jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Deyu Xia | Ning Li | Pengju Ren | Xiaodong Wen
Source: E3S Web of Conferences (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Prediction Of Material Properties By Neural Network Fusing The Atomic Local Environment And Global Description: Applied To Organic Molecules And Crystals Machine learning has brought great convenience to material property prediction. However, most existing models can only predict properties of molecules or crystals with specific size, and usually only local atomic environment or molecular global descriptor representation be used as the characteristics of the model, resulting in poor model versatility and cannot be applied to multiple systems. We propose a method that combines the description of the local atomic environment and the overall structure of the molecule, a fusion model consisting of a graph convolutional neural network and a fully connected neural network is used to predict the properties of molecules or crystals, and successfully applied to QM9 organic molecules and semiconductor crystal materials. Our method is not limited to a specific size of a molecule or a crystal structure. According to the calculation principle of the properties of the material molecules, the influences of the local atomic environment and the overall structure of the molecules on the properties are respectively considered, an appropriate weighting ratio is selected to predict the properties. As a result, the prediction performance has been greatly improved. In fact, the proposed method is not limited to organic molecules and crystals and is also applicable to other structures, such as clusters. Molecule ; Artificial neural network ; Weighting ; Crystal (programming language) ; Biological system ; Convolutional neural network ; Materials science ; Crystal structure prediction ; Representation (politics) ; Organic molecules ; Computer science ; Chemical physics ; Artificial intelligence ; Statistical physics ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Zhen Zhou
Source: Materials Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

<i>Journal of Materials Chemistry A</i> and <i>Materials Advances</i> Editor's choice web collection: “Machine learning for materials innovation” Zhen Zhou introduces a <italic>Journal of Materials Chemistry</italic>/<italic>Materials Advances</italic> Editor's choice web collection on machine learning for materials innovation (https://rsc.li/MachineLearning). Nanotechnology ; Computer science ; Chemistry ; Materials science Machine Learning in Materials Science
Authors: Yunxing Zuo
Source: eScholarship (California Digital Library) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Towards Large-scale Atomistic Simulation and Materials Discovery In materials science, the first principles modeling, especially density functional theory (DFT), serves as the de facto tool in studying physical phenomena and properties of materials from the atomistic level. However, the high computational cost and poor scaling of DFT has limited its applications in two important scientific problems — large-scale atomistic simulations and high-throughput screening for materials discovery. This thesis demonstrates how the machine learning (ML) techniques enable atomistic simulations in large size and time scale with DFT-accuracy and accelerate materials discovery with the state-of-the-art graph neural network models. This thesis is divided into two topics.In the first topic (Chapters 2 and 3), we will investigate how the machine learning interatomic potentials (ML-IAPs) are trained and provide a systematic assessment of the cost and accuracy performances for several major ML-IAPs. We have also implemented high-level Python interfaces for ML-IAPs development and materials properties calculators using a molecular dynamic (MD) engine. This toolkit enabled us to develop a highly accurate and efficient ML-IAP for refractory high-entropy alloy NbMoTaW, an important alloy system yielding exceptional mechanical properties under high temperature. We will demonstrate how the ML-IAP driven atomistic simulations help us understand the mobility of edge/screw dislocations with the presence of short-range order (SRO).\nIn the second topic (Chapter 4), we developed a Bayesian Optimization With Symmetry Relaxation (BOWSR) algorithm using MatErials Graph Network (MEGNet) energy model to obtain equilibrium crystal structures, bypassing the high-cost DFT relaxations. The BOWSR algorithm enabled us to screen ∼ 400,000 transition metal borides and carbides for ultra-incompressible hard materials. Attempts were made to synthesize the top ten candidates with the highest computed bulk modulus with eight unique compositions, and two new crystals yielding ultra-incompressibility were successfully synthesized. Computer science ; Python (programming language) ; Density functional theory ; High entropy alloys ; Computational science ; Machine learning ; Bayesian optimization ; Artificial intelligence ; Statistical physics ; Materials science ; Alloy ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Phase Equilibria and Thermodynamics
Authors: Hongxing Liang | Min Xu | Edouard Asselin
Source: ChemRxiv (preprint, 2021-05-11)
Field: Materials Science — Materials Chemistry

A Study of Two-Dimensional Single Atom-Supported MXenes as Hydrogen Evolution Reaction Catalysts Using DFT and Machine Learning Enclosed you will find the article entitled “A study of two-dimensional single atom-supported MXenes as hydrogen evolution reaction catalysts using DFT and machine learning”. Existing studies predominantly focused on the hydrogen evolution reaction (HER) activities and stabilities of oxygen-terminated MXenes with single-atom loading. However, to the best of our knowledge, two-dimensional (2D) MXenes with different terminations (e.g. Br, I, Se, Te, B, Si, P, and NH) have not yet been investigated for the purposes of HER catalysis. Therefore, in this work, we considered the combined effect of the different surface terminations (B, NH, O, F, Si, P, S, Cl, Se, Br, Te, and I) and single atom loading (Ti, V, Fe, Co, Ni, Cu, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Hf, Ta, W, Re, Os, Ir, Pt, and Au) using DFT calculation. Gibbs free energy of hydrogen adsorption (reflecting activity) and the cohesive energy (a proxy for thermal stability) of these structures (264 in total) were calculated. We demonstrate that 21 uninvestigated 2D single-atom MXene catalysts, among 264 promising candidates, show an electrocatalytic activity surpassing that of platinum and a thermal stability surpassing those of synthesized borophene sheet and MoS2. Moreover, all catalysts examined in this work were further randomly separated into training and test sets with a ratio of 7:3. The HER electrocatalytic performance and thermal stability of the catalysts in the test set were predicted by machine learning algorithms. Most importantly, we present a way to provide a comparable precision (root mean square error values for the activity and thermal stability predictions are 0.158 eV and 0.02 eV, respectively) to the published machine learning works by avoiding their adoption of complex electronic features and the associated high computational cost, and by only using features that are easily available in chemical repositories. The algorithms used in this work are expected to help future researchers quickly screen single atom loaded MXenes HER catalysts at the initial design stage in a cost-effective manner. We have no financial interest in the subject or instrumentation used and there is no known conflict of interest. MXenes ; Catalysis ; Gibbs free energy ; Hydrogen ; Thermal stability ; Materials science ; Atom (system on chip) ; Density functional theory ; Physical chemistry ; Chemistry ; Nanotechnology ; Computational chemistry ; Thermodynamics ; Computer science ; Physics ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Huziel E. Sauceda | Luis E. Gálvez-González | Stefan Chmiela | Lauro Oliver Paz‐Borbón | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: arXiv (Cornell University) (preprint, 2021-06-08)
Field: Materials Science — Materials Chemistry

BIGDML: Towards Exact Machine Learning Force Fields for Materials Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene--graphene dynamics induced by nuclear quantum effects and allow to rationalize the Arrhenius behavior of hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures. Atom (system on chip) ; Molecular dynamics ; Force field (fiction) ; Computer science ; Statistical physics ; Domain (mathematical analysis) ; Materials science ; Chemical physics ; Physics ; Nanotechnology ; Artificial intelligence ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Zun Wang | Chong Wang | Sibo Zhao | Shiqiao Du | Yong Xu | Bing-Lin Gu | Wenhui Duan
Source: arXiv (Cornell University) (preprint, 2021-01-08)
Field: Materials Science — Materials Chemistry

Symmetry-adapted graph neural networks for constructing molecular dynamics force fields Molecular dynamics is a powerful simulation tool to explore material properties. Most of the realistic material systems are too large to be simulated with first-principles molecular dynamics. Classical molecular dynamics has lower computational cost but requires accurate force fields to achieve chemical accuracy. In this work, we develop a symmetry-adapted graph neural networks framework, named molecular dynamics graph neural networks (MDGNN), to construct force fields automatically for molecular dynamics simulations for both molecules and crystals. This architecture consistently preserves the translation, rotation and permutation invariance in the simulations. We propose a new feature engineering method including higher order contributions and show that MDGNN accurately reproduces the results of both classical and first-principles molecular dynamics. We also demonstrate that force fields constructed by the model has good transferability. Therefore, MDGNN provides an efficient and promising option for molecular dynamics simulations of large scale systems with high accuracy. Molecular dynamics ; Computer science ; Force field (fiction) ; Statistical physics ; Symmetry (geometry) ; Transferability ; Artificial neural network ; Translation (biology) ; Theoretical computer science ; Artificial intelligence ; Physics ; Computational chemistry ; Mathematics ; Machine learning ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Andreas Leitherer | Angelo Ziletti | Luca M. Ghiringhelli
Source: Nature Communications (preprint, 2021-10-29)
Field: Materials Science — Materials Chemistry

Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments. Artificial intelligence ; Computer science ; Deep learning ; Artificial neural network ; Machine learning ; Bayesian probability ; Bayesian network ; Noise (video) ; Pattern recognition (psychology) ; Probabilistic logic ; Unsupervised learning ; Computation ; Complex system ; Algorithm ; Image (mathematics) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Aomar Osmani | Massinissa Hamidi | Salah Bouhouche
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2021-05-18)
Field: Materials Science — Materials Chemistry

Augmented Experiment in Material Engineering Using Machine Learning The synthesis of materials using the principle of thermogravimetric analysis to discover new anticorrosive paints requires several costly experiments. This paper presents an approach combining empirical data and domain analytical models to reduce the number of real experiments required to obtain the desired synthesis. The main idea is to predict the behavior of the synthesis of two materials with well-defined mass proportions as a function of temperature. As no exact equational model exists to predict the new material, we integrate a machine learning approach circumscribed by existing domain analytical models such as heating and kinetics equations in order to derive a generative model of augmented experiments. Extensive empirical evaluation shows that using machine learning approach guided by analytic models, it is possible to substantially reduce the number of needed physical experiments without losing the approximation quality. Computer science ; Function (biology) ; Domain (mathematical analysis) ; Generative grammar ; Generative model ; Empirical modelling ; Machine learning ; Experimental data ; Quality (philosophy) ; Artificial intelligence ; Algorithm ; Mathematics ; Simulation Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Process Optimization and Integration
Authors: Weihua Hu | Muhammed Shuaibi | Abhishek Das | Siddharth Goyal | Anuroop Sriram | Jure Leskovec | Devi Parikh | Larry Zitnick
Source:  (article, 2021-05-04)
Field: Materials Science — Materials Chemistry

ForceNet: A Graph Neural Network for Large-Scale Quantum Chemistry Simulation Machine Learning (ML) has a potential to dramatically accelerate large-scale physics-based simulations. However, practical models for real large-scale and complex problems remain out of reach. Here we present ForceNet, a model for accurate and fast quantum chemistry simulations to accelerate catalyst discovery for renewable energy applications. ForceNet is a graph neural network that uses surrounding 3D molecular structure to estimate per-atom forces---a central capability for performing atomic simulations. The key challenge is to accurately capture highly complex and non-linear quantum interactions of atoms in 3D space, on which forces are dependent. To this end, ForceNet adopts (1) expressive message passing architecture, (2) appropriate choice of basis and non-linear activation functions, and (3) model scaling in terms of network depth and width. We show ForceNet reduces the estimation error of atomic forces by 30% compared to existing ML models, and generalizes well to out-of-distribution structures. Finally, we apply ForceNet to the large-scale catalyst dataset, OC20. We use ForceNet to perform quantum chemistry simulations, where ForceNet is able to achieve 4x higher success rate than existing ML models. Overall, we demonstrate the potential for ML-based simulations to achieve practical usefulness while being orders of magnitude faster than physics-based simulations. Computer science ; Scaling ; Quantum ; Quantum chemistry ; Linear scale ; Artificial neural network ; Graph ; Scale (ratio) ; Statistical physics ; Theoretical computer science ; Chemistry ; Artificial intelligence ; Catalysis ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Sadman Sadeed Omee | Steph-Yves Louis | Nihang Fu | Lai Wei | Sourin Dey | Rongzhi Dong | Qinyang Li | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2021-09-25)
Field: Materials Science — Materials Chemistry

Scalable deeper graph neural networks for high-performance materials property prediction Machine learning (ML) based materials discovery has emerged as one of the most promising approaches for breakthroughs in materials science. While heuristic knowledge based descriptors have been combined with ML algorithms to achieve good performance, the complexity of the physicochemical mechanisms makes it urgently needed to exploit representation learning from either compositions or structures for building highly effective materials machine learning models. Among these methods, the graph neural networks have shown the best performance by its capability to learn high-level features from crystal structures. However, all these models suffer from their inability to scale up the models due to the over-smoothing issue of their message-passing GNN architecture. Here we propose a novel graph attention neural network model DeeperGATGNN with differentiable group normalization and skip-connections, which allows to train very deep graph neural network models (e.g. 30 layers compared to 3-9 layers in previous works). Through systematic benchmark studies over six benchmark datasets for energy and band gap predictions, we show that our scalable DeeperGATGNN model needs little costly hyper-parameter tuning for different datasets and achieves the state-of-the-art prediction performances over five properties out of six with up to 10\% improvement. Our work shows that to deal with the high complexity of mapping the crystal materials structures to their properties, large-scale very deep graph neural networks are needed to achieve robust performances. Computer science ; Scalability ; Exploit ; Artificial intelligence ; Normalization (sociology) ; Artificial neural network ; Smoothing ; Machine learning ; Differentiable function ; Graph ; Deep learning ; Benchmark (surveying) ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Graph Neural Networks
Authors: Jonas Busk | Peter Bjørn Jørgensen | Arghya Bhowmik | Mikkel N. Schmidt | Ole Winther | Tejs Vegge
Source: Machine Learning Science and Technology (preprint, 2021-12-01)
Field: Materials Science — Materials Chemistry

Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks Abstract Data-driven methods based on machine learning have the potential to accelerate computational analysis of atomic structures. In this context, reliable uncertainty estimates are important for assessing confidence in predictions and enabling decision making. However, machine learning models can produce badly calibrated uncertainty estimates and it is therefore crucial to detect and handle uncertainty carefully. In this work we extend a message passing neural network designed specifically for predicting properties of molecules and materials with a calibrated probabilistic predictive distribution. The method presented in this paper differs from previous work by considering both aleatoric and epistemic uncertainty in a unified framework, and by recalibrating the predictive distribution on unseen data. Through computer experiments, we show that our approach results in accurate models for predicting molecular formation energies with well calibrated uncertainty in and out of the training data distribution on two public molecular benchmark datasets, QM9 and PC9. The proposed method provides a general framework for training and evaluating neural network ensemble models that are able to produce accurate predictions of properties of molecules with well calibrated uncertainty estimates. Benchmark (surveying) ; Artificial neural network ; Computer science ; Probabilistic logic ; Machine learning ; Artificial intelligence ; Uncertainty quantification ; Property (philosophy) ; Calibration ; Data mining ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Junho Jo | Eunseong Choi | Minseon Kim | Kyoungmin Min
Source: ACS Applied Energy Materials (preprint, 2021-08-05)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided Materials Design Platform for Predicting the Mechanical Properties of Na-Ion Solid-State Electrolytes Na-ion solid-state electrolytes (Na-SSEs) exhibit high potential for electrical energy storage owing to their high energy densities and low manufacturing cost. However, their mechanical properties that are critical to maintaining structural stability at the interface are still insufficiently understood. In this study, a machine learning-based regression model was developed for predicting the mechanical properties of Na-SSEs. As a training set, 12,361 materials were obtained from a well-known materials database (Materials Project) and were represented with their respective chemical and structural descriptors. The developed surrogate model exhibited remarkable accuracies (R2 score) of 0.72 and 0.87, with mean absolute errors of 11.8 and 15.3 GPa for the shear and bulk modulus, respectively. This model was then applied to predict the mechanical properties of 2432 Na-SSEs, which have been validated with first-principles calculations. Finally, the optimization process was performed to develop an ideal materials screening platform by adding the minimized data set, wherein the prediction uncertainty is reduced. We believe that the platform proposed in this study can accelerate the search for Na-SSEs with ideal mechanical properties at minimum cost. Materials science ; Electrolyte ; Shear modulus ; Interface (matter) ; Stability (learning theory) ; Material properties ; Computer science ; Ion ; Elastic modulus ; Mechanical engineering ; Thermodynamics ; Machine learning ; Composite material ; Chemistry ; Engineering ; Physics ; Physical chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials