facilitated via the catalysts' perfectly smooth surface. However, the microscopic catalytic processes occurring at the surface are still largely unclear because liquid metals escape the accessibility of traditional experimental and computational surface science approaches. Hence, numerous controversies are found regarding different applications, with graphene (Gr) growth on liquid copper (Cu) as a prominent prototype. In this work, novel in situ and in silico techniques are employed to achieve an atomic-level characterization of the graphene adsorption height above liquid Cu, reaching quantitative agreement within 0.1 Å between experiment and theory. The results are obtained via in situ synchrotron X-ray reflectivity (XRR) measurements over wide-range q-vectors and large-scale molecular dynamics simulations based on efficient machine-learning (ML) potentials trained to first-principles density functional theory (DFT) data. The computational insight is demonstrated to be robust against inherent DFT errors and reveals the nature of graphene binding to be highly comparable at liquid Cu and solid Cu(111). Transporting the predictive first-principles quality via ML potentials to the scales required for liquid metal catalysis thus provides a powerful approach to reach microscopic understanding, analogous to the established computational approaches for catalysis at solid surfaces. Copper ; Adsorption ; Catalysis ; Atomic units ; Materials science ; Graphene ; Scale (ratio) ; Chemical engineering ; Physical chemistry ; Inorganic chemistry ; Nanotechnology ; Chemistry ; Physics ; Metallurgy ; Organic chemistry ; Quantum mechanics Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Graphene research and applications
Authors: Xingyu Liu | Xiaopeng Wang | Siyu Gao | Vincent Chang | Rithwik Tom | Maituo Yu | Luca M. Ghiringhelli | Noa Marom
Source: npj Computational Materials (article, 2022-04-19)
Field: Materials Science — Materials Chemistry

Finding predictive models for singlet fission by machine learning Abstract Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set. Singlet fission ; Singlet state ; Computer science ; Perturbation theory (quantum mechanics) ; Workflow ; Conditional independence ; Exciton ; Machine learning ; Chemistry ; Physics ; Artificial intelligence ; Nuclear physics ; Quantum mechanics ; Excited state Machine Learning in Materials Science ; Perovskite Materials and Applications ; Organic Electronics and Photovoltaics
Authors: Jerret Ross | Brian Belgodere | Vijil Chenthamarakshan | Inkit Padhi | Youssef Mroueh | Payel Das
Source: Research Square (preprint, 2022-05-13)
Field: Materials Science — Materials Chemistry

Molformer: Large Scale Chemical Language Representations Capture Molecular Structure and Properties Scale (ratio) ; Computer science ; Natural language processing ; Geography ; Cartography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Howard Yanxon | James Weng | Hannah Parraga | Wenqian Xu | Uta Ruett | Nicholas Schwarz
Source: Journal of Synchrotron Radiation (article, 2022-11-23)
Field: Materials Science — Materials Chemistry

Artifact identification in X-ray diffraction data using machine learning methods In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g. battery materials) or in complex sample environments (e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions (e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye-Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 µm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method. Diffraction ; Materials science ; X-ray crystallography ; Synchrotron ; Crystallite ; Rietveld refinement ; Crystallography ; Powder diffraction ; Crystal (programming language) ; Optics ; Chemistry ; Physics ; Computer science X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Shomik Verma | Miguel Rivera | David O. Scanlon | Aron Walsh
Source: The Journal of Chemical Physics (article, 2022-04-07)
Field: Materials Science — Materials Chemistry

Machine learned calibrations to high-throughput molecular excited state calculations Understanding the excited state properties of molecules provides insight into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to increase the efficiency of photovoltaic cells. While chemical discovery is time- and resource-intensive experimentally, computational chemistry can be used to screen large-scale databases for molecules of interest in a procedure known as high-throughput virtual screening. The first step usually involves a high-speed but low-accuracy method to screen large numbers of molecules (potentially millions), so only the best candidates are evaluated with expensive methods. However, use of a coarse first-pass screening method can potentially result in high false positive or false negative rates. Therefore, this study uses machine learning to calibrate a high-throughput technique [eXtended Tight Binding based simplified Tamm-Dancoff approximation (xTB-sTDA)] against a higher accuracy one (time-dependent density functional theory). Testing the calibration model shows an approximately sixfold decrease in the error in-domain and an approximately threefold decrease in the out-of-domain. The resulting mean absolute error of ∼0.14 eV is in line with previous work in machine learning calibrations and out-performs previous work in linear calibration of xTB-sTDA. We then apply the calibration model to screen a 250k molecule database and map inaccuracies of xTB-sTDA in chemical space. We also show generalizability of the workflow by calibrating against a higher-level technique (CC2), yielding a similarly low error. Overall, this work demonstrates that machine learning can be used to develop a cost-effective and accurate method for large-scale excited state screening, enabling accelerated molecular discovery across a variety of disciplines. Calibration ; Generalizability theory ; Throughput ; Computer science ; Workflow ; Excited state ; Chemical space ; Algorithm ; Domain (mathematical analysis) ; Approximation error ; Artificial intelligence ; Machine learning ; Chemistry ; Drug discovery ; Mathematics ; Physics ; Statistics ; Database ; Atomic physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Wen‐Feng Zeng | Xie‐Xuan Zhou | Sander Willems | Constantin Ammar | Maria Wahle | Isabell Bludau | Eugenia Voytik | Maximilian T. Strauss | Matthias Mann
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-07-16)
Field: Chemistry — Spectroscopy

AlphaPeptDeep: A modular deep learning framework to predict peptide properties for proteomics Abstract Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides ( https://github.com/MannLabs/alphapeptdeep ). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a novel HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition. Computer science ; Python (programming language) ; Modular design ; Deep learning ; Artificial intelligence ; Proteomics ; Artificial neural network ; Machine learning ; Peptide ; Computational biology ; Chemistry ; Programming language ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Antimicrobial Peptides and Activities
Authors: Gurjot S. Dhaliwal | Prasanth B. Nair | Chandra Veer Singh
Source: npj Computational Materials (article, 2022-01-17)
Field: Materials Science — Materials Chemistry

Machine learned interatomic potentials using random features Abstract We present a method to model interatomic interactions such as energy and forces in a computationally efficient way. The proposed model approximates the energy/forces using a linear combination of random features, thereby enabling fast parameter estimation by solving a linear least-squares problem. We discuss how random features based on stationary and non-stationary kernels can be used for energy approximation and provide results for three classes of materials, namely two-dimensional materials, metals and semiconductors. Force and energy predictions made using the proposed method are in close agreement with density functional theory calculations, with training time that is 96% lower than standard kernel models. Molecular Dynamics calculations using random features based interatomic potentials are shown to agree well with experimental and density functional theory values. Phonon frequencies as computed by random features based interatomic potentials are within 0.1% of the density functional theory results. Furthermore, the proposed random features-based potential addresses scalability issues encountered in this class of machine learning problems. Density functional theory ; Statistical physics ; Interatomic potential ; Computer science ; Energy (signal processing) ; Molecular dynamics ; Kernel (algebra) ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Peng Han | Peilin Zhao | Chan Lu | Junzhou Huang | Jiaxiang Wu | Shuo Shang | Bin Yao | Xiangliang Zhang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

GNN-Retro: Retrosynthetic Planning with Graph Neural Networks Retrosynthetic planning plays an important role in the field of organic chemistry, which could generate a synthetic route for the target product. The synthetic route is a series of reactions which are started from the available molecules. The most challenging problem in the generation of the synthetic route is the large search space of the candidate reactions. Estimating the cost of candidate reactions has been proved effectively to prune the search space, which could achieve a higher accuracy with the same search iteration. And the estimation of one reaction is comprised of the estimations of all its reactants. So, how to estimate the cost of these reactants will directly influence the quality of results. To get a better performance, we propose a new framework, named GNN-Retro, for retrosynthetic planning problem by combining graph neural networks(GNN) and the latest search algorithm. The structure of GNN in our framework could incorporate the information of neighboring molecules, which will improve the estimation accuracy of our framework. The experiments on the USPTO dataset show that our framework could outperform the state-of-the-art methods with a large margin under the same settings. Retrosynthetic analysis ; Margin (machine learning) ; Computer science ; Graph ; Artificial intelligence ; Search algorithm ; Data mining ; Machine learning ; Theoretical computer science ; Algorithm ; Chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Advanced Graph Neural Networks
Authors: Rima Nour Elhouda Tiri | Ayşenur Aygün | Senem Karahan Gülbay | Fatih Şen | Chin Kui Cheng | Hamed Jafarzadeh | Abbasali Abouei Mehrizi | Yasser Vasseghian
Source: Process Safety and Environmental Protection (article, 2022-04-06)
Field: Materials Science — Materials Chemistry

Improving hydrogen generation from dehydrogenation of dimethylamine borane using polyvinylpyrrolidone stabilized platinum-rhodium nanoclusters as highly efficient and reusable catalysts: Development of ANN model Dehydrogenation ; Rhodium ; Nanoclusters ; Borane ; Catalysis ; Dimethylamine ; Polyvinylpyrrolidone ; Platinum ; Materials science ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Organic chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Machine Learning in Materials Science
Authors: Jiuyang Zhao | Jacqueline M. Cole
Source: Journal of Chemical Information and Modeling (review, 2022-05-19)
Field: Materials Science — Materials Chemistry

Reconstructing Chromatic-Dispersion Relations and Predicting Refractive Indices Using Text Mining and Machine Learning Predicting the properties of materials prior to their synthesis is of great significance in materials science. Optical materials exhibit a large number of interesting properties that make them useful in a wide range of applications, including optical glasses, optical fibers, and laser optics. In all of these applications, refraction and its chromatic dispersion can directly reflect the characteristics of the transmitted light and determine the practical utility of the material. We demonstrate the feasibility of reconstructing chromatic-dispersion relations of well-known optical materials by aggregating data over a large number of independent sources, which are contained within a material database of experimentally determined refractive indices and wavelength values. We also employ this database to develop a machine-learning platform that can predict refractive indices of compounds without needing to know the structure or other properties of a material of interest. We present a web-based application that enables users to build their customized machine-learning models; this will help the scientific community to conduct further research into the discovery of optical materials. Refraction ; Dispersion (optics) ; Computer science ; Refractive index ; Chromatic scale ; Chromatic aberration ; Optics ; Range (aeronautics) ; Materials science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Chao Shu | Junjie He | Guangjie Xue | Cheng Xie
Source: Crystals (article, 2022-02-18)
Field: Materials Science — Materials Chemistry

Grain Knowledge Graph Representation Learning: A New Paradigm for Microstructure-Property Prediction The mesoscopic structure significantly affects the properties of polycrystalline materials. Current artificial-based microstructure-performance analyses are expensive and require rich expert knowledge. Recently, some machine learning models have been used to predict the properties of polycrystalline materials. However, they cannot capture the complex interactive relationship between the grains in the microstructure, which is a crucial factor affecting the material’s macroscopic properties. Here, we propose a grain knowledge graph representation learning method. First, based on the polycrystalline structure, an advanced digital representation of the knowledge graph is constructed, embedding ingenious knowledge while completely restoring the polycrystalline structure. Then, a heterogeneous grain graph attention model (HGGAT) is proposed to realize the effective high-order feature embedding of the microstructure and to mine the relationship between the structure and the material properties. Through benchmarking with other machine learning methods on magnesium alloy datasets, HGGAT consistently demonstrates superior accuracy on different performance labels. The experiment shows the rationality and validity of the grain knowledge graph representation and the feasibility of this work to predict the material’s structural characteristics. Embedding ; Computer science ; Mesoscopic physics ; Microstructure ; Graph ; Artificial intelligence ; Benchmarking ; Representation (politics) ; Theoretical computer science ; Machine learning ; Materials science ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition