Long-range electrostatics for machine learning interatomic potentials is easier than we thought
D. Kim, B. Cheng, The Journal of Chemical Physics 164 (2026).

Thermal transport of amorphous hafnia across the glass transition
Z. Zeng, X. Liang, Z. Fan, Y. Chen, M. Simoncelli, B. Cheng, ACS Materials Letters (2025) 2695–2701.

Machine learning of charges and long-range interactions from energies and forces
D.S. King, D. Kim, P. Zhong, B. Cheng, Nature Communications 16 (2025).

Lattice distortion leads to glassy thermal transport in crystalline CBi2Cl3
Z. Zeng, Z. Fan, M. Simoncelli, C. Chen, T. Liang, Y. Chen, G. Thornton, B. Cheng, Proceedings of the National Academy of Sciences 122 (2025) e2415664122.

Cartesian equivariant representations for learning and understanding molecular orbitals
D.S. King, D. Grzenda, R. Zhu, N. Hudson, I. Foster, B. Cheng, L. Gagliardi, Proceedings of the National Academy of Sciences 122 (2025).

Scalable multitemperature free energy sampling of classical Ising spin states
P. Tuo, Z. Zeng, J. Chen, B. Cheng, Journal of Chemical Theory and Computation 21 (2025) 11427–11435.

Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance
R. Wild, F. Wodaczek, V. Del Tatto, B. Cheng, A. Laio, Nature Communications 16 (2025).

Latent Ewald summation for machine learning of long-range interactions
B. Cheng, Npj Computational Materials 11 (2025).

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
D. Kim, X. Wang, S. Vargas, P. Zhong, D.S. King, T.J. Inizan, B. Cheng, Journal of Chemical Theory and Computation 21 (2025) 12709–12724.

Machine learning interatomic potential can infer electrical response
P. Zhong, D. Kim, D.S. King, B. Cheng, Npj Computational Materials 11 (2025).

Response matching for generating materials and molecules
B. Cheng, Journal of Chemical Theory and Computation 20 (2024) 9259–9266.

The seventh blind test of crystal structure prediction: Structure generation methods
L.M. Hunnisett, J. Nyman, N. Francia, N.S. Abraham, C.S. Adjiman, S. Aitipamula, T. Alkhidir, M. Almehairbi, A. Anelli, D.M. Anstine, J.E. Anthony, J.E. Arnold, F. Bahrami, M.A. Bellucci, R.M. Bhardwaj, I. Bier, J.A. Bis, A.D. Boese, D.H. Bowskill, J. Bramley, J.G. Brandenburg, D.E. Braun, P.W.V. Butler, J. Cadden, S. Carino, E.J. Chan, C. Chang, B. Cheng, S.M. Clarke, S.J. Coles, R.I. Cooper, R. Couch, R. Cuadrado, T. Darden, G.M. Day, H. Dietrich, Y. Ding, A. DiPasquale, B. Dhokale, B.P. van Eijck, M.R.J. Elsegood, D. Firaha, W. Fu, K. Fukuzawa, J. Glover, H. Goto, C. Greenwell, R. Guo, J. Harter, J. Helfferich, D.W.M. Hofmann, J. Hoja, J. Hone, R. Hong, G. Hutchison, Y. Ikabata, O. Isayev, O. Ishaque, V. Jain, Y. Jin, A. Jing, E.R. Johnson, I. Jones, K.V.J. Jose, E.A. Kabova, A. Keates, P.F. Kelly, D. Khakimov, S. Konstantinopoulos, L.N. Kuleshova, H. Li, X. Lin, A. List, C. Liu, Y.M. Liu, Z. Liu, Z.-P. Liu, J.W. Lubach, N. Marom, A.A. Maryewski, H. Matsui, A. Mattei, R.A. Mayo, J.W. Melkumov, S. Mohamed, Z. Momenzadeh Abardeh, H.S. Muddana, N. Nakayama, K.S. Nayal, M.A. Neumann, R. Nikhar, S. Obata, D. O’Connor, A.R. Oganov, K. Okuwaki, A. Otero-de-la-Roza, C.C. Pantelides, S. Parkin, C.J. Pickard, L. Pilia, T. Pivina, R. Podeszwa, A.J.A. Price, L.S. Price, S.L. Price, M.R. Probert, A. Pulido, G.R. Ramteke, A.U. Rehman, S.M. Reutzel-Edens, J. Rogal, M.J. Ross, A.F. Rumson, G. Sadiq, Z.M. Saeed, A. Salimi, M. Salvalaglio, L. Sanders de Almada, K. Sasikumar, S. Sekharan, C. Shang, K. Shankland, K. Shinohara, B. Shi, X. Shi, A.G. Skillman, H. Song, N. Strasser, J. van de Streek, I.J. Sugden, G. Sun, K. Szalewicz, B.I. Tan, L. Tan, F. Tarczynski, C.R. Taylor, A. Tkatchenko, R. Tom, M.E. Tuckerman, Y. Utsumi, L. Vogt-Maranto, J. Weatherston, L.J. Wilkinson, R.D. Willacy, L. Wojtas, G.R. Woollam, Z. Yang, E. Yonemochi, X. Yue, Q. Zeng, Y. Zhang, T. Zhou, Y. Zhou, R. Zubatyuk, J.C. Cole, Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials 80 (2024) 517–547.

Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures
X. Wang, B. Cheng, Journal of Chemical Physics 161 (2024).

Cartesian atomic cluster expansion for machine learning interatomic potentials
B. Cheng, Npj Computational Materials 10 (2024).

Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations
Z. Zeng, F. Wodaczek, K. Liu, F. Stein, J. Hutter, J. Chen, B. Cheng, Nature Communications 14 (2023).

A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals
A. Reinhardt, P.Y. Chew, B. Cheng, Journal of Chemical Physics 159 (2023).

BingqingCheng/solubility: .0
B. Cheng, (2023).

Thermodynamics of diamond formation from hydrocarbon mixtures in planets
B. Cheng, S. Hamel, M. Bethkenhagen, Nature Communications 14 (2023).

Physics-inspired machine learning of localized intensive properties
K. Chen, C. Kunkel, B. Cheng, K. Reuter, J.T. Margraf, Chemical Science (2023).

Computing chemical potentials of adsorbed or confined fluids
R. Schmid, B. Cheng, The Journal of Chemical Physics 158 (2023).