There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

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Recent advances and applications of machine learning in solid-state materials science

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 10^{4} data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

/pdf/crystal-graph-convolutional-neural-networks-for-an-accurate-dqwv22b73r.pdf

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.

Once referred to as ‘materials of 1,000 uses’, plastics meet demands in everything from clothing and automotive sectors to the manufacturing of medical equipment and electronics. Concomitant with usage, worldwide generation of plastic solid waste increases daily and is currently around 150 million tonnes per annum. Although recycled materials may have physical properties similar to those of virgin plastics, the resulting monetary savings are limited and the properties of most plastics are significantly compromised after a number of processing cycles. An alternative approach to processing plastic solid waste is chemical recycling, the success of which relies on the affordability of processes and the efficiency of catalysts. In this Review, we describe technologies available for sorting and recycling plastic solid waste into feedstocks, as well as state-of-the-art techniques to chemically recycle commercial plastics. These evaluations are followed by a survey of recent advances in the design of new high-performing recyclable polymers. Many methods exist for the recycling of plastic solid waste. Chemical recycling, which can take many forms from high-temperature pyrolysis to mild, solution-based catalytic depolymerization, can afford enormous economic and environmental benefits. This Review covers the state of the art in chemical recycling and the design of high-performance polymers amenable to such processes.

Chemical recycling of waste plastics for new materials production

The representations of a compound, called ``descriptors'' or ``features'', play an essential role in constructing a machine-learning model of its physical properties. In this study, we adopt a procedure for generating a set of descriptors from simple elemental and structural representations. First, it is applied to a large data set composed of the cohesive energy for about 18 000 compounds computed by density functional theory calculation. As a result, we obtain a kernel ridge prediction model with a prediction error of 0.041 eV/atom, which is close to the ``chemical accuracy'' of 1 kcal/mol (0.043 eV/atom). A prediction model with an error of 0.071 eV/atom of the cohesive energy is obtained for the normalized prototype structures, which can be used for the practical purpose of searching for as-yet-unknown structures. The procedure is also applied to two smaller data sets, i.e., a data set of the lattice thermal conductivity for 110 compounds computed by density functional theory calculation and a data set of the experimental melting temperature for 248 compounds. We examine the effect of the descriptor sets on the efficiency of Bayesian optimization in addition to the accuracy of the kernel ridge regression models. They exhibit good predictive performances.

Representation of compounds for machine-learning prediction of physical properties

// Yoshio Nakamura 1, 3 , Shigehisa Kitano 2, * , Akira Takahashi 1 , Arata Tsutsumida 1 , Kenjiro Namikawa 1 , Keiji Tanese 3 , Takayuki Abe 4 , Takeru Funakoshi 3 , Noboru Yamamoto 2 , Masayuki Amagai 3 , Naoya Yamazaki 1, * 1 Department of Dermatologic Oncology, National Cancer Center Hospital, Tokyo, Japan 2 Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, Japan 3 Department of Dermatology, Keio University School of Medicine, Tokyo, Japan 4 Department of Preventive Medicine and Public Health, Biostatistics Unit at Clinical Translational Research Center, Keio University School of Medicine, Tokyo, Japan * These authors have contributed equally to this work Correspondence to: Shigehisa Kitano, email: skitano@ncc.go.jp Keywords: nivolumab, metastatic melanoma, absolute lymphocyte count, absolute neutrophil count, early markers for outcome Received: June 07, 2016     Accepted: September 28, 2016     Published: October 15, 2016 ABSTRACT Background: An anti-programmed cell death protein 1 monoclonal antibody, nivolumab, is one of the most effective drugs for advanced melanoma. Tumor cell-derived or immune cell-derived markers and clinical predictors such as serum lactate dehydrogenase (LDH) and cutaneous adverse events, have already been described as prognostic factors for advanced melanoma treated with nivolumab. We sought to identify further clinical predictors that can be determined in routine clinical practice. Methods: We retrospectively analyzed clinical findings of 98 consecutive patients with unresectable stage III or IV melanoma treated with nivolumab, at the National Cancer Center Hospital or at Keio University Hospital, in Tokyo, Japan, between July 2014 and July 2016. These patients had been administered nivolumab at a dose of 2mg/kg every 3 weeks. Results: As for pretreatment prognostic factors, ECOG performance status (PS) ≥1, maximum tumor diameters of ≥30mm, elevated LDH and elevated C-reactive protein were significantly associated with poor overall survival (OS) (hazard ratio [HR] 0.29 [ P <0.001], HR 0.40 [ p =0.003], HR 0.29 [ P <0.001], HR 0.42 [ P =0.004], respectively) on univariate analysis. Among these factors, PS and LDH were identified as independent variables by multivariate analysis. As for early markers examined during therapy, patients with absolute lymphocyte count (ALC) ≥ 1000/μl (Week3: HR 0.40 [ P =0.004], Week6: HR 0.33 [ P =0.001]) and absolute neutrophil count (ANC) <4000/μl (Week3: HR 0.46 [ P =0.014], Week6: HR 0.51 [ P =0.046]) had significantly better OS. Conclusion: ALC≥1000/μl and ANC<4000/μl during treatment appear to be early markers associated with OS. Nivolumab might have minimal efficacy in patients with a massive tumor burden.

/pdf/nivolumab-for-advanced-melanoma-pretreatment-prognostic-31lolab5ji.pdf

Nivolumab for advanced melanoma: pretreatment prognostic factors and early outcome markers during therapy.

Degradable epoxy resins prepared from diepoxide monomer with dynamic covalent disulfide linkage

We propose a simple scheme to estimate the potential energy surface (PES) for which the accuracy can be easily controlled and improved. It is based on model selection within the framework of linear regression using the least absolute shrinkage and selection operator (LASSO) technique. Basis functions are selected from a systematic large set of candidate functions. The sparsity of the PES significantly reduces the computational cost of evaluating the energy and force in molecular dynamics simulations without losing accuracy. The usefulness of the scheme for describing the elemental metals Na and Mg is clearly demonstrated.

Sparse representation for a potential energy surface

Interatomic potentials have been widely used in atomistic simulations such as molecular dynamics. Recently, frameworks to construct accurate interatomic potentials that combine a set of density functional theory (DFT) calculations with machine learning techniques have been proposed. One of these methods is to use compressed sensing to derive a sparse representation for the interatomic potential. This facilitates the control of the accuracy of interatomic potentials. In this study, we demonstrate the applicability of compressed sensing to deriving the interatomic potential of ten elemental metals, namely, Ag, Al, Au, Ca, Cu, Ga, In, K, Li, and Zn. For each elemental metal, the interatomic potential is obtained from DFT calculations using elastic net regression. The interatomic potentials are found to have prediction errors of less than 3.5 meV/atom, 0.03 eV/\AA{}, and 0.15 GPa for the energy, force, and the stress tensor, respectively, which enable the accurate prediction of physical properties such as lattice constants and the phonon dispersion relationship.

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Akira Takahashi

Papers

Representation of compounds for machine-learning prediction of physical properties

Nivolumab for advanced melanoma: pretreatment prognostic factors and early outcome markers during therapy.

Degradable epoxy resins prepared from diepoxide monomer with dynamic covalent disulfide linkage

Sparse representation for a potential energy surface

First-principles interatomic potentials for ten elemental metals via compressed sensing