jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Søren Holm | Pablo A. Unzueta | Keiran C. Thompson | Todd J. Martı́nez
Source: Journal of Chemical Theory and Computation (article, 2023-05-16)
Field: Materials Science — Materials Chemistry

Single-Point Extrapolation to the Complete Basis Set Limit through Deep Learning Machine learning (ML) offers an attractive method for making predictions about molecular systems while circumventing the need to run expensive electronic structure calculations. Once trained on ab initio data, the promise of ML is to deliver accurate predictions of molecular properties that were previously computationally infeasible. In this work, we develop and train a graph neural network model to correct the basis set incompleteness error (BSIE) between a small and large basis set at the RHF and B3LYP levels of theory. Our results show that, when compared to fitting to the total potential, an ML model fitted to correct the BSIE is better at generalizing to systems not seen during training. We test this ability by training on single molecules while evaluating on molecular complexes. We also show that ensemble models yield better behaved potentials in situations where the training data is insufficient. However, even when only fitting to the BSIE, acceptable performance is only achieved when the training data sufficiently resemble the systems one wants to make predictions on. The test error of the final model trained to predict the difference between the cc-pVDZ and cc-pV5Z potential is 0.184 kcal/mol for the B3LYP density functional, and the ensemble model accurately reproduces the large basis set interaction energy curves on the S66x8 dataset. Extrapolation ; Computer science ; Basis (linear algebra) ; Test set ; Set (abstract data type) ; Artificial neural network ; Training set ; Basis set ; Limit (mathematics) ; Artificial intelligence ; Machine learning ; Reference data ; Algorithm ; Density functional theory ; Data mining ; Mathematics ; Computational chemistry ; Chemistry ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Frédéric Célerse | Matthew D. Wodrich | Sergi Vela | Simone Gallarati | Raimón Fabregat | Veronika Jurásková | Clémence Corminbœuf
Source: ChemRxiv (preprint, 2023-12-06)
Field: Materials Science — Materials Chemistry

From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-based Potentials Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free en- ergy landscape of flexible functional organic molecules. Curating such databases for species beyond “simple” drug-like compounds or molecules comprised of well-defined building blocks (e.g., peptides) is challenging, as it requires thorough chemical space mapping and evaluation of both chemical and conformational diversity. Here, we intro- duce the OFF–ON (Organic Fragments From Organocatalysts that are Non-modular) database, a repository of 7,869 equilibrium and 67,457 non–equilibrium geometries of organic compounds and dimers aimed at describing conformationally flexible func- tional organic molecules, with an emphasis on photoswitchable organocatalysts. The relevance of this database is then demonstrated by training a Local Kernel Regres- sion model on a low-cost semiempirical baseline and comparing it with a PBE0-D3 reference for several known catalysts, notably the free energy surfaces of exemplary photoswitchable organocatalysts. Our results demonstrate that the OFF–ON dataset offers reliable predictions for simulating the conformational behavior of virtually any (photoswitchable) organocatalyst or organic compound comprised of H, C, N, O, F, and S atoms, thereby opening a computationally feasible route to explore complex free energy surfaces in order to rationalize and predict catalytic behavior. Kernel (algebra) ; Organic molecules ; Modular design ; Chemical space ; Chemistry ; Molecule ; Catalysis ; Order (exchange) ; Combinatorial chemistry ; Computational chemistry ; Computer science ; Biological system ; Chemical physics ; Drug discovery ; Organic chemistry ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Susanta Das | Kenneth M. Merz
Source: ChemRxiv (preprint, 2023-11-29)
Field: Chemistry — Spectroscopy

Molecular Gas Phase Conformational Ensembles Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating the global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field (FF) based approaches. To validate these methods, we propose the use of collisional cross section (CCS) values obtained from ion mobility – mass spectrometry (IM-MS) studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules, which can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformation generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with a particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows in predicting the structures of unknown compounds with high precision. Maxima and minima ; Gas phase ; Workflow ; Computer science ; Chemical space ; Force field (fiction) ; Molecular dynamics ; Drug discovery ; Chemistry ; Computational chemistry ; Artificial intelligence ; Mathematics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Massimiliano Lupo Pasini | Mariia Karabin | Markus Eisenbach
Source: ChemRxiv (preprint, 2023-06-14)
Field: Materials Science — Materials Chemistry

Transferable prediction of formation energy across lattices of increasing size In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of solid solution alloys across atomic structures of increasing sizes, which was utilized in the cost-efficient sampling strategy. The GCNN was trained on a nickel-platinum (NiPt) dataset generated with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) using the second nearest-neighbor modified embedded-atom method (2NN MEAM) empirical interatomic potential. The dataset has been obtained by optimizing the geometries of initially randomly generated FCC crystal structures and calculating the formation energy, with configurations spanning the whole compositional range. The GCNN was first trained on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2,048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes. Using this additional data, the predictions of the GCNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes. Transferability ; Lattice (music) ; Scalability ; Atom (system on chip) ; Range (aeronautics) ; Nial ; Computer science ; k-nearest neighbors algorithm ; Materials science ; Statistical physics ; Physics ; Machine learning ; Alloy ; Parallel computing ; Intermetallic Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Jonas Busk | Mikkel N. Schmidt | Ole Winther | Tejs Vegge | Peter Bjørn Jørgensen
Source: arXiv (Cornell University) (preprint, 2023-05-10)
Field: Materials Science — Materials Chemistry

Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting and applying interatomic forces. In these settings, it is crucial to detect when predictions are unreliable to avoid wrong or misleading results. Here, we present a complete framework for training and recalibrating graph neural network ensemble models to produce accurate predictions of energy and forces with calibrated uncertainty estimates. The proposed method considers both epistemic and aleatoric uncertainty and the total uncertainties are recalibrated post hoc using a nonlinear scaling function to achieve good calibration on previously unseen data, without loss of predictive accuracy. The method is demonstrated and evaluated on two challenging, publicly available datasets, ANI-1x (Smith et al.) and Transition1x (Schreiner et al.), both containing diverse conformations far from equilibrium. A detailed analysis of the predictive performance and uncertainty calibration is provided. In all experiments, the proposed method achieved low prediction error and good uncertainty calibration, with predicted uncertainty correlating with expected error, on energy and forces. To the best of our knowledge, the method presented in this paper is the first to consider a complete framework for obtaining calibrated epistemic and aleatoric uncertainty predictions on both energy and forces in ML potentials. Uncertainty quantification ; Calibration ; Computer science ; Artificial neural network ; Propagation of uncertainty ; Function (biology) ; Nonlinear system ; Measurement uncertainty ; Graph ; Scaling ; Energy (signal processing) ; Machine learning ; Artificial intelligence ; Statistical physics ; Algorithm ; Mathematics ; Physics ; Theoretical computer science ; Statistics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Graph Neural Networks
Authors: Gabriel S. Gusmão | Andrew J. Medford
Source: arXiv (Cornell University) (preprint, 2023-04-12)
Field: Materials Science — Materials Chemistry

Maximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problems Physics-informed neural networks (PINNs) have proven a suitable mathematical scaffold for solving inverse ordinary (ODE) and partial differential equations (PDE). Typical inverse PINNs are formulated as soft-constrained multi-objective optimization problems with several hyperparameters. In this work, we demonstrate that inverse PINNs can be framed in terms of maximum-likelihood estimators (MLE) to allow explicit error propagation from interpolation to the physical model space through Taylor expansion, without the need of hyperparameter tuning. We explore its application to high-dimensional coupled ODEs constrained by differential algebraic equations that are common in transient chemical and biological kinetics. Furthermore, we show that singular-value decomposition (SVD) of the ODE coupling matrices (reaction stoichiometry matrix) provides reduced uncorrelated subspaces in which PINNs solutions can be represented and over which residuals can be projected. Finally, SVD bases serve as preconditioners for the inversion of covariance matrices in this hyperparameter-free robust application of MLE to ``kinetics-informed neural networks''. Singular value decomposition ; Estimator ; Hyperparameter ; Applied mathematics ; Ode ; Ordinary differential equation ; Artificial neural network ; Mathematics ; Uncertainty quantification ; Inverse problem ; Jacobian matrix and determinant ; Mathematical optimization ; Computer science ; Algorithm ; Differential equation ; Mathematical analysis ; Artificial intelligence Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Applications
Authors: Pranas Juknevicius | Jevgenij Chmeliov | Leonas Valkūnas | Andrius Gelžinis
Source: AIP Advances (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Application of artificial neural networks for modeling of electronic excitation dynamics in 2D lattice: Direct and inverse problems Machine learning (ML) approaches are attracting wide interest in the chemical physics community since a trained ML system can predict numerical properties of various molecular systems with a small computational cost. In this work, we analyze the applicability of deep, sequential, and fully connected neural networks (NNs) to predict the excitation decay kinetics of a simple two-dimensional lattice model, which can be adapted to describe numerous real-life systems, such as aggregates of photosynthetic molecular complexes. After choosing a suitable loss function for NN training, we have achieved excellent accuracy for a direct problem—predictions of lattice excitation decay kinetics from the model parameter values. For an inverse problem—prediction of the model parameter values from the kinetics—we found that even though the kinetics obtained from estimated values differ from the actual ones, the values themselves are predicted with a reasonable accuracy. Finally, we discuss possibilities for applications of NNs for solving global optimization problems that are related to the need to fit experimental data using similar models. Excitation ; Artificial neural network ; Lattice (music) ; Inverse problem ; Computer science ; Statistical physics ; Inverse ; Biological system ; Algorithm ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Z. Shahri | Saeed Reza Allahkaram | Reza Soltani | Hassan Jafari
Source: Journal of Materials Engineering and Performance (article, 2023-07-28)
Field: Materials Science — Materials Chemistry

Modeling of PEO Coatings by Coupling an Artificial Neural Network and Taguchi Design of Experiment Materials science ; Corrosion ; Taguchi methods ; Duty cycle ; Dielectric spectroscopy ; Artificial neural network ; Electrolyte ; Correlation coefficient ; Alloy ; Microstructure ; Composite material ; Metallurgy ; Electrochemistry ; Voltage ; Machine learning ; Computer science Corrosion Behavior and Inhibition ; Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics
Authors: Ting Zhang | Joshua Yuk Lin Lai | Mingzhe Shi | Qing Li | He Yan | Chen Zhang
Source: Research Square (preprint, 2023-06-02)
Field: Materials Science — Materials Chemistry

Expert-assisted and sub-unit-based molecular description enables ultra-fast and highly accurate prediction of the energy levels of non-fullerene acceptors in organic solar cells Abstract Non-fullerene acceptors (NFAs) have recently emerged as an important class of materials enabling high-efficiency organic solar cells. However, most research activities on NFAs are carried out through a time-consuming trial-and-error experimental process with limited predictability. Therefore, there is a pressing need to develop a fast and efficient computational method to predict their properties, particularly their energy levels. Unfortunately, conventional computational or machine learning methods tend to produce large errors (0.2 - 0.5 eV) in energy level prediction. Moreover, the commonly used experimental method is also prone to large errors in determining the energy levels of NFAs. This paper introduces a novel ultra-fast machine learning method that accurately predicts NFA energy levels within only one second of computation on a common laptop computer. The method consists of two components: data cleansing and an expert-assisted sub-unit-based molecular description, which simplifies the complexity of molecular computation. Despite the limited data available for NFAs (less than a thousand), the method achieves a small average prediction error of 0.06 eV for energy levels, significantly outperforming known computational methods and even surpassing the classic experimental method, cyclic voltammetry (CV). This method is expected to accelerate NFA research and inspire the development of similar computation approaches to solve other material or molecular science problems. Laptop ; Computer science ; Computation ; Energy (signal processing) ; Process (computing) ; Efficient energy use ; Predictability ; Computational science ; Computational resource ; Computational complexity theory ; Computer engineering ; Artificial intelligence ; Algorithm ; Biological system ; Mathematics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures
Authors: Shashank Pathrudkar | Ponkrshnan Thiagarajan | Shivang Agarwal | Amartya Banerjee | Susanta Ghosh
Source: arXiv (Cornell University) (preprint, 2023-08-24)
Field: Materials Science — Materials Chemistry

Electronic Structure Prediction of Multi-million Atom Systems Through Uncertainty Quantification Enabled Transfer Learning The ground state electron density -- obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations -- contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident -- and when verifiable, accurate -- predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at unprecedented, multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources. Leverage (statistics) ; Computer science ; Density functional theory ; Bayesian probability ; Machine learning ; Scalar (mathematics) ; Statistical physics ; Artificial intelligence ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electron and X-Ray Spectroscopy Techniques
Authors: Massimiliano Lupo Pasini | Mariia Karabin | Markus Eisenbach
Source: ChemRxiv (preprint, 2023-07-11)
Field: Materials Science — Materials Chemistry

Transferable prediction of formation energy across lattices of increasing size In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum (NiPt) solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) using the second nearest-neighbor modified embedded-atom method (2NN MEAM) empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic (FCC) crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of geometry optimization, we first trained the GCNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2,048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of the GCNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes. Transferability ; Energy minimization ; Lattice (music) ; Atom (system on chip) ; Range (aeronautics) ; Crystal structure ; Materials science ; Scalability ; Computer science ; Statistical physics ; Molecular physics ; Chemistry ; Crystallography ; Physics ; Computational chemistry ; Machine learning ; Parallel computing Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Catalytic Processes in Materials Science
Authors: Martin Vondrák | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2023-05-04)
Field: Materials Science — Materials Chemistry

q-pac: A Python Package for Machine Learned Charge Equilibration Models Many state-of-the art machine learning (ML) interatomic potentials are based on a local or semi-local (message-passing) representation of chemical environments. They therefore lack a description of long-range electrostatic interactions and non-local charge transfer. In this context, there has been much interest in developing ML-based charge equilibration models, which allow the rigorous calculation of long-range electrostatic interactions and the energetic response of molecules and materials to external fields. The recently reported kQEq method achieves this by predicting local atomic electronegativities using Kernel ML. This paper describes the q-pac Python package, which implements several algorithmic and methodological advances to kQEq and provides an extendable framework for the development of ML charge equilibration models. Python (programming language) ; Computer science ; Electronegativity ; Charge (physics) ; Electrostatics ; Range (aeronautics) ; Computational science ; Representation (politics) ; Kernel (algebra) ; Statistical physics ; Algorithm ; Physics ; Programming language ; Materials science ; Mathematics ; Quantum mechanics ; Discrete mathematics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; CO2 Reduction Techniques and Catalysts