jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rohit Batra | Ghanshyam Pilania | Blas P. Uberuaga | Rampi Ramprasad
Source: ACS Applied Materials & Interfaces (article, 2019-04-16)
Field: Materials Science — Materials Chemistry

Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia Cost versus accuracy trade-offs are frequently encountered in materials science and engineering, where a particular property of interest can be measured/computed at different levels of accuracy or fidelity. Naturally, the most accurate measurement is also the most resource and time intensive, while the inexpensive quicker alternatives tend to be noisy. In such situations, a number of machine learning (ML) based multifidelity information fusion (MFIF) strategies can be employed to fuse information accessible from varying sources of fidelity and make predictions at the highest level of accuracy. In this work, we perform a comparative study on traditionally employed single-fidelity and three MFIF strategies, namely, (1) Δ-learning, (2) low-fidelity as a feature, and (3) multifidelity cokriging (CK) to compare their relative prediction accuracies and efficiencies for accelerated property predictions and high throughput chemical space explorations. We perform our analysis using a dopant formation energy data set for hafnia, which is a well-known high-<i>k</i> material and is being extensively studied for its promising ferroelectric, piezoelectric, and pyroelectric properties. We use a dopant formation energy data set of 42 dopants in hafnia-each studied in six different hafnia phases-computed at two levels of fidelities to find merits and limitations of these ML strategies. The findings of this work indicate that the MFIF based learning schemes outperform the traditional SF machine learning methods, such as Gaussian process regression and CK provides an accurate, inexpensive and flexible alternative to other MFIF strategies. While the results presented here are for the case study of hafnia, they are expected to be general. Therefore, materials discovery problems that involve huge chemical space explorations can be studied efficiently (or even made feasible in some situations) through a combination of a large number of low-fidelity and a few high-fidelity measurements/computations, in conjunction with the CK approach. Hafnia ; Dopant ; Fusion ; Information fusion ; Fidelity ; Materials science ; Computer science ; Doping ; Artificial intelligence ; Chemical physics ; Physics ; Optoelectronics ; Composite material ; Telecommunications Machine Learning in Materials Science
Authors: J. M. García‐Lastra | Jin Hyun Chang | Tejs Vegge
Source: American Chemical Society. Abstracts of Papers (at the National Meeting) (article, 2019-01-01)
Field: Materials Science — Surfaces, Coatings and Films

CLEASE: Implementation of cluster expansion method in atomic simulation environment package Cluster Expansion (CE) is a method that has been used successfully in the past few decades to parameterize and express the configurational dependence of the scalar physical properties. The most widely parameterized physical property is energy computed using first-principles methods, but CE can also be used to parameterize other quantities such as band gap and density of states. In spite of its success and usefulness, CE remains as a niche tool used in a small subfield within the computational materials science, primarily used by specialists. Here, we present a new and versatile implementation of the CE method which is integrated as a part of Atomic Simulation Environment (ASE) open-source package. The code automates the complex set up and construction procedure of cluster expansion while giving the users the flexibility to tweak the settings and to import their own structures and previous calculation results. Recent advancements in cluster expansion such as regularization techniques from machine learning and statistics are implemented in the developed code. The code allows the users to construct cluster expansion on any bulk lattice structure, which makes it useful for a wide range of applications involving complex materials. Two types of materials, a binary metal alloy and a disordered battery cathode material, are analyzed using cluster expansion method to demonstrate the implementation. Cluster (spacecraft) ; R package ; Computer science ; Cluster expansion ; Computational science ; Physics ; Operating system ; Thermodynamics Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Machine Learning in Materials Science
Authors: Ludwig Holleis | B. S. Shivaram | Prasanna V. Balachandran
Source: Bulletin of the American Physical Society (article, 2019-03-04)
Field: Materials Science — Materials Chemistry

Machine Learning Approach for the Discovery of Enhanced Magnetocaloric Effect in Single Molecule Magnets Magnetic refrigeration ; Magnet ; Materials science ; Molecule ; Physics ; Mechanical engineering ; Engineering ; Magnetic field ; Quantum mechanics ; Magnetization Machine Learning in Materials Science ; Magnetic properties of thin films ; Magnetic and transport properties of perovskites and related materials
Authors: Kevin Tran | Willie Neiswanger | Junwoong Yoon | Qingyang Zhang | Eric P. Xing | Zachary W. Ulissi
Source: arXiv (Cornell University) (preprint, 2019-12-20)
Field: Materials Science — Materials Chemistry

Methods for comparing uncertainty quantifications for material property\n predictions Data science and informatics tools have been proliferating recently within\nthe computational materials science and catalysis fields. This proliferation\nhas spurned the creation of various frameworks for automated materials\nscreening, discovery, and design. Underpinning these frameworks are surrogate\nmodels with uncertainty estimates on their predictions. These uncertainty\nestimates are instrumental for determining which materials to screen next, but\nthe computational catalysis field does not yet have a standard procedure for\njudging the quality of such uncertainty estimates. Here we present a suite of\nfigures and performance metrics derived from the machine learning community\nthat can be used to judge the quality of such uncertainty estimates. This suite\nprobes the accuracy, calibration, and sharpness of a model quantitatively. We\nthen show a case study where we judge various methods for predicting\ndensity-functional-theory-calculated adsorption energies. Of the methods\nstudied here, we find that the best performer is a model where a convolutional\nneural network is used to supply features to a Gaussian process regressor,\nwhich then makes predictions of adsorption energies along with corresponding\nuncertainty estimates.\n Uncertainty quantification ; Suite ; Computer science ; Calibration ; Field (mathematics) ; Convolutional neural network ; Property (philosophy) ; Gaussian process ; Gaussian ; Underpinning ; Toolbox ; Process (computing) ; Machine learning ; Quality (philosophy) ; Artificial intelligence ; Data mining ; Mathematics ; Chemistry ; Statistics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Michele Ceriotti
Source: arXiv (Cornell University) (article, 2019-02-13)
Field: Materials Science — Materials Chemistry

Atomistic machine learning between predictions and understanding Automated analyses of the outcome of a simulation have been an important part of atomistic modeling since the early days, addressing the need of linking the behavior of individual atoms and the collective properties that are usually the final quantity of interest. Methods such as clustering and dimensionality reduction have been used to provide a simplified, coarse-grained representation of the structure and dynamics of complex systems, from proteins to nanoparticles. In recent years, the rise of machine learning has led to an even more widespread use of these algorithms in atomistic modeling, and to consider different classification and inference techniques as part of a coherent toolbox of data-driven approaches. This perspective briefly reviews some of the unsupervised machine-learning methods -- that are geared towards classification and coarse-graining of molecular simulations -- seen in relation to the fundamental mathematical concepts that underlie all machine-learning techniques. It discusses the importance of using concise yet complete representations of atomic structures as the starting point of the analyses, and highlights the risk of introducing preconceived biases when using machine learning to rationalize and understand structure-property relations. Supervised machine-learning techniques, that explicitly attempt to predict the properties of a material given its structure, are less susceptible to such biases. Current developments in the field suggest that using these two classes of approaches side-by-side and in a fully integrated mode, while keeping in mind the relations between the data analysis framework and the fundamental physical principles, will be key for realizing the full potential of machine learning to help understanding the behavior of complex molecules and materials. Machine learning ; Artificial intelligence ; Computer science ; Toolbox ; Inference ; Cluster analysis ; Representation (politics) ; Field (mathematics) ; Unsupervised learning ; Granularity ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Mass Spectrometry Techniques and Applications
Authors: Aditya Nandy | Jiazhou Zhu | Jon Paul Janet | Chenru Duan | Rachel B. Getman | Heather J. Kulik
Source: ChemRxiv (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation &lt;p&gt;Metal-oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal-oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure-property relationships. To overcome these challenges, we train the first machine learning (ML) models capable of predicting metal-oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network ML models, we achieve good mean absolute errors (4-5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of non-local, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing both expected trends in oxo formation energetics, such as destabilization of the metal-oxo species with increasing &lt;i&gt;d&lt;/i&gt;-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty aware evolutionary optimization using the ANN to explore a &gt; 37,000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counter-intuitive oxo-formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions.&lt;/p&gt; Chemistry ; Density functional theory ; Catalysis ; Metal ; Transition metal ; Ligand (biochemistry) ; Spin states ; Oxidation state ; Computational chemistry ; Chemical physics ; Inorganic chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Grant Hill
Source: ePrints Soton (University of Southampton) (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

ISTCP 2019: 10th Triennial Congress of the International Society for Theoretical Chemical Physics This seven-day conference mainly consisted of a mixture of plenary lectures and parallel presentation sessions, with two poster sessions and a session on European Research Council funding. Each day had two coffee breaks and time for lunch, which, combined with the poster sessions, allowed time for networking. The conference was broad in its overall scope â covering most aspects of theoretical chemical physics / physical chemistry, but three sessions were dedicated to machine learning / AI. A number of talks within other sessions also contained significant AI for chemical discovery content too. Session (web analytics) ; Presentation (obstetrics) ; Scope (computer science) ; Library science ; Computer science ; Medicine ; World Wide Web Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: David Bradley
Source: Materials Today (article, 2019-08-19)
Field: Materials Science — Materials Chemistry

Automated materials knowledge extraction Chemical space ; Computer science ; Property (philosophy) ; Collation ; Set (abstract data type) ; Space (punctuation) ; Curiosity ; Data science ; Characterization (materials science) ; Biochemical engineering ; Nanotechnology ; Chemistry ; Materials science ; Engineering ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Julian Lißner | Felix Fritzen
Source: arXiv (Cornell University) (preprint, 2019-03-26)
Field: Materials Science — Materials Chemistry

Data-Driven Microstructure Property Relations An image based prediction of the effective heat conductivity for highly heterogeneous microstructured materials is presented. The synthetic materials under consideration show different inclusion morphology, orientation, volume fraction and topology. The prediction of the effective property is made exclusively based on image data with the main emphasis being put on the 2-point spatial correlation function. This task is implemented using both unsupervised and supervised machine learning methods. First, a snapshot proper orthogonal decomposition (POD) is used to analyze big sets of random microstructures and thereafter compress significant characteristics of the microstructure into a low-dimensional feature vector. In order to manage the related amount of data and computations, three different incremental snapshot POD methods are proposed. In the second step, the obtained feature vector is used to predict the effective material property by using feed forward neural networks. Numerical examples regarding the incremental basis identification and the prediction accuracy of the approach are presented. A Python code illustrating the application of the surrogate is freely available. Snapshot (computer storage) ; Computer science ; Python (programming language) ; Computation ; Proper orthogonal decomposition ; Algorithm ; Support vector machine ; Feature vector ; Artificial intelligence ; Pattern recognition (psychology) ; Data mining ; Point of delivery Machine Learning in Materials Science ; Composite Material Mechanics ; X-ray Diffraction in Crystallography
Authors: Shenghong Ju | Junichiro Shiomi
Source: arXiv (Cornell University) (preprint, 2019-01-24)
Field: Materials Science — Materials Chemistry

Materials Informatics for Heat Transfer: Recent Progresses and Perspectives With the advances in materials and integration of electronics and thermoelectrics, the demand for novel crystalline materials with ultimate high/low thermal conductivity is increasing. However, search for optimal thermal materials is challenge due to the tremendous degrees of freedom in the composition and structure of crystal compounds and nanostructures, and thus empirical search would be exhausting. Materials informatics, which combines the simulation/experiment with machine learning, is now gaining great attention as a tool to accelerate the search of novel thermal materials. In this review, we discuss recent progress in developing materials informatics for heat transport: the exploration of crystals with high/low thermal conductivity via high-throughput screening, and nanostructure design for high/low thermal conductance using the Bayesian optimization and Monte Carlo tree search. The progresses show that the materials informatics method are useful for designing thermal functional materials. We end by addressing the remaining issues and challenges for further development. Materials informatics ; Bayesian optimization ; Thermal conductivity ; Thermoelectric materials ; Informatics ; Nanotechnology ; Electronics ; Materials science ; Computer science ; Health informatics ; Engineering ; Artificial intelligence ; Engineering informatics ; Electrical engineering Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Stephen Whitelam | Isaac Tamblyn
Source: arXiv (Cornell University) (article, 2019-12-18)
Field: Materials Science — Materials Chemistry

Learning to grow: control of material self-assembly using evolutionary reinforcement learning We show that neural networks trained by evolutionary reinforcement learning can enact efficient molecular self-assembly protocols. Presented with molecular simulation trajectories, networks learn to change temperature and chemical potential in order to promote the assembly of desired structures or choose between competing polymorphs. In the first case, networks reproduce in a qualitative sense the results of previously-known protocols, but faster and with higher fidelity; in the second case they identify strategies previously unknown, from which we can extract physical insight. Networks that take as input the elapsed time of the simulation or microscopic information from the system are both effective, the latter more so. The evolutionary scheme we have used is simple to implement and can be applied to a broad range of examples of experimental self-assembly, whether or not one can monitor the experiment as it proceeds. Our results have been achieved with no human input beyond the specification of which order parameter to promote, pointing the way to the design of synthesis protocols by artificial intelligence. Reinforcement learning ; Fidelity ; Computer science ; Artificial neural network ; Range (aeronautics) ; High fidelity ; Simple (philosophy) ; Artificial intelligence ; Control (management) ; Evolutionary robotics ; Evolutionary algorithm ; Distributed computing ; Engineering Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Modular Robots and Swarm Intelligence
Authors: Tristan Hascoet | Xuejiao Deng | Kiyoto Tai | Mari Sugiyama | Yuji Adachi | Sachiko Nakamura | Yasuo Ariki | Tomoko Hayashi | Tetusya Takiguchi
Source: arXiv (Cornell University) (preprint, 2019-10-24)
Field: Materials Science — Materials Chemistry

Assisting human experts in the interpretation of their visual process: A case study on assessing copper surface adhesive potency Deep Neural Networks are often though to lack interpretability due to the distributed nature of their internal representations. In contrast, humans can generally justify, in natural language, for their answer to a visual question with simple common sense reasoning. However, human introspection abilities have their own limits as one often struggles to justify for the recognition process behind our lowest level feature recognition ability: for instance, it is difficult to precisely explain why a given texture seems more characteristic of the surface of a finger nail rather than a plastic bottle. In this paper, we showcase an application in which deep learning models can actually help human experts justify for their own low-level visual recognition process: We study the problem of assessing the adhesive potency of copper sheets from microscopic pictures of their surface. Although highly trained material experts are able to qualitatively assess the surface adhesive potency, they are often unable to precisely justify for their decision process. We present a model that, under careful design considerations, is able to provide visual clues for human experts to understand and justify for their own recognition process. Not only can our model assist human experts in their interpretation of the surface characteristics, we show how this model can be used to test different hypothesis of the copper surface response to different manufacturing processes. Interpretability ; Process (computing) ; Artificial intelligence ; Interpretation (philosophy) ; Computer science ; Introspection ; Plastic bottle ; Cognitive science ; Human–computer interaction ; Cognitive psychology ; Psychology ; Engineering ; Bottle Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Non-Destructive Testing Techniques
Authors: Yasuharu Okamoto
Source: ChemRxiv (preprint, 2019-03-18)
Field: Chemistry — Organic Chemistry

Tradeoffs and Compatibilities of Chemical Properties in CpHqFrOs System To comply with the Kigali amendment to the Montreal Protocol in 2016, it becomes an urgent matter to develop new refrigerants with low global warming potential with simultaneously meeting conventional requirements of cooling performance, safety, and non-destructive to the ozone layer. Because each requirement links to different chemical property, proper control of various chemical properties is necessary to achieve the requirements. However, it seems to be extremely difficult to satisfy all the requirements simultaneously due to the tradeoffs among the properties. For this reason, we need to correctly recognize how these chemical properties behave when the composition of molecule is changed. Here we have done in silico screening that combines quantum chemical calculation, machine learning, and database search, where 10,163 molecules were exhaustively investigated within the properly imposed constraints, then we have found several candidates for new refrigerants. It should be noted that the synthesis of refrigerants is more difficult than that of ordinary organic molecules because glassware cannot be used for the synthesis of fluorine-containing molecules that most refrigerants apply. This makes in silico screening a more useful approach in the design of refrigerants. Refrigerant ; Quantum chemical ; Computer science ; Montreal Protocol ; Global-warming potential ; Process engineering ; Ozone layer ; Biochemical engineering ; In silico ; Molecule ; Environmental science ; Chemistry ; Ozone ; Engineering ; Organic chemistry ; Greenhouse gas ; Mechanical engineering Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Inorganic Fluorides and Related Compounds