jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lars Holdijk | Yuanqi Du | Ferry Hooft | Priyank Jaini | Bernd Ensing | Max Welling
Source: arXiv (Cornell University) (preprint, 2022-06-27)
Field: Chemistry — Spectroscopy

Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths We consider the problem of sampling transition paths between two given metastable states of a molecular system, e.g. a folded and unfolded protein or products and reactants of a chemical reaction. Due to the existence of high energy barriers separating the states, these transition paths are unlikely to be sampled with standard Molecular Dynamics (MD) simulation. Traditional methods to augment MD with a bias potential to increase the probability of the transition rely on a dimensionality reduction step based on Collective Variables (CVs). Unfortunately, selecting appropriate CVs requires chemical intuition and traditional methods are therefore not always applicable to larger systems. Additionally, when incorrect CVs are used, the bias potential might not be minimal and bias the system along dimensions irrelevant to the transition. Showing a formal relation between the problem of sampling molecular transition paths, the Schrödinger bridge problem and stochastic optimal control with neural network policies, we propose a machine learning method for sampling said transitions. Unlike previous non-machine learning approaches our method, named PIPS, does not depend on CVs. We show that our method successful generates low energy transitions for Alanine Dipeptide as well as the larger Polyproline and Chignolin proteins. Curse of dimensionality ; Molecular dynamics ; Statistical physics ; Metastability ; Intuition ; Computer science ; Sampling (signal processing) ; Transition state ; Transition time ; Chemistry ; Artificial intelligence ; Physics ; Computational chemistry ; Detector ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Gene Regulatory Network Analysis ; Receptor Mechanisms and Signaling
Authors: Jiahui Yu | Chengwei Zhang | Yingying Cheng | Yun‐Fang Yang | Yuanbin She | Fengfan Liu | Weike Su | An Su
Source: ChemRxiv (preprint, 2022-10-05)
Field: Materials Science — Materials Chemistry

SolvBERT for solvation free energy and solubility prediction: a demonstration of an NLP model for predicting the properties of molecular complexes Deep learning models based on NLP, mainly the Transformer family, have been successfully applied to solve many chemistry-related problems, but their applications are mostly limited to chemical reactions. Meanwhile, solvation is an important concept in physical and organic chemistry, describing the interaction of solutes and solvents. This interaction leads to a solvation complex, a molecular complex similar to a reactant-reagent complex. In this study, we introduced the SolvBERT model, which reads the solute and solvents through the SMILES representation of the solvation complex. SolvBERT is pretrained in an unsupervised learning fashion using a large database of computational solvation free energies. The pretrained model can be used to predict the experimental solvation free energy or solubility, depending on the fine-tuning database. To the best of our knowledge, this multi-task prediction capability has not been observed in previously developed graph-based models for predicting the properties of molecular complexes. Furthermore, the performance of our SolvBERT in predicting solvation free energy is comparable to the state-of-the-art graph-based model DMPNN, mainly due to the clustering feature of the pretraining phase of the model, as demonstrated by the TMAP visualization algorithm. Solvation ; Implicit solvation ; Computer science ; Artificial intelligence ; Chemistry ; Molecular descriptor ; Solubility ; Machine learning ; Quantitative structure–activity relationship ; Biological system ; Molecule ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Prathik R Kaundinya | Kamal Choudhary | Surya R. Kalidindi
Source: arXiv (Cornell University) (preprint, 2022-01-20)
Field: Materials Science — Materials Chemistry

Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN) Machine learning (ML) based models have greatly enhanced the traditional materials discovery and design pipeline. Specifically, in recent years, surrogate ML models for material property prediction have demonstrated success in predicting discrete scalar-valued target properties to within reasonable accuracy of their DFT-computed values. However, accurate prediction of spectral targets such as the electron Density of States (DOS) poses a much more challenging problem due to the complexity of the target, and the limited amount of available training data. In this study, we present an extension of the recently developed Atomistic Line Graph Neural Network (ALIGNN) to accurately predict DOS of a large set of material unit cell structures, trained to the publicly available JARVIS-DFT dataset. Furthermore, we evaluate two methods of representation of the target quantity - a direct discretized spectrum, and a compressed low-dimensional representation obtained using an autoencoder. Through this work, we demonstrate the utility of graph-based featurization and modeling methods in the prediction of complex targets that depend on both chemistry and directional characteristics of material structures. Computer science ; Artificial neural network ; Discretization ; Autoencoder ; Graph ; Representation (politics) ; Pipeline (software) ; Algorithm ; Scalar (mathematics) ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Kasper Tolborg | Johan Klarbring | Alex M. Ganose | Aron Walsh
Source: ChemRxiv (preprint, 2022-06-02)
Field: Materials Science — Materials Chemistry

Free energy predictions for crystal stability and synthesisability What is the likelihood that a hypothetical material - the combination of a composition and crystal structure - can be formed? Underpinning the reliability of predictions for local or global crystal stability is the choice of thermodynamic potential. Here, we discuss recent advances in free energy descriptions for crystals including both harmonic and anharmonic phonon contributions to the vibrational entropy. We critically discuss some of the techniques and descriptors, including data-driven machine learning approaches, being developed to assess the stability and synthesisability of solids. Avenues are highlighted that deserve further attention including thermodynamic and kinetic factors that govern the accessibility of metastable structures away from equilibrium. Anharmonicity ; Metastability ; Statistical physics ; Stability (learning theory) ; Crystal structure prediction ; Entropy (arrow of time) ; Kinetic energy ; Energy landscape ; Thermodynamics ; Crystal (programming language) ; Physics ; Theoretical physics ; Crystal structure ; Computer science ; Classical mechanics ; Chemistry ; Condensed matter physics ; Crystallography ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Penghua Ying | Haikuan Dong | Ting Liang | Zheyong Fan | Zheng Zhong | Jin Zhang
Source: ChemRxiv (preprint, 2022-09-06)
Field: Materials Science — Materials Chemistry

Atomistic insights into the mechanical anisotropy and fragility of monolayer fullerene networks using quantum mechanical calculations and machine-learning molecular dynamics simulations In this work, we comprehensively study the mechanical properties of the newly synthesised monolayer quasi-hexagonal-phase fullerene (qHPF) membrane [Nature \textbf{606}, 507-510 (2022)] under uniaxial tension by using quantum mechanical density-functional-theory (DFT) calculations and molecular dynamics (MD) simulations with a machine-learned neuroevolution potential (NEP). The elastic properties and fracture behaviours of monolayer qHPF are found to be strongly anisotropic due to the different properties between the inter-fullerene C-C single bonds and [2 + 2] cycloaddition bonds. Moreover, the tensile strength and fracture strain of monolayer qHPF are much smaller than those of any other existing two-dimensional (2D) carbon crystals. The very small tensile strength or fracture strain is ascribed to the inhomogeneous deformation of the stretched monolayer qHPF, which originates from the stiffness difference between the soft inter-fullerene bonds and the rigid intra-fullerene bonds. Compared with DFT calculations at the ground state, the NEP-based extensive MD simulations predict a much smaller tensile strength and fracture strain for monolayer qHPF due to their consideration of the effects of temperature and membrane size. Our work not only reveals the underlying mechanism of the fracture behaviours of monolayer fullerene networks from an atomistic perspective, but also shows the effectiveness and accuracy of the NEP approach in determining the mechanical properties of 2D materials in the realistic situations. Monolayer ; Materials science ; Fullerene ; Molecular dynamics ; Ultimate tensile strength ; Anisotropy ; Chemical physics ; Density functional theory ; Composite material ; Computational chemistry ; Nanotechnology ; Chemistry ; Physics Machine Learning in Materials Science ; Graphene research and applications ; Carbon Nanotubes in Composites
Authors: Kamal Choudhary | Mathew L. Kelley
Source: arXiv (Cornell University) (preprint, 2022-09-17)
Field: Materials Science — Materials Chemistry

ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data In this work, we present the ChemNLP library that can be used for 1) curating open access datasets for materials and chemistry literature, developing and comparing traditional machine learning, transformers and graph neural network models for 2) classifying and clustering texts, 3) named entity recognition for large-scale text-mining, 4) abstractive summarization for generating titles of articles from abstracts, 5) text generation for suggesting abstracts from titles, 6) integration with density functional theory dataset for identifying potential candidate materials such as superconductors, and 7) web-interface development for text and reference query. We primarily use the publicly available arXiv and Pubchem datasets but the tools can be used for other datasets as well. Moreover, as new models are developed, they can be easily integrated in the library. ChemNLP is available at the websites: https://github.com/usnistgov/chemnlp and https://jarvis.nist.gov/jarvischemnlp. Computer science ; Automatic summarization ; PubChem ; Information retrieval ; Natural language processing ; Question answering ; Cluster analysis ; Artificial intelligence ; World Wide Web ; Chemistry Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Data Quality and Management
Authors: Tian Xie | Ha-Kyung Kwon | Daniel Schweigert | Sheng Gong | Arthur France‐Lanord | Arash Khajeh | Emily Crabb | Michael Puzon | Chris Fajardo | Will Powelson | Yang Shao‐Horn | Jeffrey C. Grossman
Source: arXiv (Cornell University) (preprint, 2022-08-02)
Field: Materials Science — Materials Chemistry

A cloud platform for automating and sharing analysis of raw simulation data from high throughput polymer molecular dynamics simulations Open material databases storing hundreds of thousands of material structures and their corresponding properties have become the cornerstone of modern computational materials science. Yet, the raw outputs of the simulations, such as the trajectories from molecular dynamics simulations and charge densities from density functional theory calculations, are generally not shared due to their huge size. In this work, we describe a cloud-based platform to facilitate the sharing of raw data and enable the fast post-processing in the cloud to extract new properties defined by the user. As an initial demonstration, our database currently includes 6286 molecular dynamics trajectories for amorphous polymer electrolytes and 5.7 terabytes of data. We create a public analysis library at https://github.com/TRI-AMDD/htp_md to extract multiple properties from the raw data, using both expert designed functions and machine learning models. The analysis is run automatically with computation in the cloud, and results then populate a database that can be accessed publicly. Our platform encourages users to contribute both new trajectory data and analysis functions via public interfaces. Newly analyzed properties will be incorporated into the database. Finally, we create a front-end user interface at https://www.htpmd.matr.io for browsing and visualization of our data. We envision the platform to be a new way of sharing raw data and new insights for the computational materials science community. Terabyte ; Computer science ; Cloud computing ; Raw data ; Interface (matter) ; Visualization ; Data sharing ; Database ; Data mining ; Operating system Machine Learning in Materials Science ; Scientific Computing and Data Management ; Graph Theory and Algorithms
Authors: He Li | Zechen Tang | Xiaoxun Gong | Nianlong Zou | Wenhui Duan | Yong Xu
Source: arXiv (Cornell University) (preprint, 2022-11-19)
Field: Materials Science — Materials Chemistry

Deep-learning electronic-structure calculation of magnetic superstructures Ab initio study of magnetic superstructures (e.g., magnetic skyrmion) is indispensable to the research of novel materials but bottlenecked by its formidable computational cost. For solving the bottleneck problem, we develop a deep equivariant neural network method (named xDeepH) to represent density functional theory Hamiltonian $H_\text{DFT}$ as a function of atomic and magnetic structures and apply neural networks for efficient electronic structure calculation. Intelligence of neural networks is optimized by incorporating a priori knowledge about the important locality and symmetry properties into the method. Particularly, we design a neural-network architecture fully preserving all equivalent requirements on $H_\text{DFT}$ by the Euclidean and time-reversal symmetries ($E(3) \times \{I, T\}$), which is essential to improve method performance. High accuracy (sub-meV error) and good transferability of xDeepH are shown by systematic experiments on nanotube, spin-spiral, and Moiré magnets, and the capability of studying magnetic skyrmion is also demonstrated. The method could find promising applications in magnetic materials research and inspire development of deep-learning ab initio methods. Materials science ; Condensed matter physics ; Computer science ; Physics Machine Learning in Materials Science ; Electric Power Systems and Control ; Magnetic Properties and Applications
Authors: Sung-Ho Lee | Valério Olevano | B. Sklénard
Source: Solid-State Electronics (article, 2022-11-05)
Field: Materials Science — Materials Chemistry

A generalizable, uncertainty-aware neural network potential for GeSbTe with Monte Carlo dropout Dropout (neural networks) ; Monte Carlo method ; Statistical physics ; Artificial neural network ; Bayesian probability ; Computer science ; Trajectory ; Bayesian inference ; Algorithm ; Artificial intelligence ; Machine learning ; Physics ; Mathematics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Phase-change materials and chalcogenides ; Computational Drug Discovery Methods
Authors: Tomer Weiss | Alexandra Wahab | Alex Bronstein | Renana Gershoni‐Poranne
Source: ChemRxiv (preprint, 2022-05-24)
Field: Materials Science — Materials Chemistry

Interpretable Deep-Learning Unveils Structure-Property Relationships in Polybenzenoid Hydrocarbons In this work, interpretable deep learning was used to identify structure-property relationships governing the HOMO-LUMO gap and relative stability of polybenzenoid hydrocarbons (PBHs). To this end, a ring-based graph representation was used. In addition to affording reduced training times and excellent predictive ability, this representation could be combined with a subunit-based perception of PBHs, allowing chemical insights to be presented in terms of intuitive and simple structural motifs. The resulting insights agree with conventional organic chemistry knowledge and electronic structure-based analyses, and also reveal new behaviors and identify influential structural motifs. In particular, we evaluated and compared the effects of linear, angular, and branching motifs on these two molecular properties, as well as explored the role of dispersion in mitigating torsional strain inherent in non-planar PBHs. Hence, the observed regularities and the proposed analysis contribute to a deeper understanding of the behavior of PBHs and form the foundation for design strategies for new functional PBHs. Branching (polymer chemistry) ; Deep learning ; Representation (politics) ; Perception ; Chemical physics ; Biological system ; Graph ; Computer science ; Artificial intelligence ; Computational chemistry ; Chemistry ; Theoretical computer science ; Epistemology ; Biology ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Polymer crystallization and properties
Authors: Akash Singh | Yumeng Li
Source:  (article, 2022-10-30)
Field: Materials Science — Materials Chemistry

Machine Learning Potentials for Graphene Abstract Graphene has been one of the most researched material in the world for the past two decades due to its unique combination of mechanical, thermal and electrical properties. Graphene exists in a stable two dimensional (2D) structure with hexagonal carbon rings. This special 2D structure of graphene enables it to exhibit a wide range of peculiar material properties like high Young’s modulus, high specific strength, and electrical conductivity etc. However, it is extremely challenging and costly to investigate graphene solely based on experimental tests. Atomistic simulations are powerful computational techniques for characterizing materials at small length and time scales with a fraction of cost relative to experimental testing. High fidelity atomistic simulations like Density Functional Theory (DFT) simulations, and ab initio molecular dynamic simulations have higher accuracy in predicting 2D material properties but are computationally expensive. Classic molecular dynamics (MD) simulations adopt empirical interatomic potentials which drastically reduce the computational time but has lower simulation accuracy. To bridge the gap between these two type of simulation techniques, a new artificial neural network potential is developed, for graphene in this study, to enable the characterization of 2D materials using classic MD simulations with a comparable accuracy of first principles simulation. This is expected to accelerate the discovery and design of novel graphene based functional materials. In the present study mechanical and thermal properties of graphene are investigated using the machine learning potentials by conducting MD simulations. To validate the accuracy of machine learning potentials mechanical properties such as Young’s modulus, ultimate tensile strength and thermal properties such as coefficient of thermal expansion and lattice parameter are evaluated for graphene and compared with existing literature. Graphene ; Materials science ; Molecular dynamics ; Interatomic potential ; Density functional theory ; Thermal conductivity ; Nanotechnology ; Computer science ; Computational chemistry ; Composite material ; Chemistry Machine Learning in Materials Science ; Graphene research and applications ; Fuel Cells and Related Materials
Authors: Ying Feng | Mingwei Liang | Zedong Hu
Source: 2022 International Conference on Advanced Robotics and Mechatronics (ICARM) (article, 2022-07-09)
Field: Materials Science — Materials Chemistry

Tracking Control of Shape Memory Alloy Artificial Wrist Joint Using Sliding Mode Control Strategy Based on RBF Neural Network Shape memory alloy (SMA) is a kind of smart material that can generate deformation and recovery tension with the phase transition induced by temperature difference. SMA has been gradually used in many fields like micro robots and aerospace with its excellent flexible actuating ability. However, due to the internal material properties, there is a strong saturation hysteresis nonlinearity in SMA drivers, which leads to a severe drop in output accuracy, and even instability in some special conditions. In this paper, an artificial wrist joint was built. A sliding mode controller is proposed in this paper with the internal hysteresis estimated by the RBF networks to improve control ability. The proposed control strategy can achieve desired output performance of the artificial wrist, and the effectiveness of the proposed controller is verified with the simulation tests. Shape-memory alloy ; SMA* ; Control theory (sociology) ; Artificial neural network ; Computer science ; Hysteresis ; Nonlinear system ; Sliding mode control ; Controller (irrigation) ; Materials science ; Artificial intelligence ; Control (management) ; Physics Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Topology Optimization in Engineering
Authors: Maria H. Rasmussen | Diana S. Christensen | Jan H. Jensen
Source: ChemRxiv (preprint, 2022-12-01)
Field: Materials Science — Materials Chemistry

Do machines dream of atoms? Crippen's logP as a quantitative molecular benchmark for explainable AI heatmaps While there is a great deal of interest in methods aimed at explaining machine learning predictions of chemical properties, it is difficult to quantitatively benchmark such methods, especially for regression tasks. We show that the Crippen logP model (J. Chem. Inf. Comput. Sci. 1999, 39, 868) provides an excellent benchmark for atomic attribution/heatmap approaches, especially if the ground truth heatmaps can be adjusted to reflect the molecular representation. The “atom attribution from finger prints”-method developed by Riniker and Landrum (J. Chem. Inf. Comput. Sci. 2013, 5, 43) gives atomic attribution heatmaps that are in reasonable agreement with the atomic contribution heatmaps of the Crippen logP model for most molecules, with average heatmap overlaps of up to 0.54. The agreement is increased significantly (to 0.75) when the atomic contributions are adjusted to match the fact that the molecular representation is fragment-based rather than atom-based (the finger print-adapted (FPA) ground truth vector). Most heatmaps and the corresponding FPA overlaps are relatively insensitive to the training set size and the results are close to converged for a training set size of 1000 molecules, although for molecules with low overlap some heatmaps change significantly. Using the “remove an atom” approach for graph convolutional neural networks (GCNNs) suggested by Matveieva and Polishchuk (J. Cheminform. 2021, 13, 41) we find an average heatmap overlap of 0.47 for the atomic contribution heatmaps of the Crippen logP model. Like the simpler attribution benchmarks for classification tasks that have come before it, this work sets the bar for regression tasks. Benchmark (surveying) ; Computer science ; Set (abstract data type) ; Ground truth ; Representation (politics) ; Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography