jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thorold Tronrud | P. B. Tissera | Facundo A. Gómez | Robert J. J. Grand | Rüdiger Pakmor | Federico Marinacci | Christine M. Simpson
Source: Monthly Notices of the Royal Astronomical Society (preprint, 2022-07-22)
Field: Chemistry — Analytical Chemistry

Machine learning for galactic archaeology: a chemistry-based neural network method for identification of accreted disc stars ABSTRACT We develop a method (‘Galactic Archaeology Neural Network’, gann) based on neural network models (NNMs) to identify accreted stars in galactic discs by only their chemical fingerprint and age, using a suite of simulated galaxies from the Auriga Project. We train the network on the target galaxy’s own local environment defined by the stellar halo and the surviving satellites. We demonstrate that this approach allows the detection of accreted stars that are spatially mixed into the disc. Two performance measures are defined – recovery fraction of accreted stars, frecov and the probability that a star with a positive (accreted) classification is a true-positive result, P(TP). As the NNM output is akin to an assigned probability (Pa), we are able to determine positivity based on flexible threshold values that can be adjusted easily to refine the selection of presumed-accreted stars. We find that gann identifies accreted disc stars within simulated galaxies, with high frecov and/or high P(TP). We also find that stars in Gaia–Enceladus–Sausage (GES) mass systems are over 50 per cent recovered by our NNMs in the majority (18/24) of cases. Additionally, nearly every individual source of accreted stars is detected at 10 per cent or more of its peak stellar mass in the disc. We also demonstrate that a conglomerated NNM, trained on the halo and satellite stars from all of the Auriga galaxies provides the most consistent results, and could prove to be an intriguing future approach as our observational capabilities expand. Stars ; Galaxy ; Physics ; Astrophysics ; Halo ; Satellite galaxy ; Galactic halo ; Astronomy Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models
Authors: Aaron Philip | Guoqing Zhou | Benjamin Nebgen
Source: arXiv (Cornell University) (preprint, 2022-11-14)
Field: Materials Science — Materials Chemistry

Using Machine Learning Hamiltonians To Compute Molecular Motor Barrier Heights Machine Learning Inter-atomic Potentials (MLIPs) have become a common tool in use by computational chemists due to their combination of accuracy and speed. Yet, it is still not clear how well these tools behave at or near transitions states found in complex molecules. Here we investigate the applicability of MLIPs in evaluating the transition barrier of two, complex, molecular motor systems: a 1st generation Feringa motor and the 9c alkene 2nd generation Feringa motor. We compared paths generated with the Hierarchically Interacting Particle Neural Network (HIP-NN), the PM3 semi-empirical quantum method (SEQM), PM3 interfaced with HIP-NN (SEQM+HIP-NN), and Density Functional Theory calculations. We found that using SEQM+HIP-NN to generate cheap, realistic pathway guesses then refining the intermediates with DFT allowed us to cheaply find realistic reaction paths and energy barriers matching experiment, providing evidence that deep learning can be used for high precision computational tasks such as transition path sampling while also suggesting potential application to high throughput screening. Computer science ; Artificial neural network ; Matching (statistics) ; Density functional theory ; Sampling (signal processing) ; Quantum ; Path (computing) ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced NMR Techniques and Applications
Authors: Sajjad Heydari | Stefano Raniolo | Lorenzo Livi | Vittorio Limongelli
Source: arXiv (Cornell University) (preprint, 2022-05-06)
Field: Materials Science — Materials Chemistry

Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has use cases in chemistry, biology, and medicine. In the past decade, the advent of machine learning algorithms has impacted on molecular simulations for various tasks, including property prediction of atomistic systems. In this paper, we propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one, possessing a significantly larger number of atoms and degrees of freedom. In particular, we focus on the classification of high and low free-energy states. Our approach relies on utilizing (i) a novel hypergraph representation of molecules, encoding all relevant information for characterizing the potential energy of a conformation, and (ii) novel message passing and pooling layers for processing and making predictions on such hypergraph-structured data. Despite the complexity of the problem, our results show a remarkable AUC of 0.92 for transfer learning from tri-alanine to the deca-alanine system. Moreover, we show that the very same transfer learning approach can be used to group, in an unsupervised way, various secondary structures of deca-alanine in clusters having similar free-energy values. Our study represents a proof of concept that reliable transfer learning models for molecular systems can be designed paving the way to unexplored routes in prediction of structural and energetic properties of biologically relevant systems. Hypergraph ; Pooling ; Computer science ; Property (philosophy) ; Representation (politics) ; Degrees of freedom (physics and chemistry) ; Artificial intelligence ; Energy (signal processing) ; Theoretical computer science ; Machine learning ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hartmut Schlenz | Stefan Sandfeld
Source: Crystals (article, 2022-07-30)
Field: Materials Science — Materials Chemistry

Applications of Machine Learning to the Study of Crystalline Materials This Special Issue, “Applications of Machine Learning to the Study of Crystalline Materials”, is a collection of seven original articles published in 2021 and 2022 and dedicated to applications of machine learning in materials research [...] Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: Frederik Ørsted Kjeldal | Janus J. Eriksen
Source: arXiv (Cornell University) (preprint, 2022-12-19)
Field: Materials Science — Materials Chemistry

Decomposing Chemical Space: Applications to the Machine Learning of Atomic Energies We apply a number of atomic decomposition schemes across the standard QM7 dataset -- a small model set of organic molecules at equilibrium geometry -- to inspect the possible emergence of trends among contributions to atomization energies from distinct elements embedded within molecules. Specifically, a recent decomposition scheme of ours based on spatially localized molecular orbitals is compared to alternatives that instead partition molecular energies on account of which nuclei individual atomic orbitals are centred on. We find these partitioning schemes to expose the composition of chemical compound space in very dissimilar ways in terms of the grouping, binning, and heterogeneity of discrete atomic contributions, e.g., those associated with hydrogens bonded to different heavy atoms. Furthermore, unphysical dependencies on the one-electron basis set are found for some, but not all of these schemes. The relevance and importance of these compositional factors for training tailored neural network models based on atomic energies are next assessed. We identify both limitations and possible advantages with respect to contemporary machine learning models and discuss the design of potential counterparts based on atoms and the intrinsic energies of these as the principal decomposition units. Atomic orbital ; Partition (number theory) ; Chemical space ; Set (abstract data type) ; Space (punctuation) ; Decomposition ; Statistical physics ; Molecule ; Basis (linear algebra) ; Computer science ; Basis set ; Chemical physics ; Chemistry ; Electron ; Physics ; Mathematics ; Quantum mechanics ; Geometry ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Manleen Kaur Gujral | Reddi Kamesh | Anirban Roy
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Based Catalyst Modelling for Direct Conversion of Co2 to Lower Olefins Catalysis ; Computer science ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Albert Hofstetter | Lennard Böselt | Sereina Riniker
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Graph-convolutional neural networks for (QM)ML/MM molecular dynamics simulations To accurately study the chemical reactions in the condensed phase or within enzymes, both quantum-mechanical description and sufficient configurational sampling are required to reach converged estimates. Here, quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations play an important role, providing QM accuracy for the region of interest at a decreased computational cost. However, QM/MM simulations are still too expensive to study large systems on longer time scales. Recently, machine learning (ML) models have been proposed to replace the QM description. The main limitation of these models lies in the accurate description of long-range interactions present in condensed-phase systems. To overcome this issue, a recent workflow has been introduced combining a semi-empirical method (<i>i.e.</i> density functional tight binding (DFTB)) and a high-dimensional neural network potential (HDNNP) in a Δ-learning scheme. This approach has been shown to be capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. One of the promising alternative approaches to efficiently take long-range effects into account is the development of graph-convolutional neural networks (GCNNs) for the prediction of the potential-energy surface. In this work, we investigate the use of GCNN models - with and without a Δ-learning scheme - for (QM)ML/MM MD simulations. We show that the Δ-learning approach using a GCNN and DFTB as a baseline achieves competitive performance on our benchmarking set of solutes and chemical reactions in water. This method is additionally validated by performing prospective (QM)ML/MM MD simulations of retinoic acid in water and S-adenoslymethionine interacting with cytosine in water. The results indicate that the Δ-learning GCNN model is a valuable alternative for the (QM)ML/MM MD simulations of condensed-phase systems. Molecular dynamics ; QM/MM ; Computer science ; Potential energy surface ; Convolutional neural network ; Graph ; Quantum ; Statistical physics ; Artificial intelligence ; Machine learning ; Chemistry ; Theoretical computer science ; Physics ; Computational chemistry ; Quantum mechanics ; Ab initio Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Zhuoran Qiao | Anders S. Christensen | Matthew Welborn | Frederick R. Manby | Animashree Anandkumar | Thomas F. Miller
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-03-31)
Field: Materials Science — Materials Chemistry

OrbNet-Equi: Informing geometric deep learning with electronic interactions to accelerate quantum chemistry Source data for the manuscript "Informing geometric deep learning with electronic interactions to accelerate quantum chemistry"; The SDC21 training dataset; Neural network code and evaluation examples. Version 2 note: The license is updated. Typos in README.md and minor numerical errors in the U_0 predictions have been corrected. Quantum ; Chemistry ; Deep learning ; Computer science ; Nanotechnology ; Artificial intelligence ; Theoretical physics ; Human–computer interaction ; Physics ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; History and advancements in chemistry
Authors: A.J. Blanchard | Pei Zhang | Debsindhu Bhowmik | Kshitij Mehta | John Gounley | Samuel Temple Reeve | Stephan Irle | Massimiliano Lupo Pasini
Source: ChemRxiv (preprint, 2022-08-17)
Field: Materials Science — Materials Chemistry

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems. Chemical space ; Workflow ; Computer science ; Set (abstract data type) ; Quantum chemical ; Surrogate model ; Property (philosophy) ; Computation ; Graph ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Molecule ; Algorithm ; Drug discovery ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: S. Basak | M. Alzate Banguero | L. Burzawa | F. Simmons | Pavel Salev | L. Aigouy | M. M. Qazilbash | I. K. Schuller | D. N. Basov | A. Zimmers | E. W. Carlson
Source: arXiv (Cornell University) (preprint, 2022-11-02)
Field: Materials Science — Polymers and Plastics

Deep Learning Hamiltonians from Disordered Image Data in Quantum Materials The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length and time scales. Many such materials feature inhomogeneous electronic properties with intricate pattern formation on the observable surface. This rich spatial structure contains information about interactions, dimensionality, and disorder -- a spatial encoding of the Hamiltonian driving the pattern formation. Image recognition techniques from machine learning are an excellent tool for interpreting information encoded in the spatial relationships in such images. Here, we develop a deep learning framework for using the rich information available in these spatial correlations in order to discover the underlying Hamiltonian driving the patterns. We first vet the method on a known case, scanning near-field optical microscopy on a thin film of VO2. We then apply our trained convolutional neural network architecture to new optical microscope images of a different VO2 film as it goes through the metal-insulator transition. We find that a two-dimensional Hamiltonian with both interactions and random field disorder is required to explain the intricate, fractal intertwining of metal and insulator domains during the transition. This detailed knowledge about the underlying Hamiltonian paves the way to using the model to control the pattern formation via, e.g., tailored hysteresis protocols. We also introduce a distribution-based confidence measure on the results of a multi-label classifier, which does not rely on adversarial training. In addition, we propose a new machine learning based criterion for diagnosing a physical system's proximity to criticality. Hamiltonian (control theory) ; Computer science ; Observable ; Curse of dimensionality ; Convolutional neural network ; Artificial intelligence ; Deep learning ; Classifier (UML) ; Fractal ; Quantum ; Statistical physics ; Pattern recognition (psychology) ; Physics ; Mathematics ; Quantum mechanics Transition Metal Oxide Nanomaterials ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Andrij Vasylenko | Dmytro Antypov | В. Г. Гусев | Michael W. Gaultois | Matthew S. Dyer | Matthew J. Rosseinsky
Source: arXiv (Cornell University) (preprint, 2022-02-02)
Field: Materials Science — Materials Chemistry

Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties 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 in a given crystal structure, a material can be represented by the set of its constituent chemical elements. By working at the level of the periodic table, assessment 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 such as poor extrapolation within phase fields, and the impossibility of exhaustive sampling. This early stage discrimination combined with evaluation of novelty of phase fields aligns with the outstanding experimental challenge of identifying new areas of chemistry to investigate, by prioritising which elements to combine in a reaction. Here, we demonstrate that phase fields can be assessed with respect to the maximum expected value of a target functional property and ranked according to chemical novelty. We develop and present PhaseSelect, an end-to-end machine learning model that combines the representation, classification, regression 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 learn representation for phase fields and assess their functional performance. At the level of the periodic table, PhaseSelect quantifies the probability of observing a functional property, estimates its value within a phase field and also ranks a phase field novelty, which we demonstrate with significant accuracy for three avenues of materials applications for high-temperature superconductivity, high-temperature magnetism, and targeted bandgap energy. Ranking (information retrieval) ; Extrapolation ; Representation (politics) ; Novelty ; Property (philosophy) ; Computer science ; Field (mathematics) ; Set (abstract data type) ; Phase (matter) ; Sampling (signal processing) ; Artificial intelligence ; Machine learning ; Statistical physics ; Biological system ; Mathematics ; Chemistry ; Statistics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Jeffrey Law | Shubham Pandey | Prashun Gorai | Peter C. St. John
Source: ChemRxiv (preprint, 2022-09-19)
Field: Materials Science — Materials Chemistry

Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks The discovery of new materials in unexplored chemical spaces necessitates quick and accurate prediction of thermodynamic stability, often assessed using density functional theory (DFT), and efficient search strategies. Here, we develop a new approach to finding stable inorganic functional materials. We start by defining an upper bound to the fully-relaxed energy obtained via DFT as the energy resulting from a constrained optimization over only cell volume. Because the fractional atomic coordinates for these calculations are known a priori, this upper bound energy can be quickly and accurately predicted with a scale-invariant graph neural network (GNN). We generate new structures via ionic substitution of known prototypes, and train our GNN on a new database of 128,000 DFT calculations comprising both fully-relaxed and volume-only relaxed structures. By minimizing the predicted upper-bound energy, we discover new stable structures with over 99% accuracy (versus DFT). We demonstrate the method by finding promising new candidates for solid-state battery (SSB) electrolytes that not only possess the required stability, but also additional functional properties such as large electrochemical stability windows and high conduction ion fraction. We expect this proposed framework to be directly applicable to a wide range of design challenges in materials science. Density functional theory ; Upper and lower bounds ; Stability (learning theory) ; Computer science ; Invariant (physics) ; Minification ; Energy minimization ; Energy functional ; A priori and a posteriori ; Graph theory ; Chemical stability ; Graph ; Computational chemistry ; Theoretical computer science ; Chemistry ; Mathematics ; Physics ; Thermodynamics ; Machine learning ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Jaime Carracedo-Cosme | Carlos Romero‐Muñiz | Pablo Jauralde Pou | Rúben Pérez
Source: arXiv (Cornell University) (preprint, 2022-05-01)
Field: Materials Science — Materials Chemistry

Molecular Identification from AFM images using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks Despite being the main tool to visualize molecules at the atomic scale, AFM with CO-functionalized metal tips is unable to chemically identify the observed molecules. Here we present a strategy to address this challenging task using deep learning techniques. Instead of identifying a finite number of molecules following a traditional classification approach, we define the molecular identification as an image captioning problem. We design an architecture, composed of two multimodal recurrent neural networks, capable of identifying the structure and composition of an unknown molecule using a 3D-AFM image stack as input. The neural network is trained to provide the name of each molecule according to the IUPAC nomenclature rules. To train and test this algorithm we use the novel QUAM-AFM dataset, which contains almost 700,000 molecules and 165 million AFM images. The accuracy of the predictions is remarkable, achieving a high score quantified by the cumulative BLEU 4-gram, a common metric in language recognition studies. Chemical nomenclature ; Identification (biology) ; Computer science ; Stack (abstract data type) ; Atomic force microscopy ; Artificial neural network ; Molecule ; Artificial intelligence ; Metric (unit) ; Pattern recognition (psychology) ; Task (project management) ; Materials science ; Nanotechnology ; Chemistry ; Engineering ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods