jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jan Weinreich | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2022-12-05)
Field: Materials Science — Materials Chemistry

Encrypted machine learning of molecular quantum properties Large machine learning models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary within commercial settings, prohibiting the use of potentially extremely valuable data by others. Encrypting the prediction process can solve this problem by double-blind model evaluation and prohibits the extraction of training or query data. However, contemporary ML models based on fully homomorphic encryption or federated learning are either too expensive for practical use or have to trade higher speed for weaker security. We have implemented secure and computationally feasible encrypted machine learning models using oblivious transfer enabling and secure predictions of molecular quantum properties across chemical compound space. However, we find that encrypted predictions using kernel ridge regression models are a million times more expensive than without encryption. This demonstrates a dire need for a compact machine learning model architecture, including molecular representation and kernel matrix size, that minimizes model evaluation costs. Homomorphic encryption ; Encryption ; Computer science ; Kernel (algebra) ; Machine learning ; Process (computing) ; Artificial intelligence ; Theoretical computer science ; Data mining ; Computer security ; Mathematics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods
Authors: Gang Seob Jung | Hoon Joo Myung | Stephan Irle
Source: Research Square (preprint, 2022-01-12)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Potentials for Mechanics and Fracture Dynamics of Materials Artificial neural network ; Dynamics (music) ; Fracture mechanics ; Fracture (geology) ; Mechanics ; Computer science ; Geology ; Physics ; Artificial intelligence ; Engineering ; Geotechnical engineering ; Structural engineering ; Acoustics Machine Learning in Materials Science ; Mineral Processing and Grinding ; Material Properties and Failure Mechanisms
Authors: Sachin Gautham | Tarak K. Patra
Source: arXiv (Cornell University) (preprint, 2022-07-18)
Field: Materials Science — Materials Chemistry

Deep Learning Potential of Mean Force between Polymer Grafted Nanoparticles Grafting polymer chains on nanoparticles surfaces is a well-known route to control their self assembly and distribution in a polymer matrix. A wide variety of self assembled structures are achieved by changing the grafting patterns on an individual nanoparticle surface. However, accurate estimation of the effective potential of mean force between a pair of grafted nanoparticles that determines their assembly and distribution in a polymer matrix is an outstanding challenge in nanoscience. Here, we propose a new deep learning method that learns the interaction between a pair of grafted nanoparticles from the molecular dynamics trajectory of a cluster of polymer-grafted nanoparticles. Subsequently, we carry out the deep learning potential of mean force-based molecular simulation that predicts the self-assembly of a large number of polymer grafted nanoparticles into various anisotropic superstructures, including percolating networks and bilayers depending on nanoparticles concentration in 3D. The deep learning potential of mean force-predicted self-assembled superstructures are consistent with the actual superstructures of polymer grafted nanoparticles. This deep learning framework is very generic and can accelerate the characterization and prediction of the self-assembly and phase behaviour of polymer-grafted and unfunctionalized nanoparticles in free space or a polymer matrix. Nanoparticle ; Polymer ; Materials science ; Nanotechnology ; Molecular dynamics ; Potential of mean force ; Phase (matter) ; Matrix (chemical analysis) ; Composite material ; Chemistry ; Computational chemistry Material Dynamics and Properties ; Advanced Polymer Synthesis and Characterization ; Electrostatics and Colloid Interactions
Authors: Wei Zhu | Jiebo Luo | Andrew Dickson White
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-04-25)
Field: Materials Science — Materials Chemistry

Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting Chemistry research has both high material and computational costs to conduct experiments. Institutions thus consider chemical data valuable, and there have been few efforts to construct large public datasets for machine learning. Another challenge is that different intuitions are interested in different classes of molecules, creating heterogeneous data that cannot be easily joined by conventional distributed training. This work introduces federated heterogeneous molecular learning to address these challenges. Federated learning allows end-users to build a global model collaboratively while keeping their training data isolated. We first simulate a heterogeneous federated learning benchmark (FedChem) by jointly performing scaffold splitting and latent Dirichlet allocation on existing datasets for heterogeneously distributed client data. Our results on FedChem show that significant learning challenges arise when working with heterogeneous molecules across clients. We then propose a method to alleviate the problem: Federated Learning by Instance reweighTing (FLIT(+)). FLIT(+) can align the local training across heterogeneous clients by improving the performance for uncertain samples. Experiments conducted on FedChem validate the advantages of this method. This work should enable a new type of collaboration for improving AI in chemistry that mitigates concerns about sharing valuable chemical data. Computer science ; Artificial neural network ; Graph ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xuan Yao | Kris T. Delaney | Héctor D. Ceniceros | Glenn H. Fredrickson
Source: arXiv (Cornell University) (preprint, 2022-12-16)
Field: Materials Science — Materials Chemistry

Machine Learning and Polymer Self-Consistent Field Theory in Two Spatial Dimensions A computational framework that leverages data from self-consistent field theory simulations with deep learning to accelerate the exploration of parameter space for block copolymers is presented. This is a substantial two-dimensional extension of the framework introduced in [1]. Several innovations and improvements are proposed. (1) A Sobolev space-trained, convolutional neural network (CNN) is employed to handle the exponential dimension increase of the discretized, local average monomer density fields and to strongly enforce both spatial translation and rotation invariance of the predicted, field-theoretic intensive Hamiltonian. (2) A generative adversarial network (GAN) is introduced to efficiently and accurately predict saddle point, local average monomer density fields without resorting to gradient descent methods that employ the training set. This GAN approach yields important savings of both memory and computational cost. (3) The proposed machine learning framework is successfully applied to 2D cell size optimization as a clear illustration of its broad potential to accelerate the exploration of parameter space for discovering polymer nanostructures. Extensions to three-dimensional phase discovery appear to be feasible. Computer science ; Phase space ; Artificial intelligence ; Artificial neural network ; Discretization ; Deep learning ; Algorithm ; Saddle point ; Mathematical optimization ; Theoretical computer science ; Applied mathematics ; Mathematics ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Hao‐Cheng Thong | Xiaoyang Wang | Handong Wang | Linfeng Zhang | Ke Wang | Ben Xu
Source: arXiv (Cornell University) (preprint, 2022-03-02)
Field: Materials Science — Materials Chemistry

Machine-learning interatomic potential for molecular dynamics simulation of ferroelectric KNbO3 perovskite Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based materials have been recently demonstrated to be an excellent candidate for sustainable development. Molecular dynamics is a versatile theoretical calculation approach for the investigation of the dynamical properties of ferroelectric perovskites. However, molecular dynamics simulation of ferroelectric perovskites has been limited to simple systems, since the conventional construction of interatomic potential is rather difficult and inefficient. In the present study, we construct a machine-learning interatomic potential of KNbO3 (as a representative system of (K,Na)NbO3) by using a deep neural network model. Including first-principles calculation data into the training dataset ensures the quantum-mechanics accuracy of the interatomic potential. The molecular dynamics based on machine-learning interatomic potential shows good agreement with the first-principles calculations, which can accurately predict multiple fundamental properties, e.g., atomic force, energy, elastic properties, and phonon dispersion. In addition, the interatomic potential exhibits satisfactory performance in the simulation of domain wall and temperature-dependent phase transition. The construction of interatomic potential based on machine learning could potentially be transferred to other ferroelectric perovskites and consequently benefits the theoretical study of ferroelectrics. Interatomic potential ; Ferroelectricity ; Molecular dynamics ; Perovskite (structure) ; Statistical physics ; Materials science ; Phonon ; Piezoelectricity ; Phase transition ; Chemical physics ; Condensed matter physics ; Physics ; Chemistry ; Computational chemistry ; Crystallography ; Optoelectronics Machine Learning in Materials Science
Authors: Masaya Hagai | Mahito Sugiyama | Koji Tsuda | Takeshi Yanai
Source: ChemRxiv (preprint, 2022-09-06)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Encoding of Molecular Wavefunction for Quantum Computing Artificial neural networks (ANNs) for material modeling have received significant interest. We recently reported an adaptation of ANNs based on Boltzmann machine (BM) architectures to an ansatz of the multiconfigurational many-electron wavefunction, designated neural-network quantum state (NQS), for quantum chemistry calculations [Yang et al., J. Chem. Theory Comput., 2020, 16, 3513--3529]. Here, this study presents its extended formalism to a quantum algorithm that enables the preparation of the NQS through quantum gates. The descriptors of the ANN model, which are chosen as the occupancies of electronic configurations, are quantum-mechanically represented by qubits. Our algorithm may thus bring potential advantages over classical sampling-based computation employed in the previous studies. The NQS can be accurately formed using quantum-native procedures. Still, the training of the model in terms of energy minimization is efficiently performed on a classical computer; thus, our approach is a class of variational quantum eigensolver. The BM models are related to the Gibbs distribution, and our preparation procedures exploit techniques of quantum phase estimation but with no Hamiltonian evolution. The proposed algorithm is assessed by implementing it on a quantum computer simulator. Illustrative molecular calculations at the complete-active-space configuration interaction level of theory are displayed, confirming consistency with the accuracy of our previous classical approaches. Quantum computer ; Wave function ; Ansatz ; Quantum ; Quantum algorithm ; Boltzmann machine ; Qubit ; Artificial neural network ; Hamiltonian (control theory) ; Computer science ; Statistical physics ; Algorithm ; Quantum mechanics ; Physics ; Mathematics ; Artificial intelligence ; Mathematical optimization Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: D. V. Shein | D. V. Zavyalov | D. N. Zharikov | Д. В. Шеин | Д. В. Завьялов | Д. Н. Жариков
Source: Electronic scientific archive of UrFU (Ural Federal University) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Modeling of Phosphorene with Classical Molecular Dynamics Using Deep Learning The resulting single-layer phosphorene model created by classical molecular dynamic simulation and deep learning package has properties of the real material. Deep learning ; Molecular dynamics ; Phosphorene ; Dynamics (music) ; Deep neural networks ; Artificial neural network Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Srimanta Mitra | Aquil Ahmad | Sajib Biswas | A. K. Das
Source: arXiv (Cornell University) (preprint, 2022-08-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A machine learning approach to predict the structural and magnetic properties of Heusler alloy families Random forest (RF) regression model is used to predict the lattice constant, magnetic moment and formation energies of full Heusler alloys, half Heusler alloys, inverse Heusler alloys and quaternary Heusler alloys based on existing as well as indigenously prepared databases. Prior analysis was carried out to check the distribution of the data points of the response variables and found that in most of the cases, the data is not normally distributed. The outcome of the RF model performance is sufficiently accurate to predict the response variables on the test data and also shows its robustness against overfitting, outliers, multicollinearity and distribution of data points. The parity plots between the machine learning predicted values against the computed values using density functional theory (DFT) shows linear behavior with adjusted R2 values lying in the range of 0.80 to 0.94 for all the predicted properties for different types of Heusler alloys. Feature importance analysis shows that the valence electron numbers plays an important feature role in the prediction for most of the predicted outcomes. Case studies with one full Heusler alloy and one quaternary Heusler alloy were also mentioned comparing the machine learning predicted results with our earlier theoretical calculated values and experimentally measured results, suggesting high accuracy of the model predicted results. Overfitting ; Materials science ; Outlier ; Lattice constant ; Random forest ; Condensed matter physics ; Alloy ; Magnetic moment ; Artificial intelligence ; Computer science ; Metallurgy ; Physics ; Quantum mechanics Heusler alloys: electronic and magnetic properties
Authors: Dehao Liu | Pranav Pusarla | Yan Wang
Source:  (article, 2022-08-14)
Field: Materials Science — Materials Chemistry

Multi-Fidelity Physics-Constrained Neural Networks With Minimax Architecture for Materials Modeling Abstract Data sparsity is still the main challenge to apply machine learning models to solve complex scientific and engineering problems. The root cause is the “curse of dimensionality” in training these models. Training algorithms need to explore and exploit in a very high dimensional parameter space to search the optimal parameters for complex models. In this work, a new scheme of multi-fidelity physics-constrained neural networks with minimax architecture is proposed to improve the data efficiency of training neural networks by incorporating physical knowledge as constraints and sampling data with various fidelities. In this new framework, fully-connected neural networks with two levels of fidelities are combined to improve the prediction accuracy. The low-fidelity neural network is used to approximate the low-fidelity data, whereas the high-fidelity neural network is adopted to approximate the correlation function between the low-fidelity and high-fidelity data. To systematically search the optimal weights of various losses for reducing the training time, the Dual-Dimer algorithm is adopted to search high-order saddle points of the minimax optimization problem. The proposed framework is demonstrated with two-dimensional heat transfer, phase transition, and dendritic growth problems, which are fundamental in materials modeling. With the same set of training data, the prediction error of the multi-fidelity physics-constrained neural network with minimax architecture can be two orders of magnitude lower than that of the multi-fidelity neural network with minimax architecture. Minimax ; Artificial neural network ; Curse of dimensionality ; Computer science ; Fidelity ; Artificial intelligence ; Machine learning ; Mathematical optimization ; Algorithm ; Mathematics Machine Learning in Materials Science ; Machine Learning and ELM ; Model Reduction and Neural Networks
Authors: Matthew J. Rosseinsky | Andrij Vasylenko | Dymtro Antypov | Vladimir V. Gusev | Michael W. Gaultois | Matthew S. Dyer
Source: Research Square (preprint, 2022-08-01)
Field: Materials Science — Materials Chemistry

Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties Abstract Fundamental differences between materials originate from the unique nature of their constituent chemical elements. Before specific differences emerge according to the precise ratios of elements (the composition) in a given crystal structure (a phase), a material can be represented by its phase field defined simply as the set of the constituent chemical elements. By working at the level of the periodic table, classification of materials at the level of their phase fields reduces the combinatorial complexity to accelerate screening, and circumvents the challenges associated with composition-level approaches – poor extrapolation within phase fields, and the impossibility of exhaustive sampling. Here, we demonstrate that phase fields can be classified with respect to the maximum expected value of a target functional property and ranked according to synthetic accessibility. We develop and present PhaseSelect, an end-to-end machine learning model that combines the representation, classification, and ranking of phase fields. First, PhaseSelect constructs elemental characteristics from the co-occurrence of chemical elements in computationally and experimentally reported materials, then it employs attention mechanisms to classify the phase fields by their functional performance. At the level of the periodic table, PhaseSelect quantifies the probability of observing a functional property within a phase field and also ranks its synthetic accessibility, which we demonstrate with significant accuracy for three avenues of materials’ applications: high-temperature superconductivity, high-temperature magnetism, and targeted bandgap energy. Selection (genetic algorithm) ; Element (criminal law) ; Computer science ; Artificial intelligence ; Machine learning ; Political science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Mandeep K. Dhami | Inderpal Pasricha | Ravreet Kaur | Anmoldeep Singh Sidhu
Source:  (review, 2022-01-21)
Field: Materials Science — Materials Chemistry

A Review of Machine Learning Applications in Materials Science Materials science includes the design and discovery of new materials for manufacturing products with desired properties and functionality. The time frame for discovering new materials from initial research to first use is remarkably long. Materials design by using computer simulation and analysis leads to reduction in time and cost of materials development. One of the most exciting computational tools that has entered the materials science toolbox in recent years is machine learning, which is being applied to address various problems, such as new materials discovery, material property prediction, corrosion behavior prediction, etc. In this chapter, a review of applications of machine learning in materials science reported in the literature in recent years is presented. Computer science Machine Learning in Materials Science
Authors: David Pekker | Chungwen Liang | Sankha Pattanayak | Swagatam Mukhopadhyay
Source: arXiv (Cornell University) (preprint, 2022-08-09)
Field: Materials Science — Materials Chemistry

Machine Learning 1- and 2-electron reduced density matrices of polymeric molecules Encoding the electronic structure of molecules using 2-electron reduced density matrices (2RDMs) as opposed to many-body wave functions has been a decades-long quest as the 2RDM contains sufficient information to compute the exact molecular energy but requires only polynomial storage. We focus on linear polymers with varying conformations and numbers of monomers and show that we can use machine learning to predict both the 1-electron and the 2-electron reduced density matrices. Moreover, by applying the Hamiltonian operator to the predicted reduced density matrices we show that we can recover the molecular energy. Thus, we demonstrate the feasibility of a machine learning approach to predicting electronic structure that is generalizable both to new conformations as well as new molecules. At the same time our work circumvents the N-representability problem that has stymied the adaption of 2RDM methods, by directly machine-learning valid Reduced Density Matrices. Molecule ; Electron density ; Polymer ; Monomer ; Electron ; Focus (optics) ; Hamiltonian (control theory) ; Density matrix ; Work (physics) ; Density functional theory ; Energy (signal processing) ; Computer science ; Materials science ; Chemical physics ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Quantum mechanics ; Mathematical optimization ; Nuclear magnetic resonance ; Optics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies