jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinming Wang | Shuming Zeng | Zhuchi Wang | Jun Ni
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Identification of Crystalline Materials with Ultra-Low Thermal Conductivity Based on Machine Learning Study We have developed machine learning (ML) models to predict the thermal conductivity (κ). By analyzing the impact of different choice of descriptors and various algorithms, we find that the XGBoost algorithm based on the descriptors of crystal structural and compositional information can accurately predict the thermal conductivity with an average mean absolute error of 2.13 W m–¹ K–¹. To understand and interpret the model, we have analyzed the feature importance of the ML model. Among the top five important features, we find that the thermal conductivity is clearly partitioned when projected to the avg(ΔHₐₜₒₘᵢc)–Vₚc and Vₚₐ–Vₚc dual-descriptor spaces, which exhibit simple heuristic rules useful for searching and designing materials with favorable thermal conductivity. We employ the learned models on the materials in the entire Inorganic Crystallographic Structure Database (ICSD) and find that the heavy elements like Cs, Au, Hg, Tl, and Pb are helpful to reduce κ. Using the prediction results on the ICSD, we have screened the materials with low κ like BiTe₂Tl and Cl₂CsI and validated them using first-principles calculations, which could indicate prospective thermoelectric materials. Thermal conductivity ; Heuristic ; Chemistry ; Thermal ; Feature (linguistics) ; Thermoelectric effect ; Thermoelectric materials ; Artificial intelligence ; Simple (philosophy) ; Algorithm ; Machine learning ; Thermodynamics ; Data mining ; Pattern recognition (psychology) ; Computer science ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chemical Thermodynamics and Molecular Structure
Authors: Rohit Batra | Subramanian Sankaranarayanan
Source: arXiv (Cornell University) (preprint, 2020-04-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Multi-fidelity Scale Bridging and Dynamical\n Simulations of Materials Molecular dynamics (MD) is a powerful and popular tool for understanding the\ndynamical evolution of materials at the nano and mesoscopic scales. There are\nvarious flavors of MD ranging from the high fidelity albeit computationally\nexpensive ab-initio MD to relatively lower fidelity but much more efficient\nclassical MD such as atomistic and coarse-grained models. Each of these\ndifferent flavors of MD have been independently used by materials scientists to\nbring about breakthroughs in materials discovery and design. A significant gulf\nexists between the various MD flavors, each having varying levels of fidelity.\nThe accuracy of DFT or ab-initio MD is generally much higher than that of\nclassical atomistic simulations which is higher than that of coarse-grained\nmodels. Multi-fidelity scale bridging to combine the accuracy and flexibility\nof ab-initio MD with efficiency classical MD has been a longstanding goal. The\nadvent of big-data analytics has brought to the forefront powerful machine\nlearning methods that can be deployed to achieve this goal. Here, we provide\nour perspective on the challenges in multi-fidelity scale bridging and trace\nthe developments leading up to the use of machine learning algorithms and\ndata-science towards addressing this grand challenge.\n Bridging (networking) ; Fidelity ; Computer science ; Ab initio ; Mesoscopic physics ; Flexibility (engineering) ; Molecular dynamics ; High fidelity ; Perspective (graphical) ; Artificial intelligence ; Data science ; Machine learning ; Physics ; Chemistry ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides
Authors: Jan Weinreich | Nicholas J. Browning | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2020-12-17)
Field: Materials Science — Materials Chemistry

Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Solvation Accuracy Free energies govern the behavior of soft and liquid matter, and improved predictions could have a large impact on the development of drugs, electrolytes and homogeneous catalysts. Unfortunately, it is challenging to devise an accurate description of effects governing solvation such as hydrogen-bonding, van der Waals interactions, or entropy and conformational sampling. We present a machine learning approach for free energies based on a representation that employs computationally efficient Boltzmann averages for an approximated sampling of configurational space. Using the FreeSolv database, our out-of-sample prediction errors of experimental hydration free energies decay systematically with training set size, and experimental accuracy is reached after training on 80\% (490 molecules). Corresponding model errors are on par or better than state-of-the art, physics based, legacy approaches. To generate the input representation of a new query compound, the model requires approximate and short molecular dynamics runs. We showcase the usefulness of this method through analysis of predicted solvation free energies for 116k organic molecules (all force-field compatible molecules in QM9 database) identifying the most and least solvated systems, and rediscovering quasi-linear structure property relationships in terms of hydrogen-bond donors, number of NH or OH groups, oxygen-content in hydrocarbons, and number of heavy atoms. We observe that the model's accuracy is maximal when the temperature used for the molecular dynamics simulation to generate averaged input representation samples in training is the same as for the query compounds. Furthermore, the model's error converges w.r.t. sampling time for both training and testing. Solvation ; Statistical physics ; Molecular dynamics ; van der Waals force ; Representation (politics) ; Chemistry ; Molecule ; Space (punctuation) ; Chemical physics ; Computational chemistry ; Physics ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Henrik Klein Moberg
Source:  (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Single Nanoparticle Catalysis- Implementing Machine Learning Algorithms for the Identification of Kinetic Phase Transitions on the Surface of Individual Catalyst Nanoparticles during Surface Chemical Reactions through Nanoplasmonic Spectroscopy Nanoparticle ; Catalysis ; Phase (matter) ; Computer science ; Nanotechnology ; Materials science ; Chemical engineering ; Biological system ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Agnieszka Pocha | Tomasz Danel | Łukasz Maziarka
Source: arXiv (Cornell University) (preprint, 2020-11-23)
Field: Materials Science — Materials Chemistry

Comparison of Atom Representations in Graph Neural Networks for\n Molecular Property Prediction Graph neural networks have recently become a standard method for analysing\nchemical compounds. In the field of molecular property prediction, the emphasis\nis now put on designing new model architectures, and the importance of atom\nfeaturisation is oftentimes belittled. When contrasting two graph neural\nnetworks, the use of different atom features possibly leads to the incorrect\nattribution of the results to the network architecture. To provide a better\nunderstanding of this issue, we compare multiple atom representations for graph\nmodels and evaluate them on the prediction of free energy, solubility, and\nmetabolic stability. To the best of our knowledge, this is the first\nmethodological study that focuses on the relevance of atom representation to\nthe predictive performance of graph neural networks.\n Artificial neural network ; Computer science ; Graph ; Property (philosophy) ; Atom (system on chip) ; Representation (politics) ; Molecular graph ; Artificial intelligence ; Graph theory ; Relevance (law) ; Theoretical computer science ; Machine learning ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kin Long Kelvin Lee | Michael McCarthy
Source: Proceedings of the 2020 International Symposium on Molecular Spectroscopy (article, 2020-06-24)
Field: Chemistry — Physical and Theoretical Chemistry

IDENTIFYING UNKNOWN MOLECULES WITH PROBABILISTIC DEEP LEARNING AND ROTATIONAL SPECTROSCOPY A major bottleneck in the analysis of broadband chirped-pulse microwave spectra is the identification of unknown molecules. Often, a set of molecular frequencies are fit to an effective Hamiltonian, whereby a set of spectroscopic parameters are used to infer the molecular carrier. These constants are reproduced with electronic structure calculations through a trial and error process, accompanied by chemical intuition involving the precursors used, and the reaction conditions (e.g. electrical discharges). As the size of the molecules increase, the combinatorics of many hundreds to thousands of possible isomers becomes an intractable problem for chemical intuition alone.\n\nSince spectroscopic parameters are only weakly informative, we turn to statistical inference to complement conventional spectroscopic analysis. In this talk, I will discuss a new framework for identifying unknown molecules by performing inference on spectroscopic parameters with probabilistic deep learning. Using a series of decoder architectures, we are able to infer the approximate molecular composition/formula and what functional groups are present using only the rotational constants and derived quantities $\\kappa$ (the asymmetry parameter) and $\\Delta$ (the inertial defect), and approximate magnitudes and projections of the dipole moments. Probabilistic logic ; Spectroscopy ; Artificial intelligence ; Computer science ; Molecule ; Physics ; Astronomy Various Chemistry Research Topics ; Gas Sensing Nanomaterials and Sensors ; Water Quality Monitoring and Analysis
Authors: Tom K. Woo | Peter G. Boyd
Source: ECS Meeting Abstracts (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

(Invited) Using Computational High-Throughput Screening and Machine Learning for the Data Design of High Performance Nanoporous Materials Metal organic frameworks (MOFs) are a diverse class of materials built from organic and inorganic building units that can self assemble to form nanoporous frameworks. The hallmark feature of MOFs is the potential to tune the materials for any given application, which arises from the seemingly endless number of combinations of building units one can construct the materials from. To overcome this combinatorial design challenge we have developed a number of in silico screening tools and applied it to the development of new materials for gas separation processes such as post-combustion CO 2 capture. First we have constructed a database of millions hypothetical materials using a novel structure spawning algorithm in which MOF structures can be generated from any topological periodic net (~1800 are experimentally known). Using molecular simulation techniques, we have screened the hypothetical materials for their gas adsorption properties, which provides the locations of the guest-host binding sites in each material. From the best performing materials, a similarity analysis was performed on over 100,000 binding sites to determine if the best performing materials share any common features in their binding sites. For the application of post-combustion CO 2 capture, we found that two aromatic rings separated by 7.2 Ang provide an ideal, hydrophobic binding pocket that selectively adsorbs CO 2 . Our experimental collaborators were then able to synthesize 2 new MOFs with the targeted binding pockets and experimentally confirm that CO 2 binds in them as predicted. To date these MOFs are amongst the best materials for selective CO 2 capture under realistic humid combustion gas streams. Time permitting, we will also present how we have used deep learning models of nanoporous materials to rapidly predict the low-pressure adsorption properties of MOFs. Under these conditions, the chemistry of the pores is important and the geometric features such as the pore size, are not good predictors of the performance of the materials. Using a distance-between-features descriptor that accounts for the local chemistry of the materials, we have been able to develop accurate models that can predict the adsorption properties of nanoporous materials. Models were trained and tested on large, diverse databases of over 330,000 MOFs giving correlation R 2 values on the test sets of &gt;0.95, while geometric features provide R 2 values of 0.71. Nanoporous ; In silico ; Adsorption ; Metal-organic framework ; Computer science ; Nanotechnology ; Materials science ; Chemistry Machine Learning in Materials Science
Authors: George S. Fanourgakis | Konstantinos Gkagkas | Emmanuel Tylianakis | George E. Froudakis
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

A Generic Machine Learning Algorithm for the Prediction of Gas Adsorption in Nanoporous Materials In the present study, we propose a new set of descriptors, appropriate for machine learning (ML) methods, aiming to predict accurately the gas adsorption capacities of nanoporous materials. The present work focuses on systems with nonnegligible electrostatic interactions between the materials’ framework and the guest gas. For that, the CO₂, H₂, and H₂S gases are examined. The present approach is a generalization of our recent development for guest gases with no electrostatic interactions, such as CH₄. For both types of systems, as ML descriptors we consider the adsorption probabilities by the materials’ framework of a small number of probe atoms with different van der Waals diameters. After examination and evaluation of various numerical schemes, probe atoms that carry in their centers an electric dipole are found to be the most appropriate for systems with electrostatic interactions. The accuracy of the present approach is assessed by comparing the ML predictions with a data set of reference results obtained after performing grand canonical Monte Carlo (GCMC) simulations. More specifically, the CO₂, H₂, and H₂S adsorption capacities of the computation-ready, experimental (CoRE) MOFs at several different thermodynamic conditions are considered. The low computational cost for the calculation of the proposed set of ML descriptors allows the screening of very large databases. Nanoporous ; Adsorption ; Chemistry ; van der Waals force ; Computation ; Generalization ; Set (abstract data type) ; Dipole ; Grand canonical ensemble ; Monte Carlo method ; Electrostatics ; Statistical physics ; Molecule ; Algorithm ; Computer science ; Physical chemistry ; Physics ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Baldur Steingrimsson | Xuesong Fan | Anand J. Kulkarni | Michael C. Gao | Peter K. Liaw
Source: arXiv (Cornell University) (preprint, 2020-12-05)
Field: Materials Science — Materials Chemistry

Machine Learning and Data Analytics for Design and Manufacturing of\n High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of\n Interest This chapter presents an innovative framework for the application of machine\nlearning and data analytics for the identification of alloys or composites\nexhibiting certain desired properties of interest. The main focus is on alloys\nand composites with large composition spaces for structural materials. Such\nalloys or composites are referred to as high-entropy materials (HEMs) and are\nhere presented primarily in context of structural applications. For each output\nproperty of interest, the corresponding driving (input) factors are identified.\nThese input factors may include the material composition, heat treatment,\nmanufacturing process, microstructure, temperature, strain rate, environment,\nor testing mode. The framework assumes the selection of an optimization\ntechnique suitable for the application at hand and the data available.\nPhysics-based models are presented, such as for predicting the ultimate tensile\nstrength (UTS) or fatigue resistance. We devise models capable of accounting\nfor physics-based dependencies. We factor such dependencies into the models as\na priori information. In case that an artificial neural network (ANN) is deemed\nsuitable for the applications at hand, it is suggested to employ custom kernel\nfunctions consistent with the underlying physics, for the purpose of attaining\ntighter coupling, better prediction, and for extracting the most out of the -\nusually limited - input data available.\n Artificial neural network ; Computer science ; Analytics ; Context (archaeology) ; Machine learning ; Mechanical engineering ; Artificial intelligence ; Materials science ; Data mining ; Engineering Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Aditi S. Krishnapriyan | Joseph Montoya | Maciej Harańczyk | Jens S. Hummelshøj | Dmitriy Morozov
Source: arXiv (Cornell University) (preprint, 2020-10-01)
Field: Materials Science — Materials Chemistry

Machine learning with persistent homology and chemical word embeddings\n improves prediction accuracy and interpretability in metal-organic frameworks Machine learning has emerged as a powerful approach in materials discovery.\nIts major challenge is selecting features that create interpretable\nrepresentations of materials, useful across multiple prediction tasks. We\nintroduce an end-to-end machine learning model that automatically generates\ndescriptors that capture a complex representation of a material's structure and\nchemistry. This approach builds on computational topology techniques (namely,\npersistent homology) and word embeddings from natural language processing. It\nautomatically encapsulates geometric and chemical information directly from the\nmaterial system. We demonstrate our approach on multiple nanoporous\nmetal-organic framework datasets by predicting methane and carbon dioxide\nadsorption across different conditions. Our results show considerable\nimprovement in both accuracy and transferability across targets compared to\nmodels constructed from the commonly-used, manually-curated features,\nconsistently achieving an average 25-30% decrease in\nroot-mean-squared-deviation and an average increase of 40-50% in R2 scores. A\nkey advantage of our approach is interpretability: Our model identifies the\npores that correlate best to adsorption at different pressures, which\ncontributes to understanding atomic-level structure--property relationships for\nmaterials design.\n Interpretability ; Artificial intelligence ; Computer science ; Machine learning ; Nanoporous ; Transferability ; Representation (politics) ; Persistent homology ; Natural language processing ; Algorithm ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Yen‐Lin Chen | Lois Pollack
Source: Biophysical Journal (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

Convolutional Neural Networks Bridge Molecular Models and Solution X-ray Scattering Experiments Scattering ; Convolutional neural network ; Small-angle X-ray scattering ; Biological system ; RADIUS ; Physics ; Crystallography ; Computer science ; Materials science ; Optics ; Chemistry ; Artificial intelligence ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Fuel Cells and Related Materials
Authors: Sijia S. Dong | Marco Govoni | Giulia Galli
Source: arXiv (Cornell University) (preprint, 2020-12-22)
Field: Materials Science — Materials Chemistry

Machine Learning Dielectric Screening for the Simulation of Excited\n State Properties of Molecules and Materials Accurate and efficient calculations of absorption spectra of molecules and\nmaterials are essential for the understanding and rational design of broad\nclasses of systems. Solving the Bethe-Salpeter equation (BSE) for electron-hole\npairs usually yields accurate predictions of absorption spectra, but it is\ncomputationally expensive, especially if thermal averages of spectra computed\nfor multiple configurations are required. We present a method based on machine\nlearning to evaluate a key quantity entering the definition of absorption\nspectra: the dielectric screening. We show that our approach yields a model for\nthe screening that is transferable between multiple configurations sampled\nduring first principles molecular dynamics simulations; hence it leads to a\nsubstantial improvement in the efficiency of calculations of finite temperature\nspectra. We obtained computational gains of one to two orders of magnitude for\nsystems with 50 to 500 atoms, including liquids, solids, nanostructures, and\nsolid/liquid interfaces. Importantly, the models of dielectric screening\nderived here may be used not only in the solution of the BSE but also in\ndeveloping functionals for time-dependent density functional theory (TDDFT)\ncalculations of homogeneous and heterogeneous systems. Overall, our work\nprovides a strategy to combine machine learning with electronic structure\ncalculations to accelerate first principles simulations of excited-state\nproperties.\n Excited state ; Dielectric ; Absorption (acoustics) ; Work (physics) ; Spectral line ; Statistical physics ; Time-dependent density functional theory ; Density functional theory ; Absorption spectroscopy ; Materials science ; Computational physics ; Computer science ; Molecular physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies