jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Victor Sabanza Gil | Andres M. Bran | Malte Franke | Rémi Schlama | Jeremy S. Luterbacher | Philippe Schwaller
Source: arXiv (Cornell University) (preprint, 2023-12-14)
Field: Materials Science — Materials Chemistry

Holistic chemical evaluation reveals pitfalls in reaction prediction models The prediction of chemical reactions has gained significant interest within the machine learning community in recent years, owing to its complexity and crucial applications in chemistry. However, model evaluation for this task has been mostly limited to simple metrics like top-k accuracy, which obfuscates fine details of a model's limitations. Inspired by progress in other fields, we propose a new assessment scheme that builds on top of current approaches, steering towards a more holistic evaluation. We introduce the following key components for this goal: CHORISO, a curated dataset along with multiple tailored splits to recreate chemically relevant scenarios, and a collection of metrics that provide a holistic view of a model's advantages and limitations. Application of this method to state-of-the-art models reveals important differences on sensitive fronts, especially stereoselectivity and chemical out-of-distribution generalization. Our work paves the way towards robust prediction models that can ultimately accelerate chemical discovery. Generalization ; Computer science ; Machine learning ; Simple (philosophy) ; Artificial intelligence ; Key (lock) ; Task (project management) ; Data science ; Biochemical engineering ; Systems engineering ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Ting Liang | Penghua Ying | Ke Xu | Zhenqiang Ye | Chao Ling | Zheyong Fan | Jianbin Xu
Source: arXiv (Cornell University) (preprint, 2023-10-13)
Field: Materials Science — Materials Chemistry

Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics Amorphous silica (a-SiO$_2$) is a foundational disordered material for which the thermal transport properties are important for various applications. To accurately model the interatomic interactions in classical molecular dynamics (MD) simulations of thermal transport in a-SiO$_2$, we herein develop an accurate yet highly efficient machine-learned potential model that allowed us to generate a-SiO$_2$ samples closely resembling experimentally produced ones. Using the homogeneous nonequilibrium MD method and a proper quantum-statistical correction to the classical MD results, quantitative agreement with experiments is achieved for the thermal conductivities of bulk and 190 nm-thick a-SiO$_2$ films over a wide range of temperatures. To interrogate the thermal vibrations at different temperatures, we calculated the current correlation functions corresponding to the transverse acoustic (TA) and longitudinal acoustic (LA) collective vibrations. The results reveal that below the Ioffe-Regel crossover frequency, phonons as well-defined excitations, remain applicable in a-SiO$_2$ and play a predominant role at low temperatures, resulting in a temperature-dependent increase in thermal conductivity. In the high-temperature region, more phonons are excited, accompanied by a more intense liquid-like diffusion event. We attribute the temperature-independent thermal conductivity in the high-temperature range of a-SiO$_2$ to the collaborative involvement of excited phonon scattering and liquid-like diffusion in heat conduction. These findings provide physical insights into the thermal transport of a-SiO$_2$ and are expected to be applied to a vast range of amorphous materials. Phonon ; Thermal conductivity ; Thermal conduction ; Amorphous solid ; Thermal diffusivity ; Atmospheric temperature range ; Materials science ; Excited state ; Scattering ; Thermal ; Condensed matter physics ; Molecular dynamics ; Heat current ; Phonon scattering ; Heat capacity ; Thermodynamics ; Chemistry ; Optics ; Physics ; Computational chemistry ; Composite material ; Atomic physics ; Crystallography Machine Learning in Materials Science ; Glass properties and applications ; High-pressure geophysics and materials
Authors: Raquel López-Ríos de Castro | Robert M. Ziolek | Christian D. Lorenz
Source: ChemRxiv (preprint, 2023-02-14)
Field: Chemistry — Organic Chemistry

Topology-Controlled Self-Assembly of Amphiphilic Block Copolymers Contemporary synthetic chemistry approaches can be used to yield a range of distinct polymer topologies with precise control. The topology of a polymer strongly influences its self-assembly into complex nanostructures however a clear mechanistic understanding of the relationship between polymer topology and self-assembly has not yet been developed. In this work, we use atomistic molecular dynamics simulations to provide a nanoscale picture of the self-assembly of three poly(ethylene oxide)-poly(methyl acrylate) block copolymers with different topologies into micelles. We find that the topology affects the ability of the micelle to form a compact hydrophobic core, which directly affects its stability. Also, we apply unsupervised machine learning techniques to show that the topology of a polymer affects its ability to take a conformation in response to the local environment within the micelles. This work provides foundations for the rational design of polymer nanostructures based on their underlying topology. Micelle ; Topology (electrical circuits) ; Polymer ; Amphiphile ; Copolymer ; Materials science ; Network topology ; Nanostructure ; Ethylene oxide ; Self-assembly ; Block (permutation group theory) ; Nanotechnology ; Chemistry ; Computer science ; Mathematics ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly ; Machine Learning in Materials Science
Authors: Haomin Yu | Yanru Song | Jilin Hu | Chenjuan Guo | Bin Yang
Source: arXiv (Cornell University) (preprint, 2023-06-08)
Field: Materials Science — Materials Chemistry

A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction Crystal property prediction is a crucial aspect of developing novel materials. However, there are two technical challenges to be addressed for speeding up the investigation of crystals. First, labeling crystal properties is intrinsically difficult due to the high cost and time involved in physical simulations or lab experiments. Second, crystals adhere to a specific quantum chemical principle known as periodic invariance, which is often not captured by existing machine learning methods. To overcome these challenges, we propose the crystal-specific pre-training framework for learning crystal representations with self-supervision. The framework designs a mutex mask strategy for enhancing representation learning so as to alleviate the limited labels available for crystal property prediction. Moreover, we take into account the specific periodic invariance in crystal structures by developing a periodic invariance multi-graph module and periodic attribute learning within our framework. This framework has been tested on eight different tasks. The experimental results on these tasks show that the framework achieves promising prediction performance and is able to outperform recent strong baselines. Semaphore ; Property (philosophy) ; Crystal (programming language) ; Representation (politics) ; Computer science ; Graph ; Physical property ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Xiliang Lian | Mathieu Salanne
Source: ChemRxiv (preprint, 2023-07-25)
Field: Chemistry — Inorganic Chemistry

Capturing the interactions in the BaSnF4 ionic conductor: comparison between a machine-learning potential and a polarizable force field BaSnF4 is a prospective solid state electrolyte for fluoride ion batteries. However, the diffusion mechanism of the fluoride ions remains difficult to study, both in experiments and in simulations. In principle, ab initio molecular dynamics could allow to fill this gap, but this method remains very costly from the computational point of view. Using machine learning potentials is a promising method that can potentially address the the accuracy issues of classical empirical potentials while maintaining high efficiency. In this work, we fitted a dipole polarizable ion model and trained machine learning potential for BaSnF4 and made comprehensive comparisons on the ease of training, accuracy and efficiency. We also compared the results with the case of a simpler ionic system (NaF). We show that contrarily to the latter, for BaSnF4 the machine learning potential offers much higher versatility. The current work lays foundations for the investigation of fluoride ion mobility in BaSnF4 and provides insight on the choice of methods for atomistic simulations. Polarizability ; Dipole ; Ion ; Ionic bonding ; Computer science ; Conductor ; Ab initio ; Work (physics) ; Force field (fiction) ; Point (geometry) ; Machine learning ; Chemical physics ; Artificial intelligence ; Chemistry ; Materials science ; Physics ; Thermodynamics ; Mathematics ; Molecule Inorganic Fluorides and Related Compounds ; Machine Learning in Materials Science
Authors: Paul Henderson | Areg Ghazaryan | Alexander Zibrov | Andrea F. Young | Maksym Serbyn
Source: Physical review. B./Physical review. B (article, 2023-09-11)
Field: Materials Science — Materials Chemistry

Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene Harnessing the power of deep learning, researchers have developed a novel method to fit the band structure parameters of complex 2D materials, such as trilayer graphene. By training neural networks on simulated data for the density of states, and then minimizing the effective distance between network-generated images and experimental data, the authors obtain here accurate predictions of tight-binding parameters, validating their results against literature values. This method can be easily extended to other complex two-dimensional materials, as well as to other experimental techniques. Graphene ; Materials science ; Artificial neural network ; Electronic band structure ; Density of states ; Extraction (chemistry) ; Computer science ; Deep learning ; Artificial intelligence ; Statistical physics ; Nanotechnology ; Condensed matter physics ; Physics Graphene research and applications ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Yaoyuan Huo | Jun Jiang
Source: Chinese Journal of Chemical Physics (article, 2023-06-06)
Field: Materials Science — Materials Chemistry

Transferring graph neural network models for predicting bond dissociation energy between datasets Machine learning (ML) approaches like neural networks have been widely used in chemical researches for fast estimating chemical properties. Generating ML models of good precision requires datasets of high quality, which can be difficult to obtain. In this work, we trained graph neural network (GNN) models from different datasets and verified transferring of the models to other datasets. Our result shows that crossdataset evaluation can give less accurate but still correlative prediction results on different datasets. Errors are mainly due to systematic errors. The value range of prediction result is highly related to the range of training set. The precisions of different bonds show different distributions. C–H bond constantly gets the highest precision in the tested bonds. Computer science ; Artificial neural network ; Graph ; Machine learning ; Correlative ; Bond graph ; Range (aeronautics) ; Artificial intelligence ; Training set ; Data mining ; Dissociation (chemistry) ; Theoretical computer science ; Mathematics ; Chemistry ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Zhi-Hang Lee | Yuting Chen | Hao-Ting Chang | Tzuhsiung Yang
Source: ChemRxiv (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

A Joint Semi-Supervised Variational Autoencoder and Transfer Learning Model for Designing Molecular Transition Metal Complexes Deep generative models (DGMs) have shown great promise in the generation of organic molecules and inorganic materials with chemical sensible structures and optimized properties. However, there is a lack of their applications in transition metal (TM) complexes due to their flexible coordination environment, multiple accessible oxidation and spin states, despite the importance of these complexes in fine chemical synthesis, commodity production, and optical applications. Herein, we propose a joint semi-supervised junction-tree variational autoencoder (SSVAE) and artificial neural network (ANN) classifier model, coined as LiveTransForM (Ligand variational auto-encoder and Transfer learning For transition Metal complexes), for the design of octahedral TM complexes. LiveTransForM allows the design of ligands that build up TM complexes and the prediction of the spin states of the assembled complexes. We show that the accuracy of the classifier is improved when the latent variables from the SSVAE are used as input for the ANN model compared to those from the unsupervised VAE. Input augmentation using the three molecular axes also improves the accuracy of the classifier. 58 complexes with predicted spin states are then generated by LiveTransForM and the accuracy of their spin state labels are validated by density functional theory methods. Two design strategies, single mutation and seeded generation, are also introduced to allow the directed evolution of a parent complex towards a desirable spin state and local modification of seed complexes with similar spin states, respectively. Autoencoder ; Classifier (UML) ; Artificial intelligence ; Computer science ; Artificial neural network ; Deep learning ; Encoder ; Density functional theory ; Materials science ; Pattern recognition (psychology) ; Biological system ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Gagik Ayvazyan | K. D. Danilchenko | Dmitry Kovalenko | A. A. Maevsky | Yu. V. Nikityuk | В. А. Прохоренко | А. В. Семченко | V. V. Sidsky | Sergei Khakhomov | V. V. Malyutina-Bronskaya | A. V. Nesterenok
Source: Journal of Contemporary Physics (Armenian Academy of Sciences) (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Synthesis, Investigation and Neural Network Modeling of the Properties of Sol-Gel ITO/ZnO and ITO/ZnO:Mg Structures The ITO/ZnO and ITO/ZnO:Mg bilayer structures were fabricated by the sol-gel method and their structural and photoelectric properties were experimentally studied. It is shown that, compared with ZnO and ZnO:Mg films without an ITO sublayer, the morphology changes noticeably and the band gap decreases. The I–V characteristics of obtained structures were analyzed in the dark and under the influence of optical radiation of different wavelengths. Using artificial neural networks, their spectral photosensitivity was modeled. Photoelectric effect ; Bilayer ; Materials science ; Photosensitivity ; Band gap ; Sol-gel ; Optoelectronics ; Wavelength ; Visible spectrum ; Morphology (biology) ; Chemical engineering ; Nanotechnology ; Chemistry ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Transition Metal Oxide Nanomaterials
Authors: Mostafa Abedi | Jörg Behler | C. Franklin Goldsmith
Source: Journal of Chemical Theory and Computation (article, 2023-10-30)
Field: Materials Science — Materials Chemistry

High-Dimensional Neural Network Potentials for Accurate Prediction of Equation of State: A Case Study of Methane Machine learning-based interatomic potentials, such as those provided by neural networks, are increasingly important in molecular dynamics simulations. In the present work, we consider the applicability and robustness of machine learning molecular dynamics to predict the equation of state properties of methane by using high-dimensional neural network potentials (HDNNPs). We investigate two different strategies for generating training data: one strategy based upon bulk representations using periodic cells and another strategy based upon clusters of molecules. We assess the accuracy of the trained potentials by predicting the equilibrium mass density for a wide range of thermodynamic conditions to characterize the liquid phase, supercritical fluid, and gas phase, as well as the liquid-vapor coexistence curve. Our results show an excellent agreement with reference phase diagrams, with an average error below ∼2% for all studied phases. Moreover, we confirm the applicability of models trained on cluster data sets for producing accurate and reliable results. Artificial neural network ; Computer science ; Robustness (evolution) ; Equation of state ; Statistical physics ; Supercritical fluid ; Work (physics) ; Molecular dynamics ; Cluster (spacecraft) ; Methane ; Machine learning ; Artificial intelligence ; Thermodynamics ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; 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, 2023-08-10)
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 ; Work (physics) ; Density functional theory ; Data science ; World Wide Web ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Juan A. Allegretto | Diego Onna | Laura Ación | Sara A. Bilmes | Omar Azzaroni | Matías Rafti
Source: ChemRxiv (preprint, 2023-07-19)
Field: Chemistry — Inorganic Chemistry

Unified roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and their Impact on Particle Size and Morphology Metal-Organic Frameworks (MOFs) have settled in the scientific community over the last decades as versatile materials with several applications. Among those, Zeolitic Imidazolate Framework 8 (ZIF-8) is a well-known MOF that has been applied in various and diverse fields, from drug-delivery platforms to microelectronics. However, the complex role played by the reaction parameters in controlling the size and morphology of ZIF-8 particles is still not fully understood. Even further, many individual reports propose different nucleation and growth mechanisms for ZIF-8, thus creating a fragmented view of the system's behavior. To provide a unified view, we have generated a comprehensive dataset of synthetic conditions and their final outputs and applied Machine Learning techniques to analyze the data. Our approach has enabled us to identify the nucleation and growth mechanisms operating for ZIF-8 in a given portion of the chemical space and to reveal the underlying impact of synthetic variables on the final particle size and morphology. By doing so, we draw connections between the role of each synthetic variable over ZIF-8 synthesis and provided with a rule of thumb to control the final particle size. Our results provide a unified roadmap for the nucleation and growth mechanisms of ZIF-8 in agreement with mainstream trends, which can guide the rational design of ZIF-8 particles with specific sizes and morphology, determining their suitability for any targeted application. Altogether, our work represents a step forward in seeking control of the properties of MOFs through a deeper understanding of the rationale behind synthesis procedures employed for their synthesis. Nucleation ; Nanotechnology ; Zeolitic imidazolate framework ; Rule of thumb ; Computer science ; Materials science ; Biochemical engineering ; Chemistry ; Metal-organic framework ; Engineering ; Algorithm ; Physics ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Corrosion Behavior and Inhibition
Authors: Seonghwan Kim | Jeheon Woo | Woo Youn Kim
Source: arXiv (Cornell University) (preprint, 2023-04-20)
Field: Materials Science — Materials Chemistry

Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperformed the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learned the distribution of TS geometries for diverse reactions in training. Thus, TSDiff was able to find more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration. Generative grammar ; Computer science ; Diffusion ; Generative model ; Transition (genetics) ; Transition state ; Statistical physics ; Biological system ; Algorithm ; Artificial intelligence ; Chemistry ; Physics ; Thermodynamics ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics