jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jagna Witek | Joseph P. Heindel | Xingyi Guan | Itai Leven | Hongxia Hao | Pavithra M. Naullage | Allen LaCour | Selim Sami | Maximilian F. S. J. Menger | D. Vale Cofer-Shabica | Eric Berquist | Shirin Faraji | Evgeny Epifanovsky | Teresa Head‐Gordon
Source: Molecular Physics (article, 2022-10-07)
Field: Materials Science — Materials Chemistry

M-Chem: a modular software package for molecular simulation that spans scientific domains We present a new software package called M-Chem that is designed from scratch in C++ and parallelized on shared-memory multi-core architectures to facilitate efficient molecular simulations. Currently, M-Chem is a fast molecular dynamics (MD) engine that supports the evaluation of energies and forces from two-body to many-body all-atom potentials, reactive force fields, coarse-grained models, combined quantum mechanics molecular mechanics (QM/MM) models, and external force drivers from machine learning, augmented by algorithms that are focused on gains in computational simulation times. M-Chem also includes a range of standard simulation capabilities including thermostats, barostats, multi-timestepping, and periodic cells, as well as newer methods such as fast extended Lagrangians and high quality electrostatic potential generation. At present M-Chem is a developer friendly environment in which we encourage new software contributors from diverse fields to build their algorithms, models, and methods in our modular framework. The long-term objective of M-Chem is to create an interdisciplinary platform for computational methods with applications ranging from biomolecular simulations, reactive chemistry, to materials research. Modular design ; Molecular dynamics ; Computer science ; Computational science ; Software ; Thermostat ; Chemistry ; Nanotechnology ; Computational chemistry ; Materials science ; Programming language ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Nikita Serov | Vladimir V. Vinogradov
Source: Small Methods (article, 2022-03-14)
Field: Materials Science — Materials Chemistry

Inverse Material Search and Synthesis Verification by Hand Drawings via Transfer Learning and Contour Detection Nano- and micromaterials of various morphologies and compositions have extensive use in many different areas. However, the search for procedures giving custom nanomaterials with the desired structure, shape, and size remains a challenge and is often implemented by manual article screening. Here, for the first time, scanning and transmission electron microscopy inverse image search and hand drawing-based search via transfer learning are developed, namely, VGG16 convolution neural network repurposing for image features extraction and image similarity determination. Moreover, the case use of this platform is demonstrated on the calcium carbonate system, where the data are acquired by random high throughput experimental synthesis, and on Au nanoparticles data extracted from the articles. This approach can be used for advanced nanomaterials search, synthesis procedure verification, and can be further combined with machine learning solutions to provide data-driven nanomaterials discovery. Computer science ; Convolutional neural network ; Artificial intelligence ; Transfer of learning ; Nanomaterials ; Convolution (computer science) ; Similarity (geometry) ; Image (mathematics) ; Machine learning ; Artificial neural network ; Nanotechnology ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nanoparticle-Based Drug Delivery
Authors: Doosun Hong | Jaehoon Oh | Kihoon Bang | Soonho Kwon | Se-Young Yun | Hyuck Mo Lee
Source: The Journal of Physical Chemistry Letters (article, 2022-09-09)
Field: Materials Science — Materials Chemistry

Interpretable Deep Learning Model for Analyzing the Relationship between the Electronic Structure and Chemisorption Property The use of machine learning (ML) is exploding in materials science as a result of its high predictive performance of material properties. Tremendous trainable parameters are required to build an outperforming predictive model, which makes it impossible to retrace how the model predicts well. However, it is necessary to develop a ML model that can extract human-understandable knowledge while maintaining performance for a universal application to materials science. In this study, we developed a deep learning model that can interpret the correlation between surface electronic density of states (DOSs) of materials and their chemisorption property using the attention mechanism that provides which part of DOS is important to predict adsorption energies. The developed model constructs the well-known d-band center theory without any prior knowledge. This work shows that human-interpretable knowledge can be extracted from complex ML models. Property (philosophy) ; Chemisorption ; Artificial intelligence ; Computer science ; Machine learning ; Deep learning ; Work (physics) ; Adsorption ; Chemistry ; Engineering ; Mechanical engineering ; Physical chemistry ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Yuanqi Du | Xian Liu | Nilay D. Shah | Shengchao Liu | Jieyu Zhang | Bolei Zhou
Source: ChemRxiv (preprint, 2022-10-04)
Field: Materials Science — Materials Chemistry

ChemSpacE: Interpretable and Interactive Chemical Space Exploration Discovering meaningful molecules in the vast combinatorial chemical space has been a long-standing challenge in many fields from materials science to drug discovery. Recent advances in machine learning, especially generative models, have made remarkable progress and demonstrate considerable promise for automated molecule design. Nevertheless, most molecule generative models remain black-box systems, whose utility is limited by a lack of interpretability and human participation in the generation process. In this work we propose Chemical Space Explorer (ChemSpacE), a simple yet effective method for exploring the chemical space with pre-trained deep generative models. It enables users to interact with existing generative models and inform the molecule generation process. We demonstrate the efficacy of ChemSpacE on the molecule optimization task and the molecule manipulation task in single property and multi-property settings. On the molecule optimization task, the performance of ChemSpacE is on par with previous black-box optimization methods yet is considerably faster and more sample efficient. Furthermore, the interface from ChemSpacE facilitates human-in-the-loop chemical space exploration and interactive molecule design. Chemical space ; Interpretability ; Computer science ; Space (punctuation) ; Generative model ; Generative grammar ; Property (philosophy) ; Artificial intelligence ; Task (project management) ; Process (computing) ; Black box ; Interface (matter) ; Machine learning ; Human–computer interaction ; Drug discovery ; Bioinformatics ; Systems engineering ; Engineering ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ce Yang | Weihao Gao | Di Wu | Chong Wang
Source: arXiv (Cornell University) (preprint, 2022-01-28)
Field: Materials Science — Materials Chemistry

Learning to Simulate Unseen Physical Systems with Graph Neural Networks Simulation of the dynamics of physical systems is essential to the development of both science and engineering. Recently there is an increasing interest in learning to simulate the dynamics of physical systems using neural networks. However, existing approaches fail to generalize to physical substances not in the training set, such as liquids with different viscosities or elastomers with different elasticities. Here we present a machine learning method embedded with physical priors and material parameters, which we term as "Graph-based Physics Engine" (GPE), to efficiently model the physical dynamics of different substances in a wide variety of scenarios. We demonstrate that GPE can generalize to materials with different properties not seen in the training set and perform well from single-step predictions to multi-step roll-out simulations. In addition, introducing the law of momentum conservation in the model significantly improves the efficiency and stability of learning, allowing convergence to better models with fewer training steps. Physical system ; Computer science ; Convergence (economics) ; Artificial neural network ; Stability (learning theory) ; Graph ; Set (abstract data type) ; Physical law ; Variety (cybernetics) ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Topic Modeling ; Computational Physics and Python Applications
Authors: Shihui Wang | Xiaoyu Xue | Min Cheng | Shaochen Chen | Chong Liu | Li Zhou | Kexin Bi | Xu Ji
Source: Acta Chimica Sinica (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

High-Throughput Computational Screening of Metal-Organic Frameworks for CH<sub>4</sub>/H<sub>2</sub> Separation by Synergizing Machine Learning and Molecular Simulation 分离金属有机框架高通量计算筛选 王诗慧 薛小雨 程敏 陈少臣 刘冲 周利 毕可鑫 吉旭 * (四川大学化学工程学院 成都 610065) 摘要 在减少 CO 2 排放、实现碳中和的背景下, 金属有机框架(MOFs)在清洁能源领域展现出广阔应用前景.提出一种 机器学习和分子模拟协同的分层筛选策略, 快速、准确地从 134185 个假设 MOFs Chemistry ; Separation (statistics) ; Metal-organic framework ; Throughput ; High-throughput screening ; Nanotechnology ; Machine learning ; Organic chemistry ; Computer science ; Telecommunications Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Mandeep Rathee | Thorben Funke | Avishek Anand | Megha Khosla
Source: arXiv (Cornell University) (preprint, 2022-06-28)
Field: Materials Science — Materials Chemistry

BAGEL: A Benchmark for Assessing Graph Neural Network Explanations The problem of interpreting the decisions of machine learning is a well-researched and important. We are interested in a specific type of machine learning model that deals with graph data called graph neural networks. Evaluating interpretability approaches for graph neural networks (GNN) specifically are known to be challenging due to the lack of a commonly accepted benchmark. Given a GNN model, several interpretability approaches exist to explain GNN models with diverse (sometimes conflicting) evaluation methodologies. In this paper, we propose a benchmark for evaluating the explainability approaches for GNNs called Bagel. In Bagel, we firstly propose four diverse GNN explanation evaluation regimes -- 1) faithfulness, 2) sparsity, 3) correctness. and 4) plausibility. We reconcile multiple evaluation metrics in the existing literature and cover diverse notions for a holistic evaluation. Our graph datasets range from citation networks, document graphs, to graphs from molecules and proteins. We conduct an extensive empirical study on four GNN models and nine post-hoc explanation approaches for node and graph classification tasks. We open both the benchmarks and reference implementations and make them available at https://github.com/Mandeep-Rathee/Bagel-benchmark. Interpretability ; Computer science ; Benchmark (surveying) ; Graph ; Correctness ; Machine learning ; Artificial intelligence ; Artificial neural network ; Theoretical computer science ; Data mining ; Algorithm Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Advanced Graph Neural Networks
Authors: Chen-Di Han | Ying‐Cheng Lai
Source: Physical review. B./Physical review. B (article, 2022-12-14)
Field: Materials Science — Materials Chemistry

Generating extreme quantum scattering in graphene with machine learning Graphene quantum dots provide a platform for manipulating electron behaviors in two-dimensional (2D) Dirac materials. Most previous works were of the ``forward'' type in that the objective was to solve various confinement, transport, and scattering problems with given structures that can be generated by, e.g., applying an external electrical field. There are applications such as cloaking or superscattering where the challenging problem of inverse design needs to be solved: Finding a quantum-dot structure according to certain desired functional characteristics. A brute-force search of the system configuration based directly on the solutions of the Dirac equation is computationally infeasible. We articulate a machine-learning approach to addressing the inverse-design problem where artificial neural networks subject to physical constraints are exploited to replace the rigorous Dirac equation solver. In particular, we focus on the problem of designing a quantum-dot structure to generate both cloaking and superscattering in terms of the scattering efficiency as a function of the energy. We construct a physical loss function that enables accurate prediction of the scattering characteristics. We demonstrate that, in the regime of Klein tunneling, the scattering efficiency can be designed to vary over two orders of magnitude, allowing any scattering curve to be generated from a proper combination of the gate potentials. Our physics-based machine-learning approach can be a powerful design tool for 2D Dirac material-based electronics. Scattering ; Inverse scattering problem ; Cloaking ; Physics ; Solver ; Computer science ; Quantum mechanics ; Metamaterial Graphene research and applications ; Topological Materials and Phenomena ; Advanced Memory and Neural Computing
Authors: Etienne Palos | Saswata Dasgupta | Eleftherios Lambros | Francesco Paesani
Source: ChemRxiv (preprint, 2022-10-13)
Field: Materials Science — Materials Chemistry

Data-Driven Many-Body Potentials from Density Functional Theory for Aqueous Phase Chemistry Density functional theory (DFT) has been applied to modeling molecular interactions in water for over three decades. The ubiquity of water in chemical and biological processes demands a unified understanding of its physics, from the single-molecule to the thermodynamic limit and everything in between. Recent advances in the development of data-driven and machine-learning potentials have accelerated simulation of water and aqueous systems with DFT accuracy. However, the anomalous properties of water in the condensed phase, where a rigorous treatment of both local and non-local many-body interactions is in order, is often unsatisfactory or partially missing in DFT models of water. In this review, we discuss the modeling of water and aqueous systems based on DFT, and provide a comprehensive description of a general theoretical/computational framework for the development of data-driven many-body potentials from DFT reference data. This framework, coined MB-DFT, readily enables efficient many-body molecular dynamics (MB-MD) simulations of small molecules, in both gas and condensed phases, while preserving the accuracy of the underly- ing DFT model. Theoretical considerations are emphasized, including the role that the delocalization error plays in MB-DFT potentials of water, and the possibility to elevate DFT and MB-DFT to near-chemical-accuracy through a density-corrected formalism. The development of the MB-DFT framework is described in detail, along with its application in MB-MD simulations and recent extension to the modeling of reactive processes in solution within a quantum mechanics/many-body molecular mechanics (QM/MB-MM) scheme, using water as a prototypical solvent. Finally, we identify open challenges and discuss future directions for MB-DFT and QM/MB-MM simulations in condensed phases. Density functional theory ; Delocalized electron ; Statistical physics ; Molecular dynamics ; Formalism (music) ; Aqueous solution ; Molecule ; Computational chemistry ; Chemistry ; Computer science ; Chemical physics ; Physics ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Maotao Liu | Yifan Yang | Xu Gong | Li Liu | Qun Liu
Source: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (article, 2022-12-06)
Field: Materials Science — Materials Chemistry

HierMRL: Hierarchical Structure-Aware Molecular Representation Learning for Property Prediction Deep graph neural networks have recently demonstrated powerful representation learning capabilities in bioinformatics. It is still crucial and challenging to design a representation model fusing the fundamental chemistry and biology knowledge. However, most existing representation models not only ignore domain knowledge but are also built on labeled datasets. To address this issue, this paper proposed a hierarchical structure-aware pre-training model that used contrastive learning to improve molecular representations with unlabeled datasets. We conducted comprehensive experiments on 13 molecular benchmark datasets from different application domains. The results demonstrate that our hierarchical structure-aware pre-trained model achieves superior performance against state-of-the-art baselines. Computer science ; Representation (politics) ; Benchmark (surveying) ; Artificial intelligence ; Machine learning ; Feature learning ; Graph ; Property (philosophy) ; Domain (mathematical analysis) ; Domain knowledge ; Deep learning ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zeynep Uruk | Alper Kiraz | Veli Deniz
Source: Journal of Rubber Research (article, 2022-09-06)
Field: Chemistry — Analytical Chemistry

A comparison of machine learning methods to predict rheometric properties of rubber compounds Artificial neural network ; Natural rubber ; Perceptron ; Backpropagation ; Particle swarm optimization ; Extreme learning machine ; Multilayer perceptron ; Artificial intelligence ; Computer science ; Biological system ; Machine learning ; Materials science ; Composite material Spectroscopy and Chemometric Analyses ; Petroleum Processing and Analysis
Authors: Leon Gerard | Michael Scherbela | Philipp Marquetand | Philipp Grohs
Source: arXiv (Cornell University) (preprint, 2022-05-19)
Field: Materials Science — Materials Chemistry

Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? Finding accurate solutions to the Schrödinger equation is the key unsolved challenge of computational chemistry. Given its importance for the development of new chemical compounds, decades of research have been dedicated to this problem, but due to the large dimensionality even the best available methods do not yet reach the desired accuracy. Recently the combination of deep learning with Monte Carlo methods has emerged as a promising way to obtain highly accurate energies and moderate scaling of computational cost. In this paper we significantly contribute towards this goal by introducing a novel deep-learning architecture that achieves 40-70% lower energy error at 6x lower computational cost compared to previous approaches. Using our method we establish a new benchmark by calculating the most accurate variational ground state energies ever published for a number of different atoms and molecules. We systematically break down and measure our improvements, focusing in particular on the effect of increasing physical prior knowledge. We surprisingly find that increasing the prior knowledge given to the architecture can actually decrease accuracy. Benchmark (surveying) ; Curse of dimensionality ; Deep learning ; Computer science ; Measure (data warehouse) ; Key (lock) ; Monte Carlo method ; Scaling ; Statistical physics ; Artificial intelligence ; Applied mathematics ; Mathematical optimization ; Physics ; Mathematics ; Data mining ; Statistics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Andrea Mastropietro | Giuseppe Pasculli | Jürgen Bajorath
Source: STAR Protocols (article, 2022-11-24)
Field: Materials Science — Materials Chemistry

Protocol to explain graph neural network predictions using an edge-centric Shapley value-based approach Here we present EdgeSHAPer, a workflow for explaining graph neural networks by approximating Shapley values using Monte Carlo sampling. In this protocol, we describe steps to execute Python scripts for a chemical dataset from the original publication; however, this approach is also applicable to any user-provided dataset. We also detail steps encompassing neural network training, an explanation phase, and analysis via feature mapping. For complete details on the use and execution of this protocol, please refer to Mastropietro et al. (2022).<sup>1</sup>. Computer science ; Scripting language ; Workflow ; Python (programming language) ; Artificial neural network ; Protocol (science) ; Theoretical computer science ; Graph ; Data mining ; Artificial intelligence ; Machine learning ; Programming language ; Database Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Computational Drug Discovery Methods
Authors: Shokirbek Shermukhamedov | Michael Probst
Source: The European Physical Journal D (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Modelling the impact of argon atoms on a tungsten surface Abstract Sputtering from plasma-facing surfaces upon particle impact is an important process in material science. It is especially relevant in the diverter region of fusion devices, which nearly always consist of tungsten. Besides the main plasma components, argon is used in fusion devices to improve energy confinement. As a consequence, hot Ar atoms interact with W surfaces and can cause sputtering and other material degrading events. Atomistic simulations of the plasma-wall interactions make it possible to carry out a detailed analysis of sputtering, reflection, and retention processes. We report the results of molecular dynamics simulations with neural network potential energy expressions modelling the bombardment of tungsten samples by argon atoms in the energy range from 100 to 800 eV. The obtained sputtering results are in good agreement with available literature data. Furthermore, our data provide additional insight into atomic details of the processes involved in sputtering. We also investigate the effect of surface temperature on sputtering and reflection probabilities, which significantly affects the irradiation process at higher impact energies. Graphical abstract Sputtering ; Tungsten ; Argon ; Plasma ; Materials science ; Reflection (computer programming) ; Atomic physics ; Fusion power ; Fusion ; Irradiation ; Range (aeronautics) ; Nanotechnology ; Thin film ; Nuclear physics ; Metallurgy ; Physics ; Composite material ; Computer science Fusion materials and technologies ; Nuclear Materials and Properties ; Ion-surface interactions and analysis