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| 1 | Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials显示文摘Recently,high-entropy alloys(HEAs)have attracted wide attention due to their extraordinary materials properties.A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space.Ab initio calculations have emerged as a powerful approach that complements experiment.However,for multicomponent alloys existing approaches suffer from the chemical complexity involved.In this work we propose a method for studying HEAs computationally.Our approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo simulations.The high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW HEA.The approach is employed to study phase stability,phase transitions,and chemical short-range order.The importance of including local relaxation effects is revealed:they significantly stabilize single-phase formation of bcc NbMoTaW down to room temperature.Finally,a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered. | Tatiana Kostiuchenko Fritz Körmann Jörg Neugebauer Alexander Shapeev | 2019 | npj Computational Materials2019，，1： | 17 |
| 2 | Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe显示文摘We present the magnetic Moment Tensor Potentials(mMTPs),a class of machine-learning interatomic potentials,accurately reproducing both vibrational and magnetic degrees of freedom as provided,e.g.,from first-principles calculations.The accuracy is achieved by a two-step minimization scheme that coarse-grains the atomic and the spin space.The performance of the mMTPs is demonstrated for the prototype magnetic system bcc iron,with applications to phonon calculations for different magnetic states,and molecular-dynamics simulations with fluctuating magnetic moments. | Ivan Novikov Blazej Grabowski Fritz Körmann Alexander Shapeev | 2022 | npj Computational Materials2022，，1： | 4 |
| 3 | Ab initio vibrational free energies including anharmonicity for multicomponent alloys显示文摘The unique and unanticipated properties of multiple principal component alloys have reinvigorated the field of alloy design and drawn strong interest across scientific disciplines.The vast compositional parameter space makes these alloys a unique area of exploration by means of computational design.However,as of now a method to compute efficiently,yet with high accuracy the thermodynamic properties of such alloys has been missing.One of the underlying reasons is the lack of accurate and efficient approaches to compute vibrational free energies—including anharmonicity—for these chemically complex multicomponent alloys.In this work,a density-functional-theory based approach to overcome this issue is developed based on a combination of thermodynamic integration and a machine-learning potential.We demonstrate the performance of the approach by computing the anharmonic free energy of the prototypical five-component VNbMoTaW refractory high entropy alloy. | Blazej Grabowski Yuji Ikeda Prashanth Srinivasan Fritz Körmann Christoph Freysoldt Andrew Ian Duff Alexander Shapeev Jörg Neugebauer | 2019 | npj Computational Materials2019，，1： | 1 |
| 4 | Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning显示文摘The elementary excitations in metallic glasses(MGs),i.e.,β processes that involve hopping between nearby sub-basins,underlie many unusual properties of the amorphous alloys.A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions,however,has remained a daunting challenge.Recently,employing well-designed site environment descriptors and machine learning(ML),notable progress has been made in predicting the propensity for stress-activated β processes(i.e.,shear transformations)from the static structure. | Qi Wang Jun Ding Longfei Zhang Evgeny Podryabinkin Alexander Shapeev Evan Ma | 2020 | npj Computational Materials2020，，1： | 0 |
| 5 | Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass显示文摘The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials.With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond,employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit,through the design of an optimal straining pathway.Such simulations,however,call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties.Motivated by this challenge,we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k-space.These calculations enable us to identify ways in which the physical properties can be altered through“deep”elastic strain engineering up to a large fraction of the ideal strain.Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure.By training a surrogate model with ab initio computational data,our method can identify the most efficient strain energy pathway to realize physical property changes.The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass.We illustrate the applications of the method with specific results for diamonds,although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials。 | Evgenii Tsymbalov Zhe Shi Ming Dao Subra Suresh Ju Li Alexander Shapeev | 2021 | npj Computational Materials2021，，1： | 0 |
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